INDUSTRIAL PRODUCT REVIEW | January-March...

INDUSTRIAL PRODUCT REVIEW | January-March 2019 | 3

4 | INDUSTRIAL PRODUCT REVIEW | January-March 2019

VOLUME 2, ISSUE 10 | JanUary-March 2019

NEW-AGE INDUSTRIAL QUARTERLY

Some products, solutions or services mentioned in this issue may not be available in India

ISSN: 2456-8376 (Print)

‘Industrial Product Review’ quarterly is owned by MediaNext Infoprocessors Pvt. Ltd., printed and published by Amit Tekale, printed at Akruti, 31B, Parvati Industrial Estate, Pune - Satara Road, Pune – 411009 and published at 2117, Sadashiv Peth, Vijayanagar Colony, Pune – 411030 Editor – Amit TekaleAll rights reserved. Printed in India.

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The contents of the publication may not be reproduced or transmitted in any form, either in part or in full, including photocopying and recording, without the written consent of the copyright owner. Nor may any part of this publication be stored in a retrieval system of any nature without prior written consent. Industrial Product Review (IPR) does not claim any copyright whatsoever on the articles reproduced under Creative Commons Attribution License. The copyright of all such contributions remain with respective author/s.Industrial Product Review (IPR) receives unsolicited materials (including letters to the editor, press releases, promotional items and images) from time-to-time. Industrial Product Review, its affiliates and assignees may reuse, reproduce, publish, republish, distribute, store and archive such unsolicited submissions in whole or in part in any form or medium whatsoever without compensation of any sort. Industrial Product Review (IPR) accepts no responsibility or liability for claims made for any product or service reported or advertised in this issue.

EDITORIAL l ABHAY KULKARNI

Managing Editor l AMIT TEKALE

Editor and Publisher l NIRANJAN MEDHEKAR

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DESIGN l KIRAN VELHANKAR

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We acknowledge support of:


Editor’s Note

MediaNext Infoprocessors Pvt. Ltd. – the publisher of Industrial Product Review (IPR) added two B2B magazines to its portfolio in December 2018. The largest

circulating woodworking magazine in India – Modern Woodwork India and the only B2B magazine devoted to woodworking technology published from Gulf – Modern Woodwork Gulf – are now a part of MediaNext. With this addition, MediaNext decided to change the frequency of IPR from monthly to quarterly that matches the consumer habits and offers a unique value proposition.

What differentiates IPR in the marketplace is print. The manufacturing industry is using print as a differentiator and as a tool to reach the audience/customer in a different way as content and commerce gel very well together.

IPR will now be published every January, April, July and October. The circulation will be further expanded to an optimum figure and an interactive digital edition will be pushed to a large database every quarter. We hope to receive continued patronage from our contributors, readers and advertisers.

Please visit www.modernwoodworkindia.com to know more about the newly acquired magazines.

Amit tekAle Editor

[email protected]

IPR Goes Quarterly

Reportagel C4i4 Lab Marks First Foundation Day .......... 6

Connecting Ideas l MFCA in Indian Industry:

The Research Project by INEC, Germany .... 10

Show Previewl VDW Offers a Comprehensive Service

Package for EMO Hannover 2019 .............. 14

l FILTECH 2019: Targeted Solutions for All Industries ............................................ 16

l HANNOVER MESSE 2019: The Road to Digitization ........................... 17

Product Review l Schunk TANDEM plus 140: Clamping

Force Blocks for Robot-assisted Pallet Loading ..............................................18

l Stromag High Performance Brake: Designed for the Toughest Conditions ...19

Product Previewl COVAL Announces Lightweight

Vacuum Gripper CVGL .............................. 20

l Aerotech Launches Linear Amplifier XL4s ............................... 20

l ECD Introduces Liquid Analyzer System X80-B88-S88 ............................................ 21

Intelligent Manufacturingl Integrated and Intelligent Manufacturing:

Perspectives and Enablers ........................ 22

Lean Manufacturingl Understanding of Business Performance from

the Perspective of Manufacturing Strategies: Fit Manufacturing and Overall Equipment Effectiveness ............................................ 32

Research Articlel Smart Hybrid Manufacturing Control Using Cloud

Computing and the Internet-of-Things ..... 37

News..................................................... 55

Beyond Factory Wallsl Five of the World's Most Incredible

Road Trips .............................................. 56

Subscription Form .............. 58


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STRATEGY FORMULATION SESSIONS

BUSINESS HEALTH ASSESSMENT LEADERSHIP INTERVENTIONS SALES ACCELERATION WORKSHOPS OPERATIONAL IMPROVEMENT INITIATIVES

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Projects& processindustry11%

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20% Manufacturing

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Get in touch to accelerate your business growth


Reportage

The C4i4 Lab is a registered Section 8 company headquartered at Pune, and was set up through an initiative

of the Government of India’s Department of Heavy Industries to promote and spread awareness regarding Industry 4.0 among the manufacturing industry. The Lab celebrated its first Foundation Day on December 13, 2018 with the inauguration of its state-of-the-art demonstration lab. The aim of those behind it is to conduct 50 pilots for different manufacturing plants in the region over the next year.

Explaining the nature of the newly-launched demonstration lab with Industrial Product Review (IPR), Vikram Salunke, Steering Committee member-C4i4 Lab and MD-Accurate India said, “The demonstration lab is a unique concept within which we have tried to be different than others. What you see here (at the C4i4

campus) is only part of the infrastructure. A major part of the infrastructure will be situated where we are going to actually conduct the pilots. Through this demo lab we will pull the data of the pilots here. So, when a manufacturer comes here, he can get an overview and he will also see the

real data and how things are working at a shop floor level, which could be similar to his own plant. This is the unique feature of this project. Over the next year, we at C4i4, aim to handhold 50 companies for conducting the pilots.”

Taking an overview of the journey of the C4i4 Lab so far, Dattatraya Navalgundkar, Director of the C4i4 Lab said, “It all started on December 22, 2016 when some like-minded entrepreneurs and industrialists came together to discuss what the roadmap of Industry 4.0 in India will be like. This get-together was followed by a visit to Hannover Messe where they discovered what Industry 4.0 is all about, and came back with the aim of setting up a dedicated centre to spread the word about Industry 4.0. That’s how the seed of the C4i4 Lab was germinated and nurtured, and the organisation had

C4i4 Lab Marks First Foundation Day the Pune-based Centre for industry 4.0 (C4i4 lab) celebrated its first Foundation Day in December with the inauguration of a state-of-the-art demonstration lab. the plan for 2019 is to conduct 50 pilots for various manufacturing plants in the region. iPR talks to the industry stalwarts behind the endeavour to find out more...

the industry is responding very favourably to the various initiatives taken by C4i4 over the year. that is a big testament for C4i4.


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taken shape over the next year. Now, if we consider the last one year of the C4i4 Lab, lot of activities have been accomplished in the area of spreading awareness among industries. In the next year, we aim to upgrade into the role of a consultant and help small and medium scale (SME) industries to adopt the numerous advanced practices of Industry 4.0.”

About the thought behind setting up the C4i4 Lab, Rahul Kirloskar, Director-Kirloskar Group, Executive Chairman-Kirloskar Pneumatic Company Limited and also Steering Committee member of the C4i4 Lab said, “We had taken this initiative as we found Industry 4.0 is an ocean and we simply didn’t know where we should start and all that it would entail. When we look back, we are quite happy with where we are today. A lot of companies are interested and their top management understand the gravity of the subject. However, immersion at the down level

has not happened yet. Our plan for the next year is to continue with our training programmes and make sure more and more companies are associated with the C4i4 Lab.”

Vinay Nathan, CEO and Co-founder of Altizon Systems Pvt. Ltd. and also a Steering Committee member of the C4i4 Lab said, “The methodical way in which C4i4 has approached the process, in terms of creating an assessment framework, reaching out to industry using that framework, and taking that to the next level, I think all this has been a really good development. The industry is responding very favourably to the various initiatives taken by C4i4 over the year. That is a big testament for C4i4.”

The inauguration of the demonstration lab was followed by different plenary sessions and panel discussions in which several senior industry executives and government officials participated

including Raju Ketkale, Senior Vice-President and Director, Toyota Kirloskar Motors; Rene Van Berkel, UNIDO India Representative; Yogesh Kulkarni, COO and Co-Founder of Altizon Systems; Ravi Damodaran, CTO, Greaves Cotton Ltd.; Dr. R. Venkateswaran, Senior Vice-President of Persistent Systems; Uma Nidmarty, Kalzoom Advisors; Shripad Ranade, Ikigai Discoveries; and Rahul Mishra of A. T. Kearney.

Along with the C4i4 Lab Pune, the Government of India has set up three such centres at IIT Delhi, IISc Bangalore and CMTI Bangalore. This is a national initiative named SAMARTH Udyog and brings together Industry, Institutes, Industry Associations and the Government to invest in the programme to enhance our global competitiveness.

(From Left) Raju Ketkale, Senior Vice-President and Director, Toyota Kirloskar Motors; Rene Van Berkel, UNIDO India Representative; Dattatraya Navalgundkar, Director, C4i4 Lab while inaugurating the demonstration lab



Connecting Ideas

Ms. Nadja Wisniewski and Ms. Julia Schindler (extreme left) with Managing Director Mr. Ravi Nangia (4th from left) of Harmony Organics Pvt. Ltd. and his team at Kurkumbh plant. Project coordinator Prashant Gongle (extreme right)

MFCA in Indian Industry: The Research Project by INEC, Germany

Prashant GongleLocal Project Coordinator,EnPro Consultant, Pune, India

Ms. Nadja Wisniewski Research Associate at INEC Pforzheim University, Germany

Ms. Julia SchindlerResearch Associate at INEC Pforzheim University, Germany


Harmony Organics Pvt. ltd.One MFCA case study was carried out at Harmony Organics

Pvt. Ltd. in Kurkumbh in the region of Pune, Maharashtra. The chemical company is specialized in aroma chemicals for customers of various industrial sectors. For the study, the production line of a rose aroma was analyzed.

The first major challenge in this project was the balancing of the material balances. This was especially difficult because in the chemical industry, not all inputs react to the desired output but instead several byproducts can occur. Moreover, minor changes in the process parameters can have an influence on the reaction rate and on the time needed for the reaction to take place. On top of that, many recycling loops and special recycling processes made the analysis more complicated.

For the MFCA analysis, different cost elements such as material costs, energy costs, system costs (machine costs, labor costs, transportation costs, storage costs…) and waste management costs are identified for product and waste. Material

costs were by far the biggest cost driver. Besides, there were different types of waste generated that had to be disposed of. The disposal costs for these wastes, the handling costs and waste recovery costs add up to the waste management costs. For Harmony Organics, the waste management costs of the waste were approximately 10% of the total costs of the waste.

In total, the share of waste costs compared to the total costs of the production line is about 40%. This number is very high because huge amounts of byproducts are created in and are necessary for the reaction to take place. Not all byproducts become waste. Some of these byproducts are also sold on the market. Still, the amount of these byproducts is considered as a material loss in the MFCA analysis, because they cannot be reused in this production process anymore. The remaining byproducts, which cannot be sold must be treated and must be disposed of. All byproducts together add up to a significant amount of total waste which leads to a major share of waste costs.

Umberto model (physical flows) for Harmony Organics Pvt. ltd.:

in the previous issue of iPR we presented article on findings of material Flow Cost Accounting (mFCA) study conducted in two companies out of four. Now, in this 4th and last article of this series we are going to present findings of study in remaining two companies.


Automotive manufacturing CompanyThe last of the four case studies was conducted at a leading

automotive manufacturing company in Pune, Maharashtra. There, the roof ditch sealant application process in the paint shop was selected for the MFCA analysis. This process step is done manually as well as by robots.

A major challenge for the analysis was the lack of data due to which measurements and estimations had to be taken on the shop floor. During the analysis it was noted that the material balance for the process step is not balanced, i.e. there was significantly more input material than output (sealant applied on the cars and waste sealant) generated. The rule of mass conservation states that the inputs and outputs always have to be equal and that no material can get lost in the process. Therefore, the identification of such a deviation in the mass balance is an indicator for a lack of process transparency and process control.

Most of the roof ditch sealant removed in the rectification process is recycled and reinserted into another sealing process step in the paint shop. However, there it replaces a cheaper sealant material. This downgrading of the expensive roof ditch sealant is also known as downcycling. Generally speaking, the reuse of material is always better than no recycling at all.

The study revealed a proportionately large share of system

costs. Still, the material costs were the main cost driver. The identified share of the waste costs was approximately 17%.

Concluding, about the MFCA research study conducted in Y2018 in Indian industry, we find that the method is equally useful as experienced in Germany and Japan, to find out opportunities to improve business performance. The four projects were successfully completed resulting in assisting top management to get more transparency in their selected processes. Moreover, the MFCA method turned out to be an adequate instrument to identify a lack of process control and process stability.

At the end of this series of MFCA articles, we would like to thank all participants for their excellent cooperation during the project. In the near future, new MFCA projects are intended in India.

(This article has been published by INEC research associates Ms. Nadja Wisniewski and Ms. Julia Schindler in association with local

project coordinator Mr. Prashant Gongle of EnPro Consultant and Let’s Bridge IT)

Umberto model (physical flows) for a leading automotive manufacturer:

Prashant GongleL-15 Sawant Vihar Phase III,Katraj, Pune 411046. Mob.: 9850992270Email: [email protected]

Let's Bridge ITShri. Ashish Pandit, Partner9th Floor, Sunit Capital, Senapati Bapat Road, Pune - 411 016.Mob.: 91580 06601 | Email: [email protected]: www.lets-bridge-it.com


Productivity meets Sustainability

• Use Material Flow Cost Accounting (MFCA) to discover material efficiency potentials

• Eliminate wastage from your production • Save 10% of material and energy costs • Calculate and improve your company‘s CO2 balance• Take a visual approach to get efficiency projects approved

More information at: go.ifu.com/mfca_india

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Show Preview

VDW Offers a Comprehensive Service Package for EMO Hannover 2019

EMO Hannover 2019, the world's leading trade fair for metalworking, will take place from 16 to 21

September. It is organized by VDW (Verein Deutscher Werkzeugmaschinenfabriken), Frankfurt am Main, Germany, on behalf of the European Machine Tool Association Cecimo from Brussels, Belgium, and in cooperation with Deutsche Messe AG, Hanover, Germany. The VDW is the spokesman for the German machine tool industry and one of the few industry associations to organise international trade fairs on its own behalf for the industry it represents. It has many years of experience in this field. The seal of quality ‘A VDW trade fair’ has developed into a trademark

for successful events. In addition to EMO Hannover, this also includes the METAV in Düsseldorf, the international trade fair for technologies in metalworking (next date: 10 to 13 March 2020).

The VDW, together with the German Machine Tool and Manufacturing Systems Association (Fachverband Werkzeugmaschinen und Fertigungssysteme) within VDMA (Verband Deutscher Maschinen und Anlagenbau), has round about 300 members. They represent around 90 per cent of total industry turnover in Germany. The VDW represents the interests of its members both nationally and internationally.

As the organiser of EMO Hannover 2019, the VDW offers visitors and exhibitors a comprehensive range of services in cooperation with Deutsche Messe AG: online registration for visitors and exhibitors, contacts via the foreign representatives of Deutsche Messe AG in the national language, visa support, attractive offers for air and rail travel to EMO Hannover, online information in nine different languages, individual support for foreign delegations, thematic tours and much more. Detailed information can be found on the Internet at www.emo-hannover.de.

Team EMO at the press conference held in Pune. (From left) Mr. Michael Rose, Head of Protocol and Events at Deutsche Messe AG, Hannover, Dr. Wilfried Schäfer, Executive Director, VDW (German Machine Tool Builders’ Association), Frankfurt am Main and Ms. Geeta Bisht, Director, Hannover Milano Fairs India Pvt. Ltd, Mumbai.


Exhibitor groups at EMO Hannover 2019 l Machine tools, cutting, separating and ablatingl Sheet-metal, wire and pipe processing machines,

forming machinesl Machine tools for thermal, electrochemical and other

machining optionsl Surface technology, thin-layer processesl Software for the entire spectrum of production

technologyl Control systemsl Components for flexible automationl CAD/CAMl Quality management systemsl Robotics and automationl Material flow and warehousing technologyl Industrial electronics, sensor and diagnostic

technologiesl High-precision tools, diamond tools, metrological

equipmentl Forming toolsl Machines and systems for tool and die construction,

rapid prototyping, model constructionl Testing and metrological technologyl Cooling lubricantsl Welding, cutting, hardening, heatingl Mechanical, hydraulic, electrical and electronic

accessories for production technology

Dr. Wilfried Schäfer, Executive Director, VDW (German Machine Tool Builders’ Association), Frankfurt am Main addressing the press conference.

ANANT RUBBER PRODUCTS

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Show Preview

Effective filtration and separation processes are of eminent importance in all

industries. At the FILTECH 2019 Show manufacturers can catch up on the latest filtration innovations and equipment as well as related cutting-edge products.

The chemical industry, as well as industries such as textile, food and beverage, minerals processing, pulp and paper, waste management, water treatment, environmental engineering and petrochemicals need cost-effective processing structures to reduce costs as well as risks. Also, companies worldwide do face higher environmental standards gradually implemented by their governments. Sophisticated and state of the art filtration and separation solutions play a key role in all these industries. They are necessary to cope with legal norms and by reducing costs they lead to higher competitiveness in a global economy.

The leading filtration show will feature more than 400 exhibitors and will be accompanied by a 3-day conference that attracts close to 500 experts around

the globe. At FILTECH 2019 trade visitors will

meet all types of manufacturers right from raw material providers in fibres/resins, adhesives, various filter media for applications in all sectors. They will learn about current development trends in the nonwovens and composites sector offering effective separation capacity, e.g. technical fibres with integrated functional additives in nonwoven filter

constructions, simulation programs to adopt fibre fineness, pore structure, fibre coating etc. ecologically effective disposal solutions for filters and filtration residues.

Innovative developments are in fine dust particulate filter representing an entirely new use for nonwoven filter media, processing technologies based around acoustic wave separation, dynamic cross-flow filter for purifying nutraceuticals, electro-static-precipitators capable of handling dust emissions limits of 5-10 mg/nm3, a unique filtration media with ability to combine EPA efficiencies, 2-component foam gaskets, drip tight filter press fabrics for EU and FDA compliant filters, air filter media from glass fabrics, foldable media for the separation of coarse to fine dusts, nonwovens made from temp resistant fibres for hot gas filtration etc.

At FILTECH 2018 visitors came from 76 nations. The Show offers decision makers excellent opportunities to enhance their filtration and separation processes and thus to augment their competitiveness.

FiLteCh 2019:Targeted Solutions for All Industries

l IPR is the International Media Partner for FILTECHl IPR Readers are entitled to a free 3-Day-Invited Guest-Ticket. l The code can be swapped here:

https://filtech.de/exhibition/visitor-registration

l Promotion Code: IPRatFILTECH

Media Partner


Show Preview

hAnnOVeR Messe 2019: The Road to DigitizationTo stay competitive, manufacturing

enterprises need to integrate the data at play in all of their value-

creation processes. From 1 to 5 April, HANNOVER MESSE will be showcasing IT and software solutions that support exactly this kind of data connectivity between development, production and subcontractors. This includes Enterprise Resource Planning systems (ERP), which provide for the flexible, real-time monitoring of production processes.

Whether in mechanical and plant engineering, automotive, aviation, construction, metalworking, plastics, process engineering or electrical engineering and electronics – each of these industries must respond adequately to the challenges of digital transformation. Many of them have already embarked on the journey and others are preparing to follow. “The major challenge here is not to view IT as an isolated solution, but to connect all data and systems together in such a way as to provide the greatest possible benefit for business success,” says Hubertus von Monschaw, Global Director for Digital Factory in the HANNOVER MESSE team. “This year, companies from around the world will be coming to Hannover to demonstrate how artificial intelligence can significantly enhance the capabilities of existing software solutions. HANNOVER MESSE is thus strengthening its profile as the largest B2B platform in the Digital Factory segment,” he concludes.

Digitization offers new ways of bringing integrated products to market faster, reducing costs, automating complex processes and introducing new business models such as product-as-a-service. “By performing real-time

monitoring of external voices (Voice of Factory, Voice of the Asset, Voice of the Customer) via IoT or social media, factory operators can get a comprehensive and customer-centric view and apply AI and machine learning to their decision-making processes,” explains Ulf Köster, Solution Director of Digital Transformation Solutions at Oracle. Köster argues that the precondition for successful digital transformation consists of maintaining a continuous connection – integrated, intelligent and optimized – between processes and data running like a leitmotif along the entire supply chain – from product development and planning to production, commercialization and after-sales service.

SAP is also dedicated to ensuring a continuous, integrated flow of data and parameters across all business areas. “This flow must be guaranteed such that the user at all times knows exactly what the current production status is and can then

instantly respond to any fluctuations and ensure a more predictable and sustainable process overall,” says Hala Zeine, President of the Digital Supply Chain at SAP SE. “Speed, quality, individuality and the integration of new technologies like 3-D printing, blockchain or machine learning are the main requirements facing manufacturing enterprises today.”

Abas Software is also a provider of digitization strategies and activities along the supply chain, with ERP playing an important role. “We regard the ERP system as the foundation for digital transformation, because it goes beyond mapping just the critical business processes,” says Mark Muschelknautz, Chief Marketing Officer of abas Software AG. “Within the framework of IoT, Industry 4.0 and connected production, ERP is also having to master new challenges, such as the flexible, real-time monitoring of processes and machines, the aggregation of data in interaction with new analysis tools and process control to satisfy compliance requirements,” he adds.

The companies in halls 5, 6, 7 and 8 at HANNOVER MESSE will be answering the gamut of software questions along the industrial supply chain, thus developing the prerequisites for the object of the world’s leading trade fair for industrial technology in 2019: Integrated Industry - Industrial Intelligence, i.e. the digital connectivity of people and machines in the age of artificial intelligence.

For more information, please visit https://www.hannovermesse.de/

2019, 01 - 05 April


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Schunk TANDEM plus 140: Clamping Force Blocks for Robot-assisted Pallet Loading

High clamping force, compact design, low weight – Schunk TANDEM plus 140 clamping force

blocks are particularly designed for the robot-assisted pallet loading of machine tools. The modules ensure process-reliable workpiece clamping in confined spaces and open up a lot of space for handling the workpiece weight.

With a compact square base area of 140 mm x 140 mm, Schunk TANDEM plus 140 are particularly suitable for 6-sided machining in two set-ups on all common machine tools. The optimized external contour, a special design of the base jaws and cover strip, minimum clearance and cover plugs for fastening screws prevent dirt from accumulating or chips and dust from penetrating into the modules. Any penetrating coolant is lead to the outside via a coolant drain hole. Here, a sinter filter prevents the chips from penetrating into the base body. Control and lubrication of every version are done laterally, as well as on the bottom side. An alignment edge facilitates positioning on the pallet or the machine table. The one-piece rigid base body, wedge-hook kinematics, and long, hardened jaw guidance provides concentrated clamping forces in confined spaces, and ensure an excellent repeat accuracy of 0.015 mm. The clamping force blocks are also suitable for milling with a high metal removal rate, high number of cycles, and minimum tolerances.

Complete modular systemThe Schunk TANDEM plus 140 is optionally

available with pneumatic, hydraulic, or spring actuation: A centric clamping force block with a stroke of 3 mm per jaw; a long-stroke centric clamping force

block with 7 mm stroke per jaw; and a module with fixed chuck jaw with a 6 mm stroke for automated zero-point loading. Depending on the model, the clamping force amounts between 15,000 N and 30,000 N. The modular system thus covers the entire range of possible applications. As the long-stroke and the standard vises have the same connection dimensions, the different variants can be easily exchanged, as required. The TANDEM plus 140 seamlessly fits into the Schunk TANDEM plus clamping force block range with more than 50 standard versions. The portfolio covers sizes from 64 to 250 mm. All modules are suitable for top jaws with tongue and groove, as well as for chuck jaws with fine serration. A wide range of supporting jaws, top jaws and top jaw blanks for application-specific reworking ensures that the clamping blocks can be quickly and easily adapted to new clamping tasks.

The compact Schunk TANDEM plus 140 clamping force blocks are particularly designed for automated machine loading by robots.

PHOTO: SCHUNK

Schunk Intec (India) Pvt. Ltd.80 B, Yeswanthpur Industrial Suburbs, Bengaluru - 560022Tel.: 080 - 4053 8999Email: [email protected] / [email protected]/ [email protected]: www.in.schunk.com


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Stromag High Performance Brake: esigned for the Toughest Conditions

Marie Kerdoncuff Stromag, Hansastraße 120, 59425 Unna, Germany Tel: +49 2303 102-0 / +33 (0)6 83 99 84 08 | Web: www.stromag.com | Email: [email protected] / [email protected]

Stromag, part of Altra Industrial Motion Corp, has launched a new, modular, IP67 rated range of brakes designed for applications in challenging

environments. The High-Performance Brake (HPB) is an electromagnetically operated design that activates automatically in the event of a power failure to hold or stop loads. The new range is designed for working, holding and sudden stop applications, and is intended to deliver superior performance over a long working life.

The HPB is available in two-face or four-face configurations offering a brake torque range of 80 to 5,000 Nm. Stromag has combined the latest design and manufacturing technologies with its 86 years of experience in mechanical systems to create a design that offers maximum performance in a small space envelope. The company has paid particular attention to the thermal characteristics of the brake, achieving superior heat dissipation for reliable dynamic performance.

The internal components of the HPB are all designed to resist corrosion and offer enhanced durability where exposure to salt water or other contaminants is likely. The new brake range is available in IP67 rated configurations, allowing it to operate where flooding or immersion in seawater is a possibility.

Considerable attention has also been paid to the electrical design of the brake, with rectifiers and quick

About StromagFounded in 1932, Stromag has grown to become

a globally recognized leader in the development and manufacture of innovative power transmission components for industrial drivetrain applications. Stromag engineers utilize the latest design technologies and materials to provide creative, energy-efficient solutions that meet their customer’s most challenging requirements. Stromag’s extensive product range includes flexible couplings, disc brakes, limit switches, an array of hydraulically, pneumatically, and electrically actuated brakes and a complete line of electric, hydraulic and pneumatic clutches.

switching units that optimise the switching characteristics and offer easiest installation. To aid integration in customer applications, it is available with a wide range of options, including tachometer mounting, an anti-condensation heater and microswitches to monitor switching operations and wear.

Applications for the HPB brake range include cranes, winches and hoists in harbour, marine or offshore applications. The new brake is also suitable for use in industrial environments where long-term reliability and resistance to harsh environments is a priority.

The High-Performance Brake (HPB) is available in two-face or four-face configurations offering a brake torque range of 80 to 5,000 Nm.

Designed for working, holding and sudden stop applications such as cranes the HPB is intended to deliver superior performance.

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iew COVAL Announces

Lightweight Vacuum Gripper CVGL

Aerotech Launches Linear Amplifier XL4s

COVAL SaSZA Les Petits Champs, Allée Venturi, 26120 Montélier, FranceTel: +33 4 75 59 91 91 | Web: www.coval-international.com

Aerotech IndiaS. K. Arcade, First Floor, Plot No. 2/357-1, 3rd D Cross,OMBR Layout, Chikka Banaswadi, Bengaluru – 560043.Tel: 080 – 4209 1836 | Mob: 90083 07575Email: [email protected] | Web: www.aerotech.com

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COVAL announced its CVGL lightweight vacuum gripper that facilitates integration into robots

with lower payloads.This lightweight vacuum gripper comes in

three standard lengths: 424, 624 and 824 mm and can also be custom made to suit your application.

It supports three gripping interface technologies (foam, suction cups and COVAL-flex), 3 suction flow rates, and an integrated or external generator.

These grippers easily handle parts of various sizes, weights and materials, including porous objects.

Advantages:The CVGL series is composed of standard subassemblies which allow us to offer a ‘tailor-made’ solution, meeting the specific application requirements of integrators and end users:l Compactl Lightweightl Integrated functionsl Modularityl Performancel Ease of usel Universal mounting

Aerotech announced its XL4s Linear

amplifier, designed for closed-loop servo control of voice coil and single-phase motors. This linear amplifier eliminates the non-linearities common with PWM amplifiers and provides deterministic behaviour, auto-identification, and features a multi-core, double-precision, floating-point DSP that controls the digital PID.

All parameters can be set using the accompanying software, including control-loop gains and system safety functions. The XL4sis capable of long-term thermal stability, and sub-micron-level tracking accuracy with advanced features such as full state feed-forward, 192 kHz servo rates, and look-ahead-based velocity control.

It also can improve tracking errors and part quality at high-speeds in applications such as fast-tool servos, high-dynamic optical focusing axes, and high-speed pick-and-place machines.

Additional Features:l Encoder interpolationl Dedicated analog and digital I/Ol Fiber-optic interfacel Separate power connections for motor and control

supply voltagesl Panel mount capable


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ECD Introduces Liquid Analyzer System X80-B88-S88

Electro Chemical Devices (ECD)1681 Kettering, Irvine, CA 92614, USATel: +1 949-336-6060 | Web: www.ecdi.com

Electro Chemical Devices (ECD) released its X80-B88-S88 liquid analyzer system, designed for hazardous areas, featuring a sealed explosion-proof enclosure

and intrinsically safe sensors. It has a NEMA 4X, IP66 sealed 316 stainless steel design that is FM approved.

This liquid analyzer system provides precise measurement of pH, ORP, DO, conductivity, resistivity or various specifications. It is ideal for use in oil/gas production sites, refining operations and storage terminals; chemical processing plants; electric power generation; fertilizer production and storage; and similar industries.

The X80 Transmitter is approved for Class I, DIV 1, Groups B, C, D; T4 Ta = -40°C to +80°C; Class I, Zone 1, IIB+H2 T4 -40° to +85°C. The B88 Barrier is approved for Class I, DIV 1 Groups B, C, D; T5 Ta -40°C to +80°C; Class I, Zone 1 IIB+H2 T5 -40°C to +80°C.

The S88 Sensor is intrinsically safe for Class I, DIV 1, Groups B, C, D; T5 Ta = -40°C to +80°C; Class I, Zone 0 IIB+H2 T5 -40°C to +80°C. Versions with ATEX IECEx certification are available.

The transmitter is available as either a single- or a dual-channel instrument for continuous measurement with standard MODBUS or optional HART digital bus communications and three optional alarm relays.

The transmitter features a large (2.75-x-1.5 inch) easily viewable 128-x-64 pixel LCD display with three easily switchable main display screens for data, millivolt and graphics. The display comes with a gray background and black lettering for loop powered instruments or with a blue background and white lettering with LED backlighting on 24 Vdc powered instruments.

Each S88 Sensor contains a B88 Barrier comprising energy limiting devices to comply with FM hazardous location requirements. Connection between the X80 and B88 Barrier is accomplished using flameproof conduit. S88 Sensor connects to the B88 Barrier with a MiniFast Connector with tamper-proof lockout guard.

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Intelligent Manufacturing

Integrated and Intelligent Manufacturing: Perspectives and Enablers

YUbAO CHeNDepartment of Industrial and Manufacturing Systems Engineering, University of Michigan–Dearborn, Dearborn, [email protected]

Abstract:With ever-increasing market competition and advances in

technology, more and more countries are prioritizing advanced manufacturing technology as their top priority for economic growth. Germany announced the Industry 4.0 strategy in 2013. The US government launched the Advanced Manufacturing Partnership (AMP) in 2011 and the National Network for Manufacturing Innovation (NNMI) in 2014. Most recently, the Manufacturing USA initiative was officially rolled out to further “leverage existing resources... to nurture manufacturing innovation and accelerate commercialization” by fostering close collaboration between industry, academia and government partners. In 2015, the Chinese government officially published a 10-year plan and roadmap toward manufacturing: Made in China 2025. In all these national initiatives, the core technology development and implementation is in the area of advanced manufacturing systems. A new manufacturing paradigm is emerging, which can be characterized by two unique features: integrated manufacturing and intelligent manufacturing. This

trend is in line with the progress of industrial revolutions, in which higher efficiency in production systems is being continuously pursued. To this end, 10 major technologies can be identified for the new manufacturing paradigm. This paper describes the rationales and needs for integrated and intelligent manufacturing (i2M) systems. Related technologies from different fields are also described. In particular, key technological enablers, such as the Internet of Things and Services (IoTS), cyber-physical systems (CPSs) and cloud computing are discussed. Challenges are addressed with applications that are based on commercially available platforms such as General Electric (GE)’s Predix and PTC’s ThingWorx.

1. introductionIt is well-known that manufacturing is the most important

resource for today’s wealth-generating process. Manufacturing is a critical element of economic growth in all regions. With the introduction of the concept of Industry 4.0 by Germany, there has recently been a great emphasis in advancing manufacturing

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technologies around the world, in developed and developing countries. This advance is being achieved by joint effort between government and private sectors and through the close collaboration of industry and academia. It has spearheaded a strong movement toward a brighter manufacturing future.

This paper provides a study of the manufacturing technology trend and of the two unique features of integrated manufacturing and intelligent manufacturing. Aspects of the technical enablers for advanced manufacturing systems are described and potential future directions and challenges are discussed.

1.1. The Fourth Industrial RevolutionLooking at the historical advancement of manufacturing

system technology, three fundamental measurements are often used: quality, productivity and cost. These three critical measurements are both related to each other and integrated together. However, early industrial revolutions focused more on the measurement of productivity than on the other two measurements. In other words, manufacturing productivity and efficiency are the focal points for manufacturing technology advancement, while quality and cost are the constraints. In this circumstance, the question of how to improve the productivity and efficiency of manufacturing systems has been the critical issue in industrial revolutions.

Fig. 1 depicts the progress and characteristics of the industrial revolutions. During the First Industrial Revolution, with the introduction of Walter’s steam engine technology at the end of the 18th century, the method of production was changed from manual craftsmanship to mechanical production, leading to a great improvement in productivity. In the Second Industrial Revolution, with the introduction of electrical power and the transfer line, pioneered by Henry Ford at the beginning of the 20th century, high-speed mass production became the standard manufacturing practice. As a result, productivity was significantly improved and reached a whole new level. During the Third Industrial Revolution, manufacturing efficiency and productivity have been further enhanced by the combination of information technology (IT) and automation systems, such as flexible manufacturing systems (FMSs) and robotic technology. Now, as we consider ourselves to be experiencing the dawn of the Fourth Industrial Revolution, the Internet and smart devices are being widely used to further improve the productivity and flexibility of manufacturing systems.

1.2. Manufacturing initiatives in different regionsIn 2013, Germany unveiled its Industry 4.0 strategy, which

directed a great deal of global attention to the advances in manufacturing systems technology [1]. In the United States, the government launched the Advanced Manufacturing Partnership (AMP) in 2011. Since then, many other initiatives have been rolled out, including the Advanced Manufacturing Partnership Steering Committee “2.0” in 2013; the National Network for Manufacturing Innovation (NNMI) in 2014; and the Revitalize

American Manufacturing and Innovation Act, which was signed into law by the President of the United States in December 2014 [2]. Most recently, Manufacturing USA was officially launched by the US government in order to further “leverage existing resources... to nurture manufacturing innovation and accelerate commercialization” by fostering close collaboration between industry, academia and government partners [3]. In 2015, the Chinese government officially published a 10-year plan and roadmap toward manufacturing: Made in China 2025 [4]. The largest international collaborative program, Intelligent Manufacturing Systems (IMS), which is led by Japan, is also rolling out a roadmap for its next step with its IMS2020 project.

2. A new paradigm: integrated and intelligent manufacturing

Among the many features characterizing today’s modern manufacturing system technology, such as lean, virtual and rapidresponse systems, two features stand out and are sure to be carried over into the next generation of manufacturing: integrated manufacturing and intelligent manufacturing. As shown in Fig. 2, the market and process demands have driven technology from an informationintensive focus to a knowledge-intensive paradigm, in which big data analytics and knowledge bases play an important role in the current manufacturing environment.

The evolution of integrated and intelligent manufacturing (i2M) technology is driven not only by the market demand, but also by technological advances. There are 10 major technologies that can be identified as the key elements of the new manufacturing paradigm. As shown in Fig. 3, these technologies include six supporting elements: three-dimensional (3D) printing or additive manufacturing, robotic automation, advanced materials, virtual or augmented reality, the Industrial Internet and cyber-physical systems (CPSs). They also include four foundational elements: big data analytics, cloud computing, applications and mobile devices. The ways in which these elements impact advanced manufacturing systems and, more specifically, how they affect i2M, are described in the following sections.

2.1. Integrated manufacturingThe introduction of the concept of manufacturing systems

began with advances in digital computing capability in the 1960s; at that point, some kind of integration started to emerge within manufacturing. Under this scenario, the machines and devices in a manufacturing process are no longer isolated. Rather, they are parts of a system and all the components can be effectively coordinated in order to achieve improved productivities. For example, the computerintegrated manufacturing system (CIMS) has been widely adopted by companies.

The Internet of Things (IoT) and CPS technologies opened the door for tremendous opportunities in advancing such integration to a whole new level, making integration wider,


deeper and more open. As a result, manufacturing system controls are no longer limited to dealing with physical things and devices such as materials and machines; they are now able to process a large range of data, information and knowledge in real time. This processing is realized by three levels of integration in manufacturing: vertical integration, horizontal integration and end-to-end integration [1].

2.1.1. Vertical integrationVertical integration addresses the issue of seamless

connectivity among all the elements that are included in the product life cycle within an organization. Activities in marketing, design, engineering, production and sales are all closely integrated. Technologies such as the manufacturing execution system (MES) and computer-aided process planning (CAPP)

589Y. Chen / Engineering 3 (2017) 588–595

the constraints. In this circumstance, the question of how to im-prove the productivity and efficiency of manufacturing systems has been the critical issue in industrial revolutions.

Fig. 1 depicts the progress and characteristics of the industrial revolutions. During the First Industrial Revolution, with the intro-duction of Walter’s steam engine technology at the end of the 18th century, the method of production was changed from manual crafts-manship to mechanical production, leading to a great improvement in productivity. In the Second Industrial Revolution, with the in-troduction of electrical power and the transfer line, pioneered by Henry Ford at the beginning of the 20th century, high-speed mass production became the standard manufacturing practice. As a re-sult, productivity was significantly improved and reached a whole new level. During the Third Industrial Revolution, manufacturing efficiency and productivity have been further enhanced by the com-bination of information technology (IT) and automation systems, such as flexible manufacturing systems (FMSs) and robotic technol-ogy. Now, as we consider ourselves to be experiencing the dawn of the Fourth Industrial Revolution, the Internet and smart devices are being widely used to further improve the productivity and flexibility of manufacturing systems.

1.2. Manufacturing initiatives in different regions

In 2013, Germany unveiled its Industry 4.0 strategy, which di-rected a great deal of global attention to the advances in manufac-turing systems technology [1]. In the United States, the government launched the Advanced Manufacturing Partnership (AMP) in 2011. Since then, many other initiatives have been rolled out, including the Advanced Manufacturing Partnership Steering Committee “2.0” in 2013; the National Network for Manufacturing Innovation (NNMI) in 2014; and the Revitalize American Manufacturing and Innovation Act, which was signed into law by the President of the United States in December 2014 [2]. Most recently, Manufacturing USA was offi-cially launched by the US government in order to further “leverage existing resources... to nurture manufacturing innovation and accel-erate commercialization” by fostering close collaboration between industry, academia, and government partners [3]. In 2015, the Chi-nese government officially published a 10-year plan and roadmap toward manufacturing: Made in China 2025 [4]. The largest inter-national collaborative program, Intelligent Manufacturing Systems (IMS), which is led by Japan, is also rolling out a roadmap for its next step with its IMS2020 project.

2. A new paradigm: Integrated and intelligent manufacturing

Among the many features characterizing today’s modern man-ufacturing system technology, such as lean, virtual, and rapid-

response systems, two features stand out and are sure to be carried over into the next generation of manufacturing: integrated manufac-turing and intelligent manufacturing. As shown in Fig. 2, the market and process demands have driven technology from an informationintensive focus to a knowledge-intensive paradigm, in which big data analytics and knowledge bases play an important role in the current manufacturing environment.

The evolution of integrated and intelligent manufacturing (i2M) technology is driven not only by the market demand, but also by technological advances. There are 10 major technologies that can be identified as the key elements of the new manufacturing paradigm. As shown in Fig. 3, these technologies include six supporting ele-ments: three-dimensional (3D) printing or additive manufacturing, robotic automation, advanced materials, virtual or augmented re-ality, the Industrial Internet, and cyber-physical systems (CPSs). They also include four foundational elements: big data analytics, cloud computing, applications, and mobile devices. The ways in which these elements impact advanced manufacturing systems and, more specifi-cally, how they affect i2M, are described in the following sections.

2.1. Integrated manufacturing

The introduction of the concept of manufacturing systems began with advances in digital computing capability in the 1960s; at that point, some kind of integration started to emerge within manufac-turing. Under this scenario, the machines and devices in a manu-facturing process are no longer isolated. Rather, they are parts of a system, and all the components can be effectively coordinated in or-der to achieve improved productivities. For example, the computerintegrated manufacturing system (CIMS) has been widely adopted by companies.

The Internet of Things (IoT) and CPS technologies opened the door for tremendous opportunities in advancing such integration to a whole new level, making integration wider, deeper, and more open. As a result, manufacturing system controls are no longer lim-ited to dealing with physical things and devices such as materials and machines; they are now able to process a large range of data, information, and knowledge in real time. This processing is realized by three levels of integration in manufacturing: vertical integration, horizontal integration, and end-to-end integration [1].

Fig. 1. The progress and characteristics of industrial revolutions. CNC: computer numerical controller; PLC: programmable logic controller; ICT: information and communications technology; CPS: cyber-physical system.

Fig. 2. The new trend in manufacturing systems. MES: manufacturing execution system.

Fig. 3. Ten major technologies for i2M.


the constraints. In this circumstance, the question of how to im-prove the productivity and efficiency of manufacturing systems has been the critical issue in industrial revolutions.

Fig. 1 depicts the progress and characteristics of the industrial revolutions. During the First Industrial Revolution, with the intro-duction of Walter’s steam engine technology at the end of the 18th century, the method of production was changed from manual crafts-manship to mechanical production, leading to a great improvement in productivity. In the Second Industrial Revolution, with the in-troduction of electrical power and the transfer line, pioneered by Henry Ford at the beginning of the 20th century, high-speed mass production became the standard manufacturing practice. As a re-sult, productivity was significantly improved and reached a whole new level. During the Third Industrial Revolution, manufacturing efficiency and productivity have been further enhanced by the com-bination of information technology (IT) and automation systems, such as flexible manufacturing systems (FMSs) and robotic technol-ogy. Now, as we consider ourselves to be experiencing the dawn of the Fourth Industrial Revolution, the Internet and smart devices are being widely used to further improve the productivity and flexibility of manufacturing systems.

1.2. Manufacturing initiatives in different regions

In 2013, Germany unveiled its Industry 4.0 strategy, which di-rected a great deal of global attention to the advances in manufac-turing systems technology [1]. In the United States, the government launched the Advanced Manufacturing Partnership (AMP) in 2011. Since then, many other initiatives have been rolled out, including the Advanced Manufacturing Partnership Steering Committee “2.0” in 2013; the National Network for Manufacturing Innovation (NNMI) in 2014; and the Revitalize American Manufacturing and Innovation Act, which was signed into law by the President of the United States in December 2014 [2]. Most recently, Manufacturing USA was offi-cially launched by the US government in order to further “leverage existing resources... to nurture manufacturing innovation and accel-erate commercialization” by fostering close collaboration between industry, academia, and government partners [3]. In 2015, the Chi-nese government officially published a 10-year plan and roadmap toward manufacturing: Made in China 2025 [4]. The largest inter-national collaborative program, Intelligent Manufacturing Systems (IMS), which is led by Japan, is also rolling out a roadmap for its next step with its IMS2020 project.

2. A new paradigm: Integrated and intelligent manufacturing

Among the many features characterizing today’s modern man-ufacturing system technology, such as lean, virtual, and rapid-

response systems, two features stand out and are sure to be carried over into the next generation of manufacturing: integrated manufac-turing and intelligent manufacturing. As shown in Fig. 2, the market and process demands have driven technology from an informationintensive focus to a knowledge-intensive paradigm, in which big data analytics and knowledge bases play an important role in the current manufacturing environment.

The evolution of integrated and intelligent manufacturing (i2M) technology is driven not only by the market demand, but also by technological advances. There are 10 major technologies that can be identified as the key elements of the new manufacturing paradigm. As shown in Fig. 3, these technologies include six supporting ele-ments: three-dimensional (3D) printing or additive manufacturing, robotic automation, advanced materials, virtual or augmented re-ality, the Industrial Internet, and cyber-physical systems (CPSs). They also include four foundational elements: big data analytics, cloud computing, applications, and mobile devices. The ways in which these elements impact advanced manufacturing systems and, more specifi-cally, how they affect i2M, are described in the following sections.

2.1. Integrated manufacturing

The introduction of the concept of manufacturing systems began with advances in digital computing capability in the 1960s; at that point, some kind of integration started to emerge within manufac-turing. Under this scenario, the machines and devices in a manu-facturing process are no longer isolated. Rather, they are parts of a system, and all the components can be effectively coordinated in or-der to achieve improved productivities. For example, the computerintegrated manufacturing system (CIMS) has been widely adopted by companies.

The Internet of Things (IoT) and CPS technologies opened the door for tremendous opportunities in advancing such integration to a whole new level, making integration wider, deeper, and more open. As a result, manufacturing system controls are no longer lim-ited to dealing with physical things and devices such as materials and machines; they are now able to process a large range of data, information, and knowledge in real time. This processing is realized by three levels of integration in manufacturing: vertical integration, horizontal integration, and end-to-end integration [1].

Fig. 1. The progress and characteristics of industrial revolutions. CNC: computer numerical controller; PLC: programmable logic controller; ICT: information and communications technology; CPS: cyber-physical system.

Fig. 2. The new trend in manufacturing systems. MES: manufacturing execution system.

Fig. 3. Ten major technologies for i2M.

can thus be better utilized in order to support information and knowledge sharing within the organization. In this way, resources within the company—including but not limited to information, data, capital and human resources—can be used more effectively and efficiently.

2.1.2. Horizontal integrationHorizontal integration occurs when a company is closely

integrated with its suppliers and partners. Modern industry has already adopted supply-chain management technology, such that a horizontal value network has been established in many industry sectors. However, challenges still exist in terms of efficiency, intellectual property protection, the establishment of common standards, knowledge sharing and so forth. With the implementation of an advanced knowledge base and an Industrial Internet, those barriers can potentially be removed. A common knowledge network platform with practical protocols and standards is needed in order to further enhance the effectiveness and quality of horizontal integration.

2.1.3. End-to-end integrationEnd-to-end integration is probably the most active area

in the new age of manufacturing. Firstly, on the factory floor, machineto-machine integration is provided so that machines are truly an integral part of the manufacturing system. Secondly, it is now feasible to integrate customers into the manufacturing system, thus allowing engineers to obtain feedback from customers easily and in a timely manner. Thirdly, product-to-service integration is feasible, allowing the condition of the product in use to be directly monitored by the manufacturer. In this way, the value chain will be extended to the customer service of the product.

2.2. Intelligent manufacturingDue to the increased complexity of modern manufacturing

systems—particularly after all the units/elements been integrated into a common system—process decisions have become much more difficult. There is a strong need to leverage vast amounts of manufacturing data and to utilize the power of computing intelligence to enhance the decision-making process in manufacturing. Intelligent capability refers to three functions, which operate in an analogy of a human body: sensing, decision-making and action. With today’s rapid advances in sensing and control technologies, there is no lack of sensors or actuators in manufacturing systems. The challenge is how to process information and knowledge so that the right decision can automatically be made by a computer at the right time and in the right location, with little or no human intervention. New technologies are emerging in this areas, such as big data analytics, machine learning (ML) and cloud computing, which provide great potential for enhanced intelligent capability in manufacturing.


2.2.1. Big data analytics

Big data analytics is becoming a critical component of today’s IoT environment. It refers to the process of extracting information and knowledge from big data by uncovering hidden clusters and correlations, so that systematic patterns can be recognized and a better decision can be made. A tremendous amount of data is available throughout the manufacturing process today, from machines, production, logistics and user feedbacks. These data were typically unavailable or did not exist in the traditional manufacturing environment, so conventional analysts cannot deal with such large amounts of data. New procedures and schemes are being developed in big data analysis, such as correlation and clustering, statistical modeling and cognitive ML. With big data analytics, it is feasible to use only relevant and core information from terabytes or more of datasets in manufacturing and the right decision can be made effectively. With this approach, the control of manufacturing systems will shift from reactive decision-making to proactive decision-making. Due to its great potential for manufacturing applications, big data analytics is gaining ground and becoming more and more important for advanced manufacturing systems.

2.2.2. Machine learningOne of the key characteristics of human intelligence is the

capability to learn. ML refers to the computer’s capability to understand and learn the inside of a physical system through computing algorithms based on data. Data mining, statistical pattern-recognition algorithms and artificial neuron networks (ANNs) are some examples of ML methods. For manufacturing systems, the implementation of an ML algorithm makes it feasible for a machine or other device to learn its baseline and working conditions automatically. It is also feasible to create and upgrade a knowledge base throughout the manufacturing process.

2.2.3. Cloud computingCloud computing provides an Internet-based computing

service, which makes it possible to share software so that a user does not have to install the needed software locally. This practice is often called “software as a service” (SaaS). For manufacturing system implantations, however, sharing software through the Internet is no longer enough. It is also necessary to share information and knowledge in such a way as to create a marketplace for software and knowledge sharing. This practice is called “platform as a service” (PaaS). Efforts are being made to develop and implement PaaSs for manufacturing applications.

It can be foreseen that in the near future, with the advancement of intelligent manufacturing technology, data and information will be collected in real time by well-equipped sensors and transducers from all areas in the product life cycle. The data will then be processed through cloud computing and accurate decisions can be made continuously and autonomously with little or no human intervention. As pointed out in Ref. [5], a new manufacturing platform—cloud manufacturing—is feasible

by “combining with the emerged technologies such as cloud computing, the Internet of Things, serviceoriented technologies and high performance computing.” An efficient manufacturing eco-environment is emerging as integration and intelligence become the two hallmarks of a new manufacturing paradigm.

3. technology enablers

3.1. The Internet of ThingsThe term “IoT” was first introduced by the British entrepreneur

Kevin Ashton in 1999 while he was working on a global network of radio-frequency identification (RFID)-connected objects. Today, with the rapid development of Internet technology, many physical objects can be connected via the Internet through embedded electronics, software, sensors and network devices. This has been further expanded to non-physical systems, such as service or social elements. Therefore, IoT is also referred to as the Internet of Things and Services (IoTS).

For i2M systems, the IoT provides a unique and much-needed foundation that is capable of connecting all the elements of a manufacturing system together. In this way, not only can the efficiency of data collection be improved, but the quality of the data can also be significantly improved. The IoT also enables network control and the management of manufacturing equipment, assets and information flow.

Leading IT companies are providing network hardware and software support for the implementation of intelligent manufacturing systems. For example, Cisco provides the following product and services: network connectivity, fog computing, security, data analytics and automation [6].

3.2. The cyber-physical systemThe CPS is a system of collaborating computational elements

and controlling physical entities. It refers to a new generation of systems with integrated computational and physical capabilities that can interact with humans through many new modalities [7]. Many CPS devices have been developed and used in industries, including the aerospace, automotive, energy, healthcare and manufacturing industries. This generation is often regarded as embedded systems. In fact, CPS forms the backbone of IoT implementation.

Embedded devices are objects that have special sensing and computing capabilities. They are the critical elements in Industrial Internet implementation. With these devices, it is feasible to process data at the local level so that useful information or abstracted data are communicated through the network. In this way, communication efficiency can be significantly improved.

Current embedded devices have limited computation capabilities. A new generation of smart embedded devices is being developed for many applications. In a manufacturing process, smart embedded devices, such as the Watchdog Agent™, are capable of not only collecting data, but also processing the data so that certain decisions can be made fast and locally. More


and more of these smart embedded devices will be adopted in manufacturing systems for intelligent decision-making.

A fully fledged CPS is typically designed as a network of interacting elements with physical input and output instead of as standalone devices. Attention has been paid to its connectivity and to the intelligence mechanics of computational intelligence. Mobile CPS has emerged with advances in cloud computing and wireless sensing technologies. Security and reliability are two critical requirements and challenges of mobile CPS. Long-distance and global mobile CPS are yet to come.

3.3. Industrial InternetAs mentioned earlier, the IoT provides the foundation for

cloud computing and makes SaaS and PaaS feasible. However, for manufacturing applications, an industry-hardened IoT is needed to provide the reliability and security that are required by industry. To this end, an industry consortium initiated by General Electric (GE) is developing Internet technology for industry, resulting in a special IoT system for industrial application: the Industrial Internet of Things (IIoT).

The IIoT refers to the integration and connectivity of complex physical machines and devices, humans and resources through networked sensors and software for the purposes of industry production and operations. As stated by GE: “By taking advantage of the rapid explosion of sensors, ultra-low cost connectivity and data storage together with powerful analytics (commonly referred to as IIoT…) these value-added services can produce business outcomes for customers and produce incremental revenue for the company” [8]. The term IIoT was first introduced by Frost and Sullivan around the turn of this century. At that time, the Industrial Internet, which is the fundamentaltool for the implementation of IIoT, was used as a collective toolset for a digital enterprise transformation. Today, this term is often used along with other terms, such as the IoT, Industry 4.0, big data, ML and machine-to-machine communication. It is a core strategy in the United States to provide the critical capability for the next industrial revolution: IT-connected machines, people and resources.

Other than regular Internet applications, such as office automation, the Industrial Internet requires conditions such as a hardened environment on the factory floor and extreme dependence on its reliability. For example, factory floor equipment must tolerate a wider range of temperature, vibration, electrical interference and humidity, along with frequent interruptions. In addition, the Industrial Internet must be compatible with various physical devices, such as machines, robots, conveyors, testing equipment and tooling equipment. Due to the evolution of the technology and equipment on most factory floors, the IIoT must be able to work with modern and legacy equipment and protocols. It also requires a high level of security in the face of possible intrusion from both the inside and outside of the plant.

To address these challenges, industry-hardened industrial

networks have been developed that often use network switches to segment a large system into logical sub-networks, divided by address, protocol, or application. Systematic logical control and firewall systems are also used when it becomes necessary to connect to an office automation network for the vertical and horizontal integration of the enterprise.

To facilitate the implementation of the IIoT for effective industry applications, GE recently announced its software solution: the Predix Cloud [9]. The core capability of the Predix software system is to capture data out of large manufacturing or industrial operations and to perform analytics on them.

4. industrial practices and implementations4.1. Emerging technology trends in manufacturing

In the continued push to realize Industry 4.0 capability, many manufacturers have recognized that the adoption of new technology trends is necessary for their businesses. At the end of last year, Manufacturing News predicted five emerging technology trends for i2M systems. These are: cybersecurity, advanced materials, 3D printing, predictive analytics and collaborative robots [10].

4.1.1. CybersecurityWith advances in network technology and particularly in

the mobile network system, individual privacy and company security continue to be critical issues—not only for information protection, but also (and more importantly) for the safety of advanced manufacturing systems.

An increasing number of companies are developing and implementing internal cybersecurity systems. To enhance industrial cybersecurity, the US government is developing the necessary technology and legal protection. The US National Institute of Standards and Technology has established a cybersecurity framework in order to share best practices and technologies to allow industry to effectively address the security issue. Cybersecurity will remain the top priority for many companies as they prepare for Industry 4.0 implementation.

4.1.2. Advanced materialsNew and improved materials are much needed for modern

products and manufacturing. Carbon fiber has been rapidly adopted by the industry for its improved material properties and reduced weight. Carbon nanotube manufacturing has shown impressive improvement in recent years.

There are significant needs in high-tech areas as well, such as needs for new materials for batteries and 3D printing. Desirable characteristics of new materials include energy storage capability, a light weight, information-processing capability, a smart memory and so forth.


4.1.3. 3D printingNew additive manufacturing systems and materials have

improved greatly in recent years and their growth will continue in the future. Additive manufacturing holds great potential for further improvement in production efficiency, as well as in product design and development processes. With the availability of new materials and with improvements in the accuracy of 3D printing machines, more and more industries will embrace this technology for their production. It has been forecast that this may be the year in which manufacturers start to adopt 3D printing on a large scale. In fact, some industries, such as the aerospace and hospital industries, have already begun producing critical components using 3D printing technologies.

4.1.4. Predictive analyticsPredictive analytics is probably the most successful and

promising application of big data technology for industrial applications today. Manufacturing companies have realized that a tremendous amount of data is available throughout their manufacturing systems and is either being wasted or insufficiently utilized. To remedy this deficiency, predictive analytics may well be the most promising solution. Many companies are racing to develop learning and analysis algorithms for effective and practical analytics that can yield future predictions of machine or equipment conditions. In this way, maintenance can be performed more efficiently and equipment down time can be reduced significantly.

4.1.5. Collaborative robotsCollaborative robots provide a unique benefit to

manufacturing systems: their capability to work with human operators. This capability makes robot systems flexible and smart in dealing with complex and challenging material-handling and manufacturing situations. Robots are no longer viewed as standalone machines that are separate from human interactions. An increasing number of robots will be used in manufacturing for improved automation and reduced cost. To this end, collaborative robotics technology is being rapidly developed and implemented.

4.2. Intelligent manufacturing platformsIn order to implement intelligent manufacturing technology,

industries are preparing to develop cloud computing platforms based on IIoT. It has been estimated that investment in IIoT is expected to reach $60 trillion in the next 15 years. By 2020, it is predicted that more than 50 billion devices will be connected to the Internet. In order to unleash the benefits of IIoT for industrial applications, many software platforms are being developed and deployed, such as the aforementioned Predix platform by GE. A significant feature of all such platforms is the capability to build “digital twins.” A digital twin is a computerized model of a physical device or system that represents all functional features and links with the working elements. A digital twin is more than a virtual

592 Y. Chen / Engineering 3 (2017) 588–595

characteristics of new materials include energy storage capability, a light weight, information-processing capability, a smart memory, and so forth.

4.1.3. 3D printingNew additive manufacturing systems and materials have im-

proved greatly in recent years, and their growth will continue in the future. Additive manufacturing holds great potential for further improvement in production efficiency, as well as in product design and development processes. With the availability of new materials and with improvements in the accuracy of 3D printing machines, more and more industries will embrace this technology for their production. It has been forecast that this may be the year in which manufacturers start to adopt 3D printing on a large scale. In fact, some industries, such as the aerospace and hospital industries, have already begun producing critical components using 3D printing technologies.

4.1.4. Predictive analyticsPredictive analytics is probably the most successful and prom-

ising application of big data technology for industrial applications today. Manufacturing companies have realized that a tremendous amount of data is available throughout their manufacturing sys-tems, and is either being wasted or insufficiently utilized. To remedy this deficiency, predictive analytics may well be the most promising solution. Many companies are racing to develop learning and anal-ysis algorithms for effective and practical analytics that can yield future predictions of machine or equipment conditions. In this way, maintenance can be performed more efficiently and equipment down time can be reduced significantly.

4.1.5. Collaborative robotsCollaborative robots provide a unique benefit to manufactur-

ing systems: their capability to work with human operators. This capability makes robot systems flexible and smart in dealing with complex and challenging material-handling and manufacturing sit-uations. Robots are no longer viewed as standalone machines that are separate from human interactions. An increasing number of robots will be used in manufacturing for improved automation and reduced cost. To this end, collaborative robotics technology is being rapidly developed and implemented.

4.2. Intelligent manufacturing platforms

In order to implement intelligent manufacturing technology, in-dustries are preparing to develop cloud computing platforms based on IIoT. It has been estimated that investment in IIoT is expected to reach $60 trillion in the next 15 years. By 2020, it is predicted that more than 50 billion devices will be connected to the Internet. In or-der to unleash the benefits of IIoT for industrial applications, many software platforms are being developed and deployed, such as the aforementioned Predix platform by GE.

A significant feature of all such platforms is the capability to build “digital twins.” A digital twin is a computerized model of a physical device or system that represents all functional features and links with the working elements. A digital twin is more than a virtual computer system for simulation study. It provides the oper-ation status, insights, outcomes, and knowledge that are associated with the proper functions of the physical system. A digital twin is capable of communicating with the physical system it represents via real-time sensing devices, so as to keep it almost synchronized with the real-time status, working condition, position, and environment situation. Digital twins allow for the prediction of future conditions.

4.2.1. GE: PredixGE’s Predix is a comprehensive, purpose-build industrial plat-

form for the implementation of intelligent systems to monitor and control physical devices or systems through the Industrial Internet [9]. As shown in Fig. 4 [9], the key elements in Predix include the Predix Edge, Predix Cloud, and Predix Machines. An edge-to-cloud deployment model is used in Predix, which differs from other public cloud-only models. This architecture is suitable for many industrial applications.

Predix was developed based on GE’s own practice. In its early years, GE had a need to build digital twins for the operation mon-itoring and control of machines such as turbines. For this purpose, GE developed twins and edges, and used them successfully through the Industrial Internet. GE then opened the platform to the public so that original equipment manufacturers (OEMs) and third-party developers could build twin virtual models for various systems.

The core in the Predix is its Cloud Foundry, which provides an open-source PaaS. It has a unique microservices architecture, which supports many existing languages and programming tools. Using

Fig. 4. The Predix platform [9]. CPU: central processing unit.

computer system for simulation study. It provides the operation status, insights, outcomes and knowledge that are associated with the proper functions of the physical system. A digital twin is capable of communicating with the physical system it represents via real-time sensing devices, so as to keep it almost synchronized with the real-time status, working condition, position and environment situation. Digital twins allow for the prediction of future conditions.

4.2.1. GE: PredixGE’s Predix is a comprehensive, purpose-build industrial

platform for the implementation of intelligent systems to monitor and control physical devices or systems through the Industrial Internet [9]. As shown in Fig. 4 [9], the key elements in Predix include the Predix Edge, Predix Cloud and Predix Machines. An edge-to-cloud deployment model is used in Predix, which differs from other public cloud-only models. This architecture is suitable for many industrial applications.

Predix was developed based on GE’s own practice. In its early years, GE had a need to build digital twins for the operation monitoring and control of machines such as turbines. For this purpose, GE developed twins and edges and used them successfully through the Industrial Internet. GE then opened the platform to the public so that original equipment manufacturers (OEMs) and third-party developers could build twin virtual models for various systems.

The core in the Predix is its Cloud Foundry, which provides an open-source PaaS. It has a unique microservices architecture, which supports many existing languages and programming tools. Using its modern development-and-operations (DevOps) environment, third-party developers and particularly app developers, can quickly build, test and implement scalable systems for various industrial applications. There are currently more than 500 000 twins and applications using this platform.

In Predix, industrial data are collected from manufacturing processes in unprecedented volumes, which are almost impossible for the public cloud to handle. In addition, Predix provides an industryspecific intelligent analytic service to help companies process, analyze and make decisions about their


manufacturing status. With such capability in place, it is possible for a company to make realtime decisions that can dramatically improve the business operation of the enterprise.

Typical applications of Predix include:l Scheduling and logisticsl Connected productsl Intelligent environmentsl Field-force managementl Industrial analyticsl Asset performancel Application performance management (APM)l Operations optimization

It should be pointed out that there is still a long way to go in the development and deployment of the Pridex platform. It is still in the early stages and its applications are largely in the processes of monitoring and diagnostic decision-making. Its analytic capability is still very limited. Its artificial intelligence (AI) and deep learning algorithms need to be further enhanced.

Recently, GE announced a collaborative program with China Telecom to provide the Predix service to customers in China. This development will certainly have a significant impact on the advance of intelligent manufacturing technologies in the largest manufacturing base in the world.

4.2.2. Siemens: Product life-cycle management and the smart factory

The smart factory is the leading concept in Germany’s Industry 4.0 strategy. There are two levels of smart factory technology. The first level focuses on the shop floor, where all production devices will be fully integrated by wired or wireless communication systems. Individual machines or devices will no longer be isolated. A fully automated MES will be deployed with various types of sensors, transducers and device controllers. Data and information can be collected in real time at various locations regarding device statuses, working conditions, environmental parameters (i.e., temperature or humidity) and so forth. This information will be readily available to human or system control for the monitoring, prediction and control of the manufacturing systems.

As a result, the shop floor will be much more smart and flexible. There is even a push to make end-to-end, machine-to-machine communication a reality. To this end, many sensing and signal-processing technologies, including smart image processing and recognition, are being developed and implemented on the factory floor. In the future, it will be feasible to have each machine, or even each product, carry a chip that stores all relevant information for effective communication. It is feasible that when a product arrives at a location, the product’s chip will transmit the process information to a machine so that appropriate processing preparation and execution can be performed without human intervention.

At the second level of smart factory technology, a smart

factory comes with a fully digitized factory model (i.e., a digital twin) for a production system. The digital twin is completely connected to the corporate product life-cycle management (PLM) system with sensors, controllers, programmable logic controllers (PLCs), computer numerical controllers (CNCs), supervisory control and data acquisition (SCADA) systems and other communication devices. The factory floor conditions of a system will be immediately reflected by its digital twin, for the effective monitoring, prediction and control of current and future events.

To facilitate communication between the digital twins, Siemens rolled out the Intosite software platform. This is a cloud-based application for sharing digital manufacturing and production information in a 3D context [11]. It provides smart map navigation of virtual factories in various locations and enables collaboration through thesharing of the same manufacturing data by engineers and managers at different locations.

With this software smart factory platform, a cloud-based manufacturing operational management (MOM) system can be implemented, through which data and information from the factory floor are updated in real time and are readily available, should a decision regarding the process need to be made. All changes on the factory floor can also be uploaded automatically to other IT systems, such as the PLM repository, with the Siemens PLM software: Teamcenter. In this way, the efficiency and productivity of a manufacturing system will be significantly improved. Siemens’s Fusion system attempts to support the IIoT platform and could be used for manufacturing implementation.

However, Siemens’ smart-factory-based technology requires extensive integration of the software platform with the factory floor systems. This system should also be more closely integrated with predictive analytics for further enhancement of its intelligent manufacturing decision process.

4.2.3. PTC: ThingWorxIn 2014, PTC acquired ThingWorx and further developed it

into a major IIoT platform by integrating it with PTC’s Internet-based PLM program. ThingWorx is now playing a major role in the implementation of intelligent manufacturing technology. It is a well-developed platform with several elements, including ThingWorx Studio, Thing-Worx Analytics, ThingWorx Utilities and ThingWorx Industrial Connectivity. As illustrated in Fig. 5 [12], all these elements work with the central piece: the ThingWorx Foundation.

The ThingWorx Foundation provides connections to all the ThingWorx components, with end-to-end security technology. It enables users to connect, create and deploy industry-specific applications throughout the IoT system. Among the components included in ThingWorx, three fundamental functions are provided: Core, Connection Services and Edge.

The ThingWorx platform was developed based on a groundup and model-drive approach. Many graphic drag-and-


drop tools have been developed and are available for the end user to use to construct specific applications; this makes its application relatively easy and convenient. Since it is one of the earliest platforms for IoT application for industries, there are many user-friendly tools and algorithms available for analysis and data presentation. Some of the tools are also used by other platforms, such as Pridex.

4.3. Predictive analytics for intelligent manufacturingIn the development of intelligent manufacturing technology,

effective predictive analytics is probably the most important element for the practical implementation of such technology. In all PaaSs, major efforts are devoted to building effective analytics for decisionmaking. In recent years, many leading computer software and hardware providers, such as IBM, Microsoft, Intel and Google, are gearing up their efforts to develop analytics technology. Advances in analytics technologies will have a significant impact on intelligent manufacturing implementation.

4.3.1. IBM: Predictive analyticsIBM has a long history of providing software tools for

industrial applications. In 2010, IBM rolled out a specific predictive analytics service platform: SPSS Predictive Analytics Enterprise. This platform aims at providing deep descriptive and predictive analytics for various industrial applications, including manufacturing, healthcare and administration management. As a single solution, it can work with all types of data, whether structured or unstructured, or in numerical or graphic formats. It also includes some processing tools with the capability for statistical data analysis, data mining and ML. It will be very interesting to see how manufacturers can benefit from another powerful IBM knowledge-building engine: IBM Watson Analytics.

IBM claims that the SPSS Predictive Analytics Enterprise can provide the following capabilities and benefits [13]:l Descriptive and inference statistical analysis;

l Predictive modeling and advanced algorithms for numerical, text and graphic data formats;

l The capability for interactive visual and plain-language presentations of data and information;

l A framework for secure and automatic data collection and management; and

l Real-time scoring for the predictive control of company assets.

4.3.2. Microsoft Azure: Machine learning and predictive analytics

Microsoft offers a collection of integrated cloud services through its Azure framework [14]. In this framework, certain ML and predictive analytics tools are provided. As illustrated in Fig. 6 [14], the user can create an ML application by selecting a set of read-to-use algorithms in the ML studio. The application can then be deployed through an Internet-connected processor, such as a PC, in order to build a predictive model for specific applications.

Other elements in the Azure framework that can be used for data processing and intelligent decision-making include HDInsight and R Server. With Microsoft Azure HDInsight and Microsoft R language, HDInsight clusters can be created in Azure so that users or R programmers can select a particular algorithm or method to build practical analytics.

At this time, the majority of applications of Microsoft Azure is in the commercial and service sectors, such as for the management of web services. However, these analytics tools should be employable in manufacturing areas in the future.

4.3.3. Intel: Nervana™ AI AcademyThe computer hardware giant Intel is also aggressively

developing its AI capability to provide ML and predictive analytics for various industrial applications. This is being done through the framework of Nervana™ AI Academy [15].

Nervana is a platform that is specifically for machine deep learning, based on its Nervana neon™ and Nervana Engine technologies. The Nervana Engine uses a new memory technology called highbandwidth memory that is both high capacity and high speed and is therefore particularly good at addressing the vast amount of data in today’s typical industrial environment. Nervana neonTM is a highlevel programming language that is used for deep learning programs. With the integration of Intel’s next-generation processor, Nervana provides a powerful AI platform with processing and builtin networking that has unprecedented speed and scalability. Intel claims that all form of data, including numerical or non-numerical data such as natural language and graphics, can be processed in this platform.

4.3.4. Google: Cloud ML PlatformGoogle has been developing AI and deep ML technology

for a long time. It provides a cloud-based ML service through its Google Cloud ML Platform. This net-based ML algorithm has excellent performance and accuracy. The uniqueness of the Google AI platform include its powerful text analysis, speech


its modern development-and-operations (DevOps) environment, third-party developers, and particularly app developers, can quickly build, test, and implement scalable systems for various industrial applications. There are currently more than 500 000 twins and applications using this platform.

In Predix, industrial data are collected from manufacturing pro-cesses in unprecedented volumes, which are almost impossible for the public cloud to handle. In addition, Predix provides an industry- specific intelligent analytic service to help companies process, ana-lyze, and make decisions about their manufacturing status. With such capability in place, it is possible for a company to make real- time decisions that can dramatically improve the business operation of the enterprise.

Typical applications of Predix include:

It should be pointed out that there is still a long way to go in the development and deployment of the Pridex platform. It is still in the early stages, and its applications are largely in the processes of monitoring and diagnostic decision-making. Its analytic capability is still very limited. Its artificial intelligence (AI) and deep learning algorithms need to be further enhanced.

Recently, GE announced a collaborative program with China Tel-ecom to provide the Predix service to customers in China. This de-velopment will certainly have a significant impact on the advance of intelligent manufacturing technologies in the largest manufacturing base in the world.

4.2.2. Siemens: Product life-cycle management and the smart factoryThe smart factory is the leading concept in Germany’s Industry

4.0 strategy. There are two levels of smart factory technology. The first level focuses on the shop floor, where all production devices will be fully integrated by wired or wireless communication sys-tems. Individual machines or devices will no longer be isolated. A fully automated MES will be deployed with various types of sensors, transducers, and device controllers. Data and information can be collected in real time at various locations regarding device statuses, working conditions, environmental parameters (i.e., temperature or humidity), and so forth. This information will be readily available to human or system control for the monitoring, prediction, and control of the manufacturing systems.

As a result, the shop floor will be much more smart and flexible. There is even a push to make end-to-end, machine-to-machine com-munication a reality. To this end, many sensing and signal-processing technologies, including smart image processing and recognition, are being developed and implemented on the factory floor. In the future, it will be feasible to have each machine, or even each product, carry a chip that stores all relevant information for effective communica-tion. It is feasible that when a product arrives at a location, the prod-uct’s chip will transmit the process information to a machine so that appropriate processing preparation and execution can be performed without human intervention.

At the second level of smart factory technology, a smart factory comes with a fully digitized factory model (i.e., a digital twin) for a production system. The digital twin is completely connected to the corporate product life-cycle management (PLM) system with sen-sors, controllers, programmable logic controllers (PLCs), computer numerical controllers (CNCs), supervisory control and data acquisi-tion (SCADA) systems, and other communication devices. The factory

floor conditions of a system will be immediately reflected by its digital twin, for the effective monitoring, prediction, and control of current and future events.

To facilitate communication between the digital twins, Siemens rolled out the Intosite software platform. This is a cloud-based ap-plication for sharing digital manufacturing and production informa-tion in a 3D context [11]. It provides smart map navigation of virtual factories in various locations, and enables collaboration through the sharing of the same manufacturing data by engineers and managers at different locations.

With this software smart factory platform, a cloud-based man-ufacturing operational management (MOM) system can be imple-mented, through which data and information from the factory floor are updated in real time and are readily available, should a decision regarding the process need to be made. All changes on the factory floor can also be uploaded automatically to other IT systems, such as the PLM repository, with the Siemens PLM software: Teamcenter. In this way, the efficiency and productivity of a manufacturing system will be significantly improved. Siemens’s Fusion system attempts to support the IIoT platform, and could be used for manufacturing im-plementation.

However, Siemens’ smart-factory-based technology requires ex-tensive integration of the software platform with the factory floor systems. This system should also be more closely integrated with predictive analytics for further enhancement of its intelligent manu-facturing decision process.

4.2.3. PTC: ThingWorxIn 2014, PTC acquired ThingWorx and further developed it into a

major IIoT platform by integrating it with PTC’s Internet-based PLM program. ThingWorx is now playing a major role in the implementa-tion of intelligent manufacturing technology. It is a well-developed platform with several elements, including ThingWorx Studio, Thing-Worx Analytics, ThingWorx Utilities, and ThingWorx Industrial Con-nectivity. As illustrated in Fig. 5 [12], all these elements work with the central piece: the ThingWorx Foundation.

The ThingWorx Foundation provides connections to all the ThingWorx components, with end-to-end security technology. It enables users to connect, create, and deploy industry-specific appli-cations throughout the IoT system. Among the components included in ThingWorx, three fundamental functions are provided: Core, Con-nection Services, and Edge.

The ThingWorx platform was developed based on a ground-up and model-drive approach. Many graphic drag-and-drop tools have been developed and are available for the end user to use to construct specific applications; this makes its application relatively easy and convenient. Since it is one of the earliest platforms for IoT application for industries, there are many user-friendly tools and algorithms available for analysis and data presentation. Some of the tools are also used by other platforms, such as Pridex.

Fig. 5. The ThingWorx platform [12].


recognition and image analysis capabilities In order to enhance industrial applications, Google recently added a new component to its ML platform: TensorFlow. This is an open-source software library for data processing with a data flow graph structure. In the data flow graph, nodes represent mathematical operations, while edges represent multidimensional data arrays. This framework can process a vast amount of data and information well, for a situation with high levels of uncertainty. This technology can be implemented in manufacturing, where high levels of uncertainty are always a reality.

4.4. ChallengesWith all these promising advances in manufacturing

systems technology, significant challenges still exist, mainly in the following areas: legacy IT infrastructure, standardization, knowledge base and closed-loop control.

4.4.1. Legacy IT infrastructureIn many companies, IT infrastructures were basically

developed as communication networks linking different data or information pools. Although this is efficient for limited data handling, it is becoming more and more difficult to deal with vast amounts of data and information across different manufacturing platforms. In particular, there is a significant security challenge when adopting a powerful cloud computing platform. Legacy IT infrastructure must be reevaluated or replaced for the new manufacturing paradigm.

4.4.2. StandardizationStandardization is necessary in order to integrate different

elements in manufacturing systems, including both hardware and software. At the device level, communication input/output and protocols must be standardized for efficient and secure data transfer. This is particularly critical for end-to-end integration. At the platform level, interfaces between software modules must be standardized so that the potential of computational intelligence can be fully utilized.

4.4.3. Knowledge baseThe availability of an effective knowledge base is still the

bottleneck in the implementation of intelligent manufacturing technology. Although ML techniques have been adopted for the construction of knowledge bases from data, significant challenges remain due to the high levels of uncertainty in the manufacturing environment. Based on the open structure of several IIoT platforms, such as GE’s Predix, many parties are working together to build a knowledge base; however, a practical and effective knowledge base that is capable of manufacturing monitoring and control is yet to be developed.

4.4.4. Closed-loop controlPredictive analytics has been playing a significant role in

intelligent manufacturing. However, its implementation and impact on the factory floor is still very limited. The link between analytics and actuation must be closed so that a truly intelligent closed-loop control strategy can be implemented in the next generation of intelligent manufacturing. To this end, both hardware and software innovations are urgently needed for the development of cloud manufacturing platforms.

5. ConclusionA new industrial revolution is on the horizon, led by

manufacturing revitalization and advances, which can be characterized as the i2M system. In many countries, governmental and private sectors are working closely together to upgrade the manufacturing base and improve market shares.

The core breakthrough in i2M technology is in the area of the complete integration of information and communication technology with modern manufacturing systems. To this end, advanced CPS and IIoT technologies, together with big data and cloud computing, are playing significant roles in the shift into a new manufacturing paradigm. Industrial platforms are being developed for the implantation of the new manufacturing ecosystem. With these advances in manufacturing, the benefits of the Fourth Industrial Revolution are being materialized and demonstrated.

References[1] Kagermann H, Wahlster W, Helbig J; National Academy of Science

and Engineering. Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Final report of the Industrie 4.0 Working Group. Munich: National Academy of Science and Engineering; 2013 Apr.

[2] Revitalize American Manufacturing and Innovation Act of 2014, H.R. 2996, 113th Cong. (2014).

[3] National Economic Council, Office of Science and Technology Policy. A strategy for American innovation. Washington, DC: The White House; 2015 Oct.

[4] Lee XE. Made in China 2025: A new era for Chinese manufacturing China. In: CKGSB Knowledge [Internet]. Beijing: Cheung Kong Graduate School of Business; 2015 Sep 2 [cited 2016 Nov 2]. Available from: http://knowledge.ckgsb.edu. cn/2015/09/02/technology/made-in-china-2025-a-new-era-for-chinese-manufacturing/.

[5] Zhang L, Luo Y, Tao F, Li BH, Ren L, Zhang X, et al. Cloud manufacturing: A new manufacturing paradigm. Enterp Inf Syst-UK 2014;8(2):167–87.

[6] Evans D. The Internet of Things: How the next evolution of the Internet is changing everything. White paper. San Jose: Cisco Systems, Inc.; 2011.

[7] Lee J, Bagheri B, Kao HA. A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manuf Lett 2015;3:18–23.

[8] GE Digital. Predix: The industrial internet platform, white paper.


Boston: General Electric Company; 2016.[9] Babcock C. GE Predix Cloud: Industrial support for

machine data. In: InformationWeek [Internet]. San Francisco: UBM Tech; 2015 Aug 6 [cited 2016 Nov 2]. Available from: http://www.informationweek.com/cloud/platform-as-a-service/ge-predix-cloud-industrial-support-for-machine-data/d/d-id/1321628.

[10] Schmidt M. 5 emerging technology trends for manufacturers in 2017. In: Manufacturing News [Internet]. Prospect: Design-2-Part; 2016 Dec 15 [cited 2016 Nov 2]. Available from: http://news.d2p.com/2016/12/15/5-emerging-technology-trends-for-manufacturers-in-2017/.

[11] Feuer Z, Weissman Z. Smart factory—The factory of the future [Internet]. Sunnyvale: LinkedIn; 2016 Dec 19 [cited 2016 Nov 6]. Available from: https://www.linkedin.com/pulse/smart-factory-future-zvi-feuer?articleId=8390740796107302304.

[12] The ThingWorx IoT Technology Platform [Internet]. Needham: PTC; c2017 [cited 2016 Nov 6]. Available from: https://www.thingworx.com/platforms/.

[13] IBM SPSS Predictive Analytics Enterprise [Internet]. Armonk: IBM Corporation; [cited 2016 Nov 6]. Available from: https://www.ibm.com/us-en/marketplace/spss-predictive-analytics-enterprise#product-header-top.

[14] Ericson G, Glover D, Franks L. Deploy an Azure Machine Learning web service [Internet]. 2017 Jan 6 [cited 2016 Nov 6]. Available from: https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-publish-a-machinelearning-web-service.

[15] Page C. Intel’s Nervana AI platform takes aim at Nvidia’s GPU technology. In: The Inquirer [Internet]. London: Incisive Business Media (IP) Limited; 2016 Nov 18 [cited 2016 Nov 6]. Available from: https://www.theinquirer.net/inquirer/news/2477796/intels-nervana-ai-platform-takes-aim-at-nvidias-gpu-techology.

originally published in: Engineering

Published by Elsevierhttp://dx.doi.org/10.1016/J.ENG.2017.04.009

Reproduced under CC BY Licence


understanding of Business Performance from the Perspective of Manufacturing strategies: Fit Manufacturing and Overall Equipment Effectiveness

Lean Manufacturing

AThe University of Mosul, Faculty of Administration and Economics, Department of Industrial Management, Mosul, IraqBDepartment of Business Administration, Faculty of Management, Universiti Teknologi Malaysia, Johor Bahru, MalaysiaCDepartment of Accounting & Finance, Faculty of Management, Universiti Teknologi Malaysia, Johor Bahru, MalaysiaDResearch and Development Department, Ahoora Ltd I Management Consultation Group, Kuala Lumpur, MalaysiaEHamta Business Solution Sdn Bhd I Business Development and Internation Trade, Kuala Lumpur, Malaysia

RAgHeD ibRAHim esmAeelA,B

[email protected]

NORHAYAti ZAkUANb

NORiZA mOHD JAmAlC

HAmeD tAHeRDOOstD,e

PHOT

O: AD

OBE S

TOCK

1. introductionManufacturing sector is an essential ingredient to accelerate

economic growth of the country. Recent advancements in globalization and technology affect manufacturing systems. Mostly, manufacturing sector focused on the usage of two broader manufacturing systems. These systems are Agile Manufacturing System and Lean Manufacturing System. However, stakeholders like customers, societies and policy makers consistently pressurising manufacturing sector to incorporate the social and environmental factor within manufacturing process to protect society and environment from negative effect of the manufacturing process. The purpose of these all manufacturing systems is to enhance manufacturing effectiveness through increasing process effectiveness and reducing cost. More so, global competition has necessitated the formulation of both efficient and effective paradigms in response to the global economies for the purpose of improving the


overall performance. Lean and Agile Manufacturing have gained wider acceptability in recent years’ enterprises. Leanness primarily leads to elimination of the non-value added activities while Agility focuses mainly on leads to market responsiveness [1]. Thus, by applying these strategies manufacturing sector is strategizing to enhance their Business Performance. Thus the integration of these two manufacturing strategies are vital to survive in current market competitive environment.

Recently, the managers have become strategic in developing measures techniques to effectively manage the manufacturing processes and machines. Most manufacturing companies are facing challenges, including waste of time, money and energy as well as overworked staff [2]. On the other hand, the Overall Equipment Effectiveness (OEE) has been shown to be a novel technique that can measure the effectiveness of a machine and it has been demonstrated to truly simplify complex production problems into simple and intuitive presentation of information. The OEE helps in the systematic analysis of the production processes while continually identifying potential problematic areas affecting the use of the machines [2]. Additionally, the OEE generates a quantitative metric based not only on element availability, but also performance and quality for evaluating the performance and effectiveness of an individual equipment or the entire processes [4]. Thus, these companies are attempting to raise the Business Process Management due to its achievement the overall improvement along with a quality of companies [5].

Managing OEE in the manufacturing industry is an essential strategy for continuous improvement of timely delivery and service quality in order to meet customers’ satisfaction as well as their expectations. Achieving customers’ satisfaction largely depends on vendors’ performance, reliability, responding to customers’ needs and continuous improvement. Managing the OEE is one of the approaches employed to ensure the reliability of the production operations and to be able to satisfy the customers and end users. This is the way for manufacturer to ensure reliability while supporting both entities’ competitiveness in the market, as well as complying the world class standards [6]. This paper is going to provide an overview of the Business Performance evaluation from the perspective of Fit Manufacturing and Overall Equipment Effectiveness.

2. PreliminaryManufacturing is defined as a process of transforming materials

into products. Hence, firms will ensure that customers are offered products with the lowest possible cost in order to have more efficient and effective manufacturing work. However, the environment of manufacturing seems to be faced with significant challenges, thus questioning the present view on manufacturing work. In fact, the most notable challenges for manufacturing in Malaysia today is the increased level of complexity and uncertainty as a result of increased globalization of the markets and operations, diversified demands of customers, drastic reductions in product lifecycles and manufacturing and ICT technology progress [7]. Moreover, all of these challenges are

linked to the ability of the firms to catch-up with the recent trends of manufacturing in order to stay in business.

On top of that, the recent developments in the manufacturing system suggests that the perspectives on manufacturing should be changed from a resource-based to knowledge-based view; from linearity to complexity; from individual to system competition; and from mono-disciplinarily to trans-disciplinarily [8]. Hence, Fit Manufacturing is treated as a medium to improve manufacturers’ Business Performance from a resource based view perspective [9]. According to Pham and Thomas [10], Fit Manufacturing framework assists manufacturing firms in becoming economically sustainable, and meeting global market competition. However, there is minimum effort made for analyzing the implementation of Fit Manufacturing in manufacturing firms’ business operations.

3. Fit manufacturingWilliams [11] revealed that Fit Manufacturing is a company-wide

approach, supporting governances to oversee problems in the marketplace, such as customer suppositions in relation to production. Fit Manufacturing can assist administrations through continuing Agility and Sustainability. Furthermore, Pham, Thomas [12] stated that Fit Manufacturing comprises of a number of combined activities, such as manufacturing, marketing and product innovation strategies which lead to the achievement of economic sustainability. Furthermore, it has been discovered that Fit Manufacturing is a competitive manufacturing model which involves Lean and Agile Manufacturing and its sustainable benefits [13]. Fit Manufacturing system can be considered as an integrated approach which involves Lean Manufacturing, Agile Manufacturing and Sustainability [13].

Fit Manufacturing is perceived as company-wide strategy which helps organizations to handle market place complexities, consumer expectations in terms of products and prices, adaptation of production capacities to meet new products designs challenges, waste elimination in processes, market fluctuations and supply chain management. Fit Manufacturing helps organizations to remain agile and sustainable through a strategic approach that emphasizes on motivated and skilled workforce, use of advance computer technologies and flexible organizational structure [13].

Before a production firm can be described as fit, the enterprise ought to be able to set out five fundamental elements of a fit firm which are (1) access to knowledge, experience and expertise (2) increased organizational flexibility and adaptability, (3) innovative products, (4) JIT inventory management and supply chain management, (5) product customization at mass production cost. Table 1 presents the Fit Manufacturing Components used in previous studies.

3.1. Lean Manufacturing (Leanness)Leanness means developing a value stream to eliminate all waste,

including time and to ensure a level schedule. The Lean Manufacturing System is classified as a manufacturing strategy which is a waste reduction method.


3.2. Agile Manufacturing (Agility)

Agile Manufacturing is considered as an operational strategy that organizations have utilized to reduce environmental risks, resulting in a global economic slowdown. Agile Manufacturing is intended to enhance the competitiveness of firms. Manufacturing processes based on Agile Manufacturing are characterized by customer supplier integrated process, involving product design, manufacturing, marketing and support services [20].

3.3. SustainabilitySustainability is considered as a method of determining the

ranking of economic opportunities over time [21]. Sustainability will determine a number of aspects, such as environmental, social and economic aspects [22]. Sustainable manufacturing can also be defined as the creation of manufactured products that uses non-polluting processes, natural resources and conserve energy and are economically sound and safe for employees, communities and consumers. In summary, Sustainability in Fit Manufacturing refers to the stability between demand and capacity.

4. Overall equipment effectiveness (Oee) In order to stay competitive, manufacturing companies must

have productive facilities [23]. With the stiffening global competition, companies strive for ways to gain competitive advantage by optimizing and improving their production [24]. To achieve this goal, a firm should have proper strategy for identifying and eliminating production losses so as to sell their products at the lowest possible cost. Also, there is a need to have an effective system to measure the productive performance of the manufacturing process. Overall Equipment Effectiveness (OEE) was proposed by Nakajima [25] as a quantitative tool to measure a factory’s individual equipment’s productivity. Overall Equipment Effectiveness is defined as one of the performance measurement tools that measure different kinds of production losses along with illustration areas for process improvement [26]. Bernstein [27] defined Overall Equipment Effectiveness as “the primary metric of Total Productive Maintenance which indicates a single piece of equipment's actual contribution as a percentage of its potential to add value to the value stream”. Gram [28] reveals that the Overall Equipment Effectiveness is considered as a useful metric toward measuring losses of equipment and also measures the effectiveness of a single machine. Thus, it can be concluded that OEE is a very valuable measure for identifying sources of losses in production. It is also very useful for performance optimization of the existing capacity, for deferring big capital

investments, overtime expenditure reductions, process variability reduction, operator performance improvement and reduction in changeover times. These benefits of OEE help a company to maintain a competitive edge over its competitors as well as enhance the production operation of the company. OEE helps to identify and measure manufacturing losses in significant areas such as quality rate, performance and availability. Stamatis [29] demonstrated a number of benefits of OEE including; productivity improvement, cost reduction and awareness raising along with machine productivity and increasing equipment life and consequently increase Business Performance. The outcomes of these goals are to increase profits, accomplish (or maintain) a competitive, distinguish equipment ownership and reduce costs.

5. business performanceNeely [30] asserts that “in the academic community people from

a wide variety of different functional backgrounds are researching the topic of performance measurement. Experts in accounting, economics, human resource management, marketing, operations management, psychology and sociology are all exploring the subject and one of the major problems with the field is that they are all doing so independently”. Furthermore, Duquette and Stowe [31] show that Business Performance measurement regard as an indication as estimating the level of project realization. Hence, a variety of performance measurements together with management systems have been gained considerable attention to evaluate different operational perspectives of performing a business [32-34]. According to the literature review, there are different dimensions to measure the Business Performance [34]. In brief, the Business Performance is measured through two perspectives, first from the Financial Viewpoint and Second, Non-Financial. For financial perspective, ROI and Profit Margins were used and for non-financial measures Occupancy percentage, Growth in Sales and Number of Successful new Services/Products. Table 2 provides the summery of Business Performance dimensions. As suggested [35, 36], Business Performance is dependent variable and should be measured by Subjective measures. The reason to use subjective measures is that many firms and organizations decline to disclose their real financial information and financial record [37]. In accordance with Dess and Robinson [38], the objective data will not indicate the actual Business Performance of a firm because there might be the manipulation of the data. Therefore, the literature supports subjective measurement as an fitting substitute to objective measurement [34, 37, 39].


5.1. Business growth

According to Davidsson, Kirchhoff [40], the organizational growth has become characteristic in the literature with many studies incorporating growth. As stated by Fitzsimmons, Steffens [41] growth is a crucial component of maintainable economic benefit, and it is difficult to separate sustained growth from profitability [42]. According to Wiklund [43], growth is an accurate and assessable performance measure compare to accounting indicators, therefore it is considered as the most important measure of performance, and provides a large indicator for financial performance, particularly for manufacturer’ firms. In relation to Sternberg [44], based on business performance literature, growth is often known as indication of success and as the best existing support for business performance success due to the fact that reliable data on the financial performance of manufacturers can be difficult to attain.

Experts mainly share the opinion that the growth of SMEs has a special importance in an economy. This study also considered that growth is an important precondition for the achievement of the other financial goals of a business in addition to considering as one of the vital elements for firm’s advantage [52]. According to Wiklund [43], growth is a more accurate and easily assessable performance measure, therefore it is considered as the most important measure of performance. Business growth can be a stable growth of total performance, which includes output, sales volume, profits and asset growth, or it can be a fast improvement of total performance [53, 54].

5.2. Profitability

Another most popular measure of business performance is profitability that must be considered it is unlikely that firm’ growth can be sustained without profits [41], therefore, profitability measures as well related to business performance in manufacturer. Therefore, based on scope and research questions in this study, profitability and growth might be the most appropriate and pertinent measures in Malaysia’ manufacturers context to measure the business performance. To continue operating and to chive profits, these two related measures might signify the two main objectives for any manufacturers

formation.

5.3. Market performance

Marketing performance measurement is the assessment of “the relationship between marketing activities and Business Performance” [55]. Market performance can be considered as one of the concepts that linked with all enterprises both size along with each sector since the market success of the firm is an importance of market performance [56]. Marketing performance has a positive influence on firm business performance, profitability and stock returns [57]. Marketing performance refers many of the objectives like customer satisfaction, perceived quality, customer loyalty, as well as firm reputation [58].

6. DiscussionEbrahim [14] found a positive correlation between Fit

Manufacturing and Business Performance. The author also suggested that the successful integration of the performance of Lean Manufacturing, Agile Manufacturing and Sustainability in the Production Fitness measures would be capable of indicating the ideal performance for both costoriented and profit-oriented strategies. The Leanness measure has always been associated with performance of profit-oriented strategies. On the other hand, Agility and Sustainability measures can be associated with the performance of cost-oriented strategies. Moreover, Yang, Hong [8] found the relationship between Lean Manufacturing practices, environmental management (environmental management practices along with environmental performance) and Business Performance outcomes (market together with financial performance). This study determined the Lean Manufacturing experiences are positively associated with environmental management practices which itself has negatively correlated with the market and financial performance. So according to Yang, Hong [8], there is relationship between Lean Manufacturing and Business Performance both direct and indirect.

Furthermore, from the perspective of Sustainability, Chen [59]


found a positive and direct effect of Sustainability with Business Performance in terms of improvement methods. He also concluded that environmental and social improvement practices have a positive direct correlation with the product and process innovation. Although Othman and Ameer [60] stated that there is a correlation between financial performance indexes and non-financial performance indexes like customer satisfaction index and employees’ satisfaction, they also suggested that there is a relationship between Sustainability and Performance either financial or non-financial.

Besides above, from the view point of Agility, a direct positive correlation was revealed between Lean Manufacturing and operational performance by Inman, Sale [48] in their model called Agile Manufacturing Model. They found a significant and positive relationship between Lean Manufacturing and Performance (operational, financial and marketing).

On the other hand, Pham and Thomas [10] demonstrated that there is a relationship between Fit Manufacturing and Overall Equipment Effectiveness (OEE). The authors also explained that OEE can be considered as one of the outputs of Fitting Manufacturing. In other words, Fit Manufacturing facilities enhancing the level of Overall Equipment Effectiveness. For example, Lean Manufacturing focuses on the low inventory and waste by increasing the process effectiveness which will result into the effective utilization of the equipment. Tamizharasi and Kathiresan [61] illustrates a relationship between effectiveness of equipment and efficiency which play a significant role in modern manufacturing industry to regulate the performance of the organization's production function as well as the success level in the organization. They mentioned that one of the best tools to improve the effectiveness of the manufacturing process is OEE. On the other hand, Azizi [62] found that there is a relationship between OEE and Agile Manufacturing. This means that there is a relationship between Agility as a dimension of Fit Manufacturing with OEE. Furthermore, the OEE is a metric that is usually employed in evaluating how successful a manufacturing operation is managed. The implementation of OEE is most vital in managing the sustainability of an organization [6]. According to Domingo and Aguado [63] , there is an absence of relationship with sustainability despite the efforts to create a much better OEE. It is important to note that the purpose of OEE is to assist those who has lack of experience in Lean Manufacturing together with the Sustainable facts in deciding their business flow.

Raja [64] illustrated a positive correlation between OEE and Performance. The OEE tool has been shown to be suitable to be used in different manufacturing environment, with high level of accuracy. Bititci, McLeod [65] stated that the OEE is a platform for Business Performance

improvement. Figures obtained using OEE methods have been demonstrated to show the real effectiveness of the process or line. This exact figure acquired from the OEE tool is proven to be helpful to the management in decision making, especially in terms of the development of the resources for the improvement of the firm’s performance level. According to Pham and Thomas [10] found a significant positive relationship between Fit Manufacturing and OEE.

Pham and Thomas [10] summarizes the relationship among Overall Equipment Effectiveness (OEE), Fit Manufacturing and Measures of for performance. Overall Equipment Effectiveness (OEE) was used to assess if the framework showed precise and measurable improvements in the effectiveness of the equipment within the working group companies. The OEE calculation allows a company to measure the effectiveness of the “Lean” elements used within the framework. The Overall Equipment Effectiveness has been chosen as a mediator because the literature support that there is a positive relationship between Fit Manufacturing and Overall Equipment Effectiveness and also between Overall Equipment Effectiveness and Business Performance. Table 3 presents the summary of the relationships between proposed factors.

7. ConclusionRecent advancements in globalization and technology affect

manufacturing systems. Global competition has necessitated the formulation of both efficient and effective paradigms in response to the global economies for the purpose of improving the overall performance. Mostly, manufacturer focused on the usage of manufacturing strategies namely; Agile Manufacturing, Lean Manufacturing and Sustainability known as Fit Manufacturing. Manufacturing sectors plan to enhance their performance by applying these strategies. Thus the integration of these three manufacturing strategies are crucial to survive in current market competitive environment. Besides, Overall Equipment Effectiveness is known as an approach to ensure the reliability of the production operations which enable firms to satisfy their customers and end users. This paper assessed the relationship between Business Performance and Fit Manufacturing as manufacturing strategy with mediation of Overall Equipment Effectiveness in manufacturing firm.

References10.1016/j.promfg.2018.03.142

originally published in: Procedia Manufacturing, Published by Elsevier

10.1016/j.promfg.2018.03.142 Reproduced under CC BY Licence


Research Article

Smart Hybrid Manufacturing Control Using Cloud Computing and the Internet-of-ThingsJONNRO eRAsmUs [email protected]

PAUl gReFeN iReNe VANDeRFeesteN kONstANtiNOs tRAgANOsSchool of Industrial Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands

AbstractIndustry 4.0 is expected to deliver significant gains

in productivity by assimilating several technological advancements including cloud computing, the Internet-of-Things and smart devices. However, it is unclear how these technologies should be leveraged together to deliver the promised benefits. We present the architecture design of an information system that integrates these technologies to support hybrid manufacturing processes, i.e., processes in which human and robotic workers collaborate. We show how well-structured architecture design is the basis for a modular, complex cyber-physical system that provides horizontal, cross-functional manufacturing process management and

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vertical control of heterogenous work cells. The modular nature allows the extensible cloud support enhancing its accessibility to small and medium enterprises. The information system is designed as part of the HORSE Project: a five-year research and innovation project aimed at making recent technological advancements more accessible to small and medium manufacturing enterprises. The project consortium includes 10 factories to represent the typical problems encountered on the factory floor and provide real-world environments to test and evaluate the developed information system. The resulting information system architecture model is proposed as a reference architecture for a manufacturing operations management system for Industry 4.0. As a reference architecture, it serves two purposes: (1) it frames the scientific inquiry and advancement of information systems for Industry 4.0 and (2) it can be used as a template to develop commercial-grade manufacturing applications for Industry 4.0.

1. introductionIndustry 4.0 is a trend in automation and digitization that

promises significant gains in production output, product customizability and manufacturing flexibility [1]. This new industrial age stems from the coincident rise of cloud computing, the Internet-of-Things (IoT) and smart devices [2]. It is expected that mass customized products will be produced by smart robotics in dynamic processes managed in the cloud [3]. It is even conceptually understood how these technologies should work together to achieve smart manufacturing and deliver on the promises of Industry 4.0 [4,5]. Computation that is not time-critical is relegated to the cloud. The IoT facilitates commands and responses to and from devices and teams of humans and smart robotics perform sophisticated operations. Figure 1 gives an overview of the technologies and their roles in smart manufacturing.

The technologies that underpin Industry 4.0 are increasingly appealing and accessible to manufacturing enterprises. Cloud computing and internet connectivity is prevalent in industrialized countries and robots are becoming increasingly intelligent and affordable [6,7]. Future scenarios are proposed where humans and robots harmoniously collaborate to perform irregular and complicated tasks [8]. Even small and medium enterprises (SMEs) now consider user-friendly automation solutions. For example, a robot that can be programmed by a demonstration is significantly easier and inexpensive to introduce in a relatively low-tech environment [9].

Although more accessible, affordable and available, these technologies are developed independently and remain largely detached. The separate technologies can be acquired and even implemented, but it is unclear how to unlock the promised benefit of an integrated solution. Monostori [10] argues that these new technologies threaten the traditional automation hierarchy, which further distorts our understanding of the manufacturing system and its various elements. Thus, the adoption of smart manufacturing technology is hindered by utilization and integration instead of acquisition. In fact, the absence of out-of-the-box solutions that combine the different technologies is considered a primary impediment on the path towards smart manufacturing [6].

The symptoms of the problem manifest most fervently on a factory floor with humans and robots. The activities of humans and robots are controlled differently. Humans receive written, oral, or visual instructions while machines are compelled to action via their control systems. These control regimes function independently [11], which makes it difficult to transfer tasks between humans and robots even if their capabilities would allow this [12,13]. Furthermore, robot control is often poorly integrated with cross-functional processes management [14]. Robot control follows a Machines 2018, 6, 62 2 of 25

Machines 2018, 6, x FOR PEER REVIEW 2 of 25

Figure 1. Roles of selected technologies in smart manufacturing.

The technologies that underpin Industry 4.0 are increasingly appealing and accessible to manufacturing enterprises. Cloud computing and internet connectivity is prevalent in industrialized countries and robots are becoming increasingly intelligent and affordable [6,7]. Future scenarios are proposed where humans and robots harmoniously collaborate to perform irregular and complicated tasks [8]. Even small and medium enterprises (SMEs) now consider user-friendly automation solutions. For example, a robot that can be programmed by a demonstration is significantly easier and inexpensive to introduce in a relatively low-tech environment [9].

Although more accessible, affordable, and available, these technologies are developed independently and remain largely detached. The separate technologies can be acquired and even implemented, but it is unclear how to unlock the promised benefit of an integrated solution. Monostori [10] argues that these new technologies threaten the traditional automation hierarchy, which further distorts our understanding of the manufacturing system and its various elements. Thus, the adoption of smart manufacturing technology is hindered by utilization and integration instead of acquisition. In fact, the absence of out-of-the-box solutions that combine the different technologies is considered a primary impediment on the path towards smart manufacturing [6].

The symptoms of the problem manifest most fervently on a factory floor with humans and robots. The activities of humans and robots are controlled differently. Humans receive written, oral, or visual instructions while machines are compelled to action via their control systems. These control regimes function independently [11], which makes it difficult to transfer tasks between humans and robots even if their capabilities would allow this [12,13]. Furthermore, robot control is often poorly integrated with cross-functional processes management [14]. Robot control follows a vertical orientation focused on the operations within a work cell. Process management follow a horizontal orientation focused on the operations across work cells and in the context of enterprise information processing. Thus, current robot control does not support simple reassignment of robots to different work cells. The most apparent symptom is the increased safety hazards introduced by automation. Robots must be equipped with extensive safety precautions to allow close collaboration with humans (the accident in a car factory [15] is well known in the domain). To compensate, human and robot working spaces are usually physically separated. These symptoms and concerns hamper mainstream adoption of human-robot collaboration technology [13,16].

The contribution of this paper is an architecture model of an information system that utilizes modern manufacturing technologies to deliver seamless integration between human and automated activities. The model is proposed as a reference architecture for a manufacturing operations management system for Industry 4.0. As reference architecture, it serves two purposes: (1) it frames the scientific inquiry and advancement of information systems for Industry 4.0 and (2) it can be used as a template or at least a starting point to develop commercial-grade manufacturing applications for Industry 4.0.

The information system model is developed as part of the HORSE Project, which is a European research and innovation project in the Horizon 2020 program [17]. The project brings together


The technologies that underpin Industry 4.0 are increasingly appealing and accessible tomanufacturing enterprises. Cloud computing and internet connectivity is prevalent in industrializedcountries and robots are becoming increasingly intelligent and affordable [6,7]. Future scenarios areproposed where humans and robots harmoniously collaborate to perform irregular and complicatedtasks [8]. Even small and medium enterprises (SMEs) now consider user-friendly automation solutions.For example, a robot that can be programmed by a demonstration is significantly easier and inexpensiveto introduce in a relatively low-tech environment [9].

Although more accessible, affordable, and available, these technologies are developedindependently and remain largely detached. The separate technologies can be acquired and evenimplemented, but it is unclear how to unlock the promised benefit of an integrated solution.Monostori [10] argues that these new technologies threaten the traditional automation hierarchy,which further distorts our understanding of the manufacturing system and its various elements. Thus,the adoption of smart manufacturing technology is hindered by utilization and integration instead ofacquisition. In fact, the absence of out-of-the-box solutions that combine the different technologies isconsidered a primary impediment on the path towards smart manufacturing [6].

The symptoms of the problem manifest most fervently on a factory floor with humans and robots.The activities of humans and robots are controlled differently. Humans receive written, oral, or visualinstructions while machines are compelled to action via their control systems. These control regimesfunction independently [11], which makes it difficult to transfer tasks between humans and robotseven if their capabilities would allow this [12,13]. Furthermore, robot control is often poorly integratedwith cross-functional processes management [14]. Robot control follows a vertical orientation focusedon the operations within a work cell. Process management follow a horizontal orientation focused onthe operations across work cells and in the context of enterprise information processing. Thus, currentrobot control does not support simple reassignment of robots to different work cells. The most apparentsymptom is the increased safety hazards introduced by automation. Robots must be equipped withextensive safety precautions to allow close collaboration with humans (the accident in a car factory [15]is well known in the domain). To compensate, human and robot working spaces are usually physicallyseparated. These symptoms and concerns hamper mainstream adoption of human-robot collaborationtechnology [13,16].

The contribution of this paper is an architecture model of an information system that utilizesmodern manufacturing technologies to deliver seamless integration between human and automatedactivities. The model is proposed as a reference architecture for a manufacturing operationsmanagement system for Industry 4.0. As reference architecture, it serves two purposes: (1) it framesthe scientific inquiry and advancement of information systems for Industry 4.0 and (2) it can be usedas a template or at least a starting point to develop commercial-grade manufacturing applications forIndustry 4.0.



vertical orientation focused on the operations within a work cell. Process management follow a horizontal orientation focused on the operations across work cells and in the context of enterprise information processing. Thus, current robot control does not support simple reassignment of robots to different work cells. The most apparent symptom is the increased safety hazards introduced by automation. Robots must be equipped with extensive safety precautions to allow close collaboration with humans (the accident in a car factory [15] is well known in the domain). To compensate, human and robot working spaces are usually physically separated. These symptoms and concerns hamper mainstream adoption of human-robot collaboration technology [13,16].

The contribution of this article is an architecture model of an information system that utilizes modern manufacturing technologies to deliver seamless integration between human and automated activities. The model is proposed as a reference architecture for a manufacturing operations management system for Industry 4.0. As reference architecture, it serves two purposes: (1) it frames the scientific inquiry and advancement of information systems for Industry 4.0 and (2) it can be used as a template or at least a starting point to develop commercial-grade manufacturing applications for Industry 4.0.

The information system model is developed as part of the HORSE Project, which is a European research and innovation project in the Horizon 2020 program [17]. The project brings together 22 organizations including research institutions, technology vendors and manufacturing enterprises. The primary objective of the project is to make advanced manufacturing technology more accessible to SMEs. These technologies are packaged in a modular, integrated “HORSE System” and include human intention detection,

robot force control, teaching-by-demonstration, augmented reality, dynamic allocation of tasks to humans and robots and manufacturing process management. The intended SME context of the HORSE System often implies limited availability of on-site information technology resources. Therefore, the use of cloud services can be an important enabling factor in the application of HORSE concepts and technology in an SME.

This article starts with a detailed explanation of the research approach derived from the Reference Architecture for Industry 4.0 (RAMI4.0) and the Kruchten 4 + 1 software engineering framework. In Section 3, the logical system architecture is presented at three levels of aggregation to give insight into the function and structure of the HORSE System. Thereafter, Section 4 presents the physical architecture of the HORSE System in two stages: first, determining which parts of the system can be located ‘in the cloud’ and, second, presenting the HORSE System as an IoT application. The consideration of cloud-support expands on earlier work of Grefen et al. [18]. In Section 5, three real-world scenarios are presented as proof of concept of the HORSE System. Section 6 considers the possibilities of cloud-based management of inter-organizational manufacturing processes and supply chains. Lastly, conclusions and findings are discussed in Section 7.

2. Research approachThe information system architecture presented in this paper is

the result of rigorous design and science research. The purpose of design science research is to generate prescriptive knowledge that can be used to solve practical problems [19]. As such, problems from the industry are studied to ensure practical relevance and an artefact is created to help solve similar problems. The artefact in Machines 2018, 6, 62 4 of 25


Figure 2. Research framework used in the HORSE Project.

The design science research column of Figure 2 consists of development and evaluation. It produces validated reference architecture. The reference architecture is presented in a logical and physical view in Sections 3 and 4, respectively, according to the Kruchten 4 + 1 framework. The remaining development and process views of the Kruchten framework are omitted in the interest of brevity. The evaluation is articulated in scenarios that demonstrate application of the information system. For relevance, three pilot cases act as the organizations that have the business need while three core concepts of Industry 4.0. provide the technology push driving the artifact development. The artifact is applied in the applicable environments of the three pilot cases, as articulated in the scenarios. For rigor, design principles are derived from industry standards and the design process is based on the Kruchten 4 + 1 framework. Lastly, the contribution toward the knowledge base is a validated information system model.

2.2. Design Principles

RAMI4.0 was established and defined in DIN SPEC 91345:2016-04 [23] to give some structure to the rapidly developing and changing technologies in manufacturing. According to the standard, “the fundamental purpose of Industrie 4.0 is to facilitate cooperation and collaboration between technical objects, which means they have to be virtually represented and connected.” The reference architecture brings together the business, life cycle, and hierarchical views of an asset by relating the concepts on three orthogonal dimensions [24]:

• The layers dimension is more formally referred to as the architecture axis. This axis “represents the information that is relevant to the role of an asset.” It covers the business-to-technology spectrum by relating different aspects of an asset to layers of the enterprise architecture.

• The life cycle and value stream dimension “represents the lifetime of an asset and the value-added process.” This axis distinguishes between the type and instance of the factory and its elements. For example, the digital design of a product and its instantiation as a manufactured product.

• The hierarchy levels dimension is used to “assign functional models to specific levels” of an enterprise. This axis uses aggregation to establish enterprise levels that range from the connected world (i.e., networks of manufacturing organizations in their eco-systems) via stations (manufacturing work cells) to devices and products.

The life cycle and value stream dimension of RAMI4.0 distinguishes between the type and instance of a product and its value-added processes [23]. Type can be equated to the design of the product and processes while instance is the execution of processes to produce a product. This separation emphasizes the importance of consistency across the product life cycle.


The design science research column of Figure 2 consists of development and evaluation.It produces validated reference architecture. The reference architecture is presented in a logicaland physical view in Sections 3 and 4, respectively, according to the Kruchten 4 + 1 framework.The remaining development and process views of the Kruchten framework are omitted in the interestof brevity. The evaluation is articulated in scenarios that demonstrate application of the informationsystem. For relevance, three pilot cases act as the organizations that have the business need whilethree core concepts of Industry 4.0. provide the technology push driving the artifact development.The artifact is applied in the applicable environments of the three pilot cases, as articulated in thescenarios. For rigor, design principles are derived from industry standards and the design processis based on the Kruchten 4 + 1 framework. Lastly, the contribution toward the knowledge base is avalidated information system model.

2.2. Design Principles

RAMI4.0 was established and defined in DIN SPEC 91345:2016-04 [23] to give some structureto the rapidly developing and changing technologies in manufacturing. According to the standard,“the fundamental purpose of Industrie 4.0 is to facilitate cooperation and collaboration betweentechnical objects, which means they have to be virtually represented and connected.” The referencearchitecture brings together the business, life cycle, and hierarchical views of an asset by relating theconcepts on three orthogonal dimensions [24]:

• The layers dimension is more formally referred to as the architecture axis. This axis “represents theinformation that is relevant to the role of an asset.” It covers the business-to-technology spectrumby relating different aspects of an asset to layers of the enterprise architecture.

• The life cycle and value stream dimension “represents the lifetime of an asset and the value-addedprocess.” This axis distinguishes between the type and instance of the factory and its elements.For example, the digital design of a product and its instantiation as a manufactured product.

• The hierarchy levels dimension is used to “assign functional models to specific levels” ofan enterprise. This axis uses aggregation to establish enterprise levels that range from theconnected world (i.e., networks of manufacturing organizations in their eco-systems) via stations(manufacturing work cells) to devices and products.

The life cycle and value stream dimension of RAMI4.0 distinguishes between the type and instanceof a product and its value-added processes [23]. Type can be equated to the design of the productand processes while instance is the execution of processes to produce a product. This separationemphasizes the importance of consistency across the product life cycle.



this paper is in the form of a conceptual design of an information system [20] to serve as reference architecture to develop and build an information system for the management of smart manufacturing operations.

To structure the research, the design science research framework of Hevner et al. [21] is adopted and discussed in Section 2.1. More importantly, the design approach is thoroughly reported to ensure repeatability. A similar result should be achieved given the same problem and context. The design approach is documented in the form of principles and process. The principles that guide the design are based on information systems architecture theory and discussed in Section 2.2. The research process is based on the widely adopted Kruchten 4 + 1 framework for software engineering [22], which is explained in Section 2.3.

2.1. Research frameworkThe HORSE System must be both practical and relevant for the

typical challenges faced in SMEs. To achieve this goal, the design science research framework of Hevner et al. [21] is adopted. The framework emphasizes practical relevance by advocating for consideration of business need during the development and evaluation of the results in a realistic environment. The research framework, based on the Hevner et al. framework [21], is shown in Figure 2.

The design science research column of Figure 2 consists of development and evaluation. It produces validated reference architecture. The reference architecture is presented in a logical and physical view in Section 3 and Section 4, respectively, according to the Kruchten 4 + 1 framework. The remaining development and process views of the Kruchten framework are omitted in the interest of brevity. The evaluation is articulated in scenarios that demonstrate application of the information system. For relevance, three pilot cases act as the organizations that have the business need while three core concepts of Industry 4.0. provide the technology push driving the artifact development. The artifact is applied in the applicable environments of the three pilot cases, as articulated in the scenarios. For rigor, design principles are derived from industry standards and the design process is based on the Kruchten 4 + 1 framework. Lastly, the contribution toward the knowledge base is a validated information system model.

2.2. Design PrinciplesRAMI4.0 was established and defined in DIN SPEC 91345:2016-

04 [23] to give some structure to the rapidly developing and changing technologies in manufacturing. According to the standard, “the fundamental purpose of Industrie 4.0 is to facilitate cooperation and collaboration between technical objects, which means they have to be virtually represented and connected.” The reference architecture brings together the business, life cycle and hierarchical views of an asset by relating the concepts on three orthogonal dimensions [24]:l The layers dimension is more formally referred to as the

architecture axis. This axis “represents the information that

Machines 2018, 6, 62 5 of 25

The hierarchy levels dimension of RAMI4.0 references the international standard IEC62264:2013 [25].More specifically, the physical hierarchy of IEC62264:2013 is referenced. The physical hierarchy establishesa naming convention for the sections in the factory. Enterprise is the highest level of the hierarchy and workcell is the lowest for a discreet manufacturing facility. Figure 3 shows an illustrative physical hierarchy of ahypothetical manufacturing enterprise.


The hierarchy levels dimension of RAMI4.0 references the international standard IEC62264:2013 [25]. More specifically, the physical hierarchy of IEC62264:2013 is referenced. The physical hierarchy establishes a naming convention for the sections in the factory. Enterprise is the highest level of the hierarchy and work cell is the lowest for a discreet manufacturing facility. Figure 3 shows an illustrative physical hierarchy of a hypothetical manufacturing enterprise.

Figure 3. Illustrative physical hierarchy of a manufacturing enterprise showing the different control regimes applied at different levels of the enterprise.

The separation of concerns is widely used to manage complexity in system design [26–28]. The technique allows the designer to consider some aspect of the system separately from the rest of the system, which decreases local complexity. We apply the technique to create two separations in the HORSE System derived from RAMI4.0 and apply these as design principles below.

1. Separation between design-time and run-time system functions based on the life cycle and value stream dimension.

2. Separation between horizontal and vertical control based on the hierarchy levels dimension.

The first separation between design-time and run-time is applied to both the manufacturing processes and the participants in those processes. Manufacturing processes and agents (the participants in the processes) are identified and described during the design period and then instantiated and activated to perform activities during the run-time.

The second separation between the horizontal and vertical control is derived from the physical hierarchy, which is shown in Figure 3. Horizontal control is concerned with the sequence of activities performed by several participants to transform materials into products, i.e., management of the manufacturing processes across multiple work cells. Vertical control is concerned with the actions performed by a single participant or a team of participants within a single work cell of the factory. The different control regimes are also indicated in Figure 3. Thus, horizontal and vertical control is separated to account for different control regimes. Horizontal control is concerned with the coordination of activities that may be spatially dispersed while vertical control is concerned with the sub-second synchronization of actions.

2.3. Design Process

The HORSE System includes several disparate technologies and stresses the need for systematic development and sound architectural principles. The Kruchten 4 + 1 framework [22] is used to deal with the various views of stakeholders and their sequencing in time. The framework, as shown in

Figure 3. Illustrative physical hierarchy of a manufacturing enterprise showing the different controlregimes applied at different levels of the enterprise.

The separation of concerns is widely used to manage complexity in system design [26–28].The technique allows the designer to consider some aspect of the system separately from the rest ofthe system, which decreases local complexity. We apply the technique to create two separations in theHORSE System derived from RAMI4.0 and apply these as design principles below.

1. Separation between design-time and run-time system functions based on the life cycle and valuestream dimension.

2. Separation between horizontal and vertical control based on the hierarchy levels dimension.

The first separation between design-time and run-time is applied to both the manufacturingprocesses and the participants in those processes. Manufacturing processes and agents (the participantsin the processes) are identified and described during the design period and then instantiated andactivated to perform activities during the run-time.

The second separation between the horizontal and vertical control is derived from the physicalhierarchy, which is shown in Figure 3. Horizontal control is concerned with the sequence of activitiesperformed by several participants to transform materials into products, i.e., management of themanufacturing processes across multiple work cells. Vertical control is concerned with the actionsperformed by a single participant or a team of participants within a single work cell of the factory.The different control regimes are also indicated in Figure 3. Thus, horizontal and vertical controlis separated to account for different control regimes. Horizontal control is concerned with thecoordination of activities that may be spatially dispersed while vertical control is concerned with thesub-second synchronization of actions.

2.3. Design Process

The HORSE System includes several disparate technologies and stresses the need for systematicdevelopment and sound architectural principles. The Kruchten 4 + 1 framework [22] is used to deal with

is relevant to the role of an asset.” It covers the business-to-technology spectrum by relating different aspects of an asset to layers of the enterprise architecture.l The life cycle and value stream dimension “represents the lifetime

of an asset and the value-added process.” This axis distinguishes between the type and instance of the factory and its elements. For example, the digital design of a product and its instantiation as a manufactured product.l The hierarchy levels dimension is used to “assign functional

models to specific levels” of an enterprise. This axis uses aggregation to establish enterprise levels that range from the connected world (i.e., networks of manufacturing organizations in their eco-systems) via stations (manufacturing work cells) to devices and products.

The life cycle and value stream dimension of RAMI4.0 distinguishes between the type and instance of a product and its value-added processes [23]. Type can be equated to the design of the product and processes while instance is the execution of processes to produce a product. This separation emphasizes the importance of consistency across the product life cycle. The hierarchy levels dimension of RAMI4.0 references the international standard IEC62264:2013 [25]. More specifically, the physical hierarchy of IEC62264:2013 is referenced. The physical hierarchy establishes a naming convention for the sections in the factory. Enterprise is the highest level of the hierarchy and work cell is the lowest for a discreet manufacturing facility. Figure 3 shows an illustrative physical hierarchy of a hypothetical manufacturing enterprise.

The separation of concerns is widely used to manage complexity in system design [26,27,28]. The technique allows the designer to consider some aspect of the system separately from

Figure 3. Illustrative physical hierarchy of a manufacturing enterprise showing the different control regimes applied at different levels of the enterprise.


the rest of the system, which decreases local complexity. We apply the technique to create two separations in the HORSE System derived from RAMI4.0 and apply these as design principles below.1. Separation between design-time and run-time system functions

based on the life cycle and value stream dimension.2. Separation between horizontal and vertical control based on

the hierarchy levels dimension.The first separation between design-time and run-time is

applied to both the manufacturing processes and the participants in those processes. Manufacturing processes and agents (the participants in the processes) are identified and described during the design period and then instantiated and activated to perform activities during the run-time.

The second separation between the horizontal and vertical control is derived from the physical hierarchy, which is shown in Figure 3. Horizontal control is concerned with the sequence of activities performed by several participants to transform materials into products, i.e., management of the manufacturing processes across multiple work cells. Vertical control is concerned with the actions performed by a single participant or a team of participants within a single work cell of the factory. The different control regimes are also indicated in Figure 3. Thus, horizontal and vertical control is separated to account for different control regimes. Horizontal control is concerned with the coordination of activities that may be spatially dispersed while vertical control is concerned with the sub-second synchronization of actions.

2.3. Design processThe HORSE System includes several disparate technologies

and stresses the need for systematic development and sound architectural principles. The Kruchten 4 + 1 framework [22] is used to deal with the various views of stakeholders and their sequencing in time. The framework, as shown in Figure 4, employs phased development resulting in four views with their respective primary stakeholders.1. The logical view is concerned with what the system should do. It

specifies the functionality of the system in the form of modules and relationships between modules. The main stakeholders are the end users of the system.

2. The development view is concerned with good software management. It specifies how the software system is organized in a developmental environment. The main stakeholders are the software engineers.

3. The process view is concerned with the performance and scalability of the system. It specifies the concurrency and synchronization of the system modules. The main stakeholders are the integrators of the system.

4. The physical view is concerned with realization and deployment of the system. It specifies the allocation of hardware resources to software modules. The main stakeholders are the engineers who are responsible for installing and maintaining the system.

Separate views with different stakeholders can result in a divergence of ideas and an understanding about the system. To avoid

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the various views of stakeholders and their sequencing in time. The framework, as shown in Figure 4,employs phased development resulting in four views with their respective primary stakeholders.

1. The logical view is concerned with what the system should do. It specifies the functionality ofthe system in the form of modules and relationships between modules. The main stakeholdersare the end users of the system.

2. The development view is concerned with good software management. It specifies how thesoftware system is organized in a developmental environment. The main stakeholders are thesoftware engineers.

3. The process view is concerned with the performance and scalability of the system. It specifiesthe concurrency and synchronization of the system modules. The main stakeholders are theintegrators of the system.

4. The physical view is concerned with realization and deployment of the system. It specifies theallocation of hardware resources to software modules. The main stakeholders are the engineerswho are responsible for installing and maintaining the system.

Separate views with different stakeholders can result in a divergence of ideas and anunderstanding about the system. To avoid such a divergence, the four views are reconciled by afifth concept:

5. Scenarios represent user cases of the system that demonstrate system functionality andperformance. The scenarios should be specific and practical enough to facilitate discussionabout the expected operation of the system in its intended context.


Figure 4, employs phased development resulting in four views with their respective primary stakeholders.

1. The logical view is concerned with what the system should do. It specifies the functionality of the system in the form of modules and relationships between modules. The main stakeholders are the end users of the system.

2. The development view is concerned with good software management. It specifies how the software system is organized in a developmental environment. The main stakeholders are the software engineers.

3. The process view is concerned with the performance and scalability of the system. It specifies the concurrency and synchronization of the system modules. The main stakeholders are the integrators of the system.

4. The physical view is concerned with realization and deployment of the system. It specifies the allocation of hardware resources to software modules. The main stakeholders are the engineers who are responsible for installing and maintaining the system.

Separate views with different stakeholders can result in a divergence of ideas and an understanding about the system. To avoid such a divergence, the four views are reconciled by a fifth concept:

5. Scenarios represent user cases of the system that demonstrate system functionality and performance. The scenarios should be specific and practical enough to facilitate discussion about the expected operation of the system in its intended context.

Figure 4. Kruchten 4 + 1 framework [22].

The Kruchten 4 + 1 framework is used to sequence the development of the HORSE System. First, the logical architecture is used to specify the functional structure of the system without reference to specific implementation techniques, technologies, or deployment. The main input into this design is a clear description of the scenarios and the problems faced in those scenarios. The output of this phase is a logical architecture design with five aggregation levels along with one context level [29]. We discuss four of these six levels in Section 3 of this paper. The development and process views are concerned with a good software engineering practice and are, thus, omitted from this paper.

In the physical view, it is determined how and where the software resulting from the previous two views will run. The exact software and hardware may be different for each deployment of the HORSE System, but the type of technology remains the same. The exact software and hardware used in the HORSE Project is specified in this case study [30]. This research paper is more concerned with the separation between cloud-based and on-premise deployments. Thus, the cloud-supported parts of the HORSE System are identified, which leads to a clear division between the modules running ‘in the cloud’ and those running on premise. This division is discussed and justified in Section 4 of this paper based on performance and security considerations. The scenarios are located within the three industrial pilot cases of the project and discussed in Section 5.


The Kruchten 4 + 1 framework is used to sequence the development of the HORSE System. First,the logical architecture is used to specify the functional structure of the system without reference tospecific implementation techniques, technologies, or deployment. The main input into this design is aclear description of the scenarios and the problems faced in those scenarios. The output of this phase isa logical architecture design with five aggregation levels along with one context level [29]. We discussfour of these six levels in Section 3 of this paper. The development and process views are concernedwith a good software engineering practice and are, thus, omitted from this paper.

In the physical view, it is determined how and where the software resulting from the previoustwo views will run. The exact software and hardware may be different for each deployment of theHORSE System, but the type of technology remains the same. The exact software and hardware usedin the HORSE Project is specified in this case study [30]. This research paper is more concerned withthe separation between cloud-based and on-premise deployments. Thus, the cloud-supported parts ofthe HORSE System are identified, which leads to a clear division between the modules running ‘inthe cloud’ and those running on premise. This division is discussed and justified in Section 4 of thispaper based on performance and security considerations. The scenarios are located within the threeindustrial pilot cases of the project and discussed in Section 5.


such a divergence, the four views are reconciled by a fifth concept:5. Scenarios represent user cases of the system that demonstrate

system functionality and performance. The scenarios should be specific and practical enough to facilitate discussion about the expected operation of the system in its intended context.

The Kruchten 4 + 1 framework is used to sequence the development of the HORSE System. First, the logical architecture is used to specify the functional structure of the system without reference to specific implementation techniques, technologies, or deployment. The main input into this design is a clear description of the scenarios and the problems faced in those scenarios. The output of this phase is a logical architecture design with five aggregation levels along with one context level [29]. We discuss four of these six levels in Section 3 of this paper. The development and process views are concerned with a good software engineering practice and are, thus, omitted from this article.

In the physical view, it is determined how and where the software resulting from the previous two views will run. The exact software and hardware may be different for each deployment of the HORSE System, but the type of technology remains the same. The exact software and hardware used in the HORSE Project is specified in this case study [30]. This research paper is more concerned with the separation between cloud-based and on-premise deployments. Thus, the cloud-supported parts of the HORSE System are identified, which leads to a clear division between the modules running ‘in the cloud’ and those running on premise. This division is discussed and justified in Section 4 of this paper based on performance and security considerations. The scenarios are located within the three industrial pilot cases of the project and discussed in Section 5.

3. logical view of the HORse systemThe logical system architecture of the HORSE System is a

hierarchical, multi-level view. The complete design comprises six


levels of aggregation, but only the first four levels are discussed in this paper in the interest of brevity. The full system design report [29] elaborates on the remaining two levels of aggregation, but it limits the discussion to system structure and design decisions. Conversely, this research paper emphasizes scientific rigor and presents the HORSE System in the context of Industry 4.0. The four levels discussed in this paper are labeled Context Architecture and Architecture Levels 1, 2 and 3. The Level 2 architecture is divided into the design time half and the execution time half. Only the execution time subsystem on the local layer is discussed because this subsystem is the most complicated and is responsible for the main interface between software and hardware on the factory floor.

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3. Logical View of the HORSE System

The logical system architecture of the HORSE System is a hierarchical, multi-level view.The complete design comprises six levels of aggregation, but only the first four levels are discussed inthis paper in the interest of brevity. The full system design report [29] elaborates on the remaining twolevels of aggregation, but it limits the discussion to system structure and design decisions. Conversely,this research paper emphasizes scientific rigor and presents the HORSE System in the context ofIndustry 4.0. The four levels discussed in this paper are labeled Context Architecture and ArchitectureLevels 1, 2, and 3. The Level 2 architecture is divided into the design time half and the execution timehalf. Only the execution time subsystem on the local layer is discussed because this subsystem is themost complicated and is responsible for the main interface between software and hardware on thefactory floor.

3.1. HORSE Context Architecture

The HORSE System is primarily concerned with highly configurable, flexible manufacturingprocesses involving human and robotic participants. All three pilot cases in the HORSE Project, whichare discussed in Section 5, feature processes with cooperation between humans and robots. Therefore,the HORSE System interfaces with a variety of humans, robots, and sensors that participate in themanufacturing processes. These processes do not exist in isolation. Any manufacturing enterprisealso performs other business processes such as purchasing, product development, sales, and customersupport. Consequently, the HORSE system must be contextualized in the existing hardware andsoftware systems of the enterprise.

An illustrative enterprise architecture is shown in Figure 5 with the HORSE System positionedas a central hub amongst typical systems found in factories [14]. Business management systems arerepresented with an enterprise resource planning system (ERP), which is a manufacturing executionsystem (MES) and a product life-cycle management system (PLMS) at the top of Figure 5. Toward thelower end of Figure 5, the HORSE System is connected to a robot, human, and sensor, which promoteshuman-robot co-existence. In practice, the situation is typically more complicated, but this simplifiedview shows the context of the HORSE System.


3. Logical View of the HORSE System

The logical system architecture of the HORSE System is a hierarchical, multi-level view. The complete design comprises six levels of aggregation, but only the first four levels are discussed in this paper in the interest of brevity. The full system design report [29] elaborates on the remaining two levels of aggregation, but it limits the discussion to system structure and design decisions. Conversely, this research paper emphasizes scientific rigor and presents the HORSE System in the context of Industry 4.0. The four levels discussed in this paper are labeled Context Architecture and Architecture Levels 1, 2, and 3. The Level 2 architecture is divided into the design time half and the execution time half. Only the execution time subsystem on the local layer is discussed because this subsystem is the most complicated and is responsible for the main interface between software and hardware on the factory floor.

3.1. HORSE Context Architecture

The HORSE System is primarily concerned with highly configurable, flexible manufacturing processes involving human and robotic participants. All three pilot cases in the HORSE Project, which are discussed in Section 5, feature processes with cooperation between humans and robots. Therefore, the HORSE System interfaces with a variety of humans, robots, and sensors that participate in the manufacturing processes. These processes do not exist in isolation. Any manufacturing enterprise also performs other business processes such as purchasing, product development, sales, and customer support. Consequently, the HORSE system must be contextualized in the existing hardware and software systems of the enterprise.

An illustrative enterprise architecture is shown in Figure 5 with the HORSE System positioned as a central hub amongst typical systems found in factories [14]. Business management systems are represented with an enterprise resource planning system (ERP), which is a manufacturing execution system (MES) and a product life-cycle management system (PLMS) at the top of Figure 5. Toward the lower end of Figure 5, the HORSE System is connected to a robot, human, and sensor, which promotes human-robot co-existence. In practice, the situation is typically more complicated, but this simplified view shows the context of the HORSE System.

Figure 5. Context of the HORSE System simplified to only show typical systems. Figure 5. Context of the HORSE System simplified to only show typical systems.

3.1. HoRSE context architectureThe HORSE System is primarily concerned with highly

configurable, flexible manufacturing processes involving human and robotic participants. All three pilot cases in the HORSE Project, which are discussed in Section 5, feature processes with cooperation between humans and robots. Therefore, the HORSE System interfaces with a variety of humans, robots and sensors that participate in the manufacturing processes. These processes do not exist in isolation. Any manufacturing enterprise also performs other business processes such as purchasing, product development, sales and customer support. Consequently, the HORSE system must be contextualized in the existing hardware and software systems of the enterprise.

An illustrative enterprise architecture is shown in Figure 5 with the HORSE System positioned as a central hub amongst typical systems found in factories [14]. Business management systems are represented with an enterprise resource planning system (ERP), which is a manufacturing execution system (MES) and a product life-cycle management system (PLMS) at the top of Figure 5. Toward the lower end of Figure 5, the HORSE System is connected to a robot, human and sensor, which promotes human-robot co-existence. In practice, the situation is typically more complicated, but this simplified view shows the context of the HORSE System.

3.2. HoRSE System Architecture Level 1Level 1 of the architecture model unpacks the HORSE System

box of Figure 5 to refine its contents. This refinement applies the two design principles described in Section 2.2: separation of design-time and run-time system functions and separation of horizontal and vertical control. The first design principle calls for system functionality dedicated to design and control, respectively. This consideration gives the architecture a columned style with a design-time column and an execution-time column. The second

Figure 5. Context of the HORSE System simplified to only show typical systems.

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3.2. HORSE System Architecture Level 1

Level 1 of the architecture model unpacks the HORSE System box of Figure 5 to refine its contents.This refinement applies the two design principles described in Section 2.2: separation of design-timeand run-time system functions and separation of horizontal and vertical control. The first designprinciple calls for system functionality dedicated to design and control, respectively. This considerationgives the architecture a columned style with a design-time column and an execution-time column.The second design principle calls for separation between functionality that is aimed at the support ofactivities within a single manufacturing work cell and functionality that is aimed at synchronizingactivities across multiple work cells. This consideration gives the architecture a layered style withglobal and local control layers. The global layer interfaces with business management systems at thetop of Figure 5 while the local layer interfaces with humans and robot controllers, which is shown atthe bottom of Figure 5.

Applying the two design principles results in a system architecture with four sub-systems andtwo data stores, as shown in Figure 6. This is a columned architecture embedded in a layeredarchitecture [31]. The design-time and execution-time columns are connected via databases thatcontain specifications of manufacturing activities and the participants involved in the activities.During design-time, the global and local layers are indirectly connected via the data stores since thesesubsystems are used to create and edit the manufacturing activities and actors. However, for executiontime, HORSE Exec Global and HORSE Exec Local are directly coupled to pass messages directlybetween global and local control. Note that the design-time subsystem on the local layer is labeled asconfiguration instead of design, since this fits better with the configuration of equipment and toolswithin work cells.


3.2. HORSE System Architecture Level 1

Level 1 of the architecture model unpacks the HORSE System box of Figure 5 to refine its contents. This refinement applies the two design principles described in Section 2.2: separation of design-time and run-time system functions and separation of horizontal and vertical control. The first design principle calls for system functionality dedicated to design and control, respectively. This consideration gives the architecture a columned style with a design-time column and an execution-time column. The second design principle calls for separation between functionality that is aimed at the support of activities within a single manufacturing work cell and functionality that is aimed at synchronizing activities across multiple work cells. This consideration gives the architecture a layered style with global and local control layers. The global layer interfaces with business management systems at the top of Figure 5 while the local layer interfaces with humans and robot controllers, which is shown at the bottom of Figure 5.

Applying the two design principles results in a system architecture with four sub-systems and two data stores, as shown in Figure 6. This is a columned architecture embedded in a layered architecture [31]. The design-time and execution-time columns are connected via databases that contain specifications of manufacturing activities and the participants involved in the activities. During design-time, the global and local layers are indirectly connected via the data stores since these subsystems are used to create and edit the manufacturing activities and actors. However, for execution time, HORSE Exec Global and HORSE Exec Local are directly coupled to pass messages directly between global and local control. Note that the design-time subsystem on the local layer is labeled as configuration instead of design, since this fits better with the configuration of equipment and tools within work cells.

Figure 6. HORSE architecture, aggregation level 1.

In the two subsections below, the HORSE system architecture is further elaborated. The design period and execution time columns of Figure 6 are discussed separately to manage the inherent complexity of the system.

3.3. HORSE Design Time Architecture Level 2

This section elaborates on the design-time column of the HORSE System, which is shown in Figure 7. On the global layer, the HORSE Design Global subsystem provides functionality to design manufacturing processes across multiple work cells populated by multiple, possibly heterogenous, actors. As such, the subsystem contains two modules dedicated to the design of processes and agents (terminology used to denote any independent process participant), respectively. For the Process Design module, existing business process management (BPM) technology is used as the basis and extended to accommodate the physical nature of manufacturing processes as opposed to the administrative

Figure 6. HORSE architecture, aggregation level 1.

In the two subsections below, the HORSE system architecture is further elaborated. The designperiod and execution time columns of Figure 6 are discussed separately to manage the inherentcomplexity of the system.

3.3. HORSE Design Time Architecture Level 2

This section elaborates on the design-time column of the HORSE System, which is shown inFigure 7. On the global layer, the HORSE Design Global subsystem provides functionality to designmanufacturing processes across multiple work cells populated by multiple, possibly heterogenous,actors. As such, the subsystem contains two modules dedicated to the design of processes and agents(terminology used to denote any independent process participant), respectively. For the Process Designmodule, existing business process management (BPM) technology is used as the basis and extended

Figure 6. HORSE architecture, aggregation level 1


design principle calls for separation between functionality that is aimed at the support of activities within a single manufacturing work cell and functionality that is aimed at synchronizing activities across multiple work cells. This consideration gives the architecture a layered style with global and local control layers. The global layer interfaces with business management systems at the top of Figure 5 while the local layer interfaces with humans and robot controllers, which is shown at the bottom of Figure 5.

Applying the two design principles results in a system architecture with four sub-systems and two data stores, as shown in Figure 6. This is a columned architecture embedded in a layered architecture [31]. The design-time and execution-time columns are connected via databases that contain specifications of manufacturing activities and the participants involved in the activities. During design-time, the global and local layers are indirectly connected via the data stores since these subsystems are used to create and edit the manufacturing activities and actors. However, for execution time, HORSE Exec Global and HORSE Exec Local are directly coupled to pass messages directly between global and local control. Note that the design-time subsystem on the local layer is labeled as configuration instead of design, since this fits better with the configuration of equipment and tools within work cells.

In the two subsections below, the HORSE system architecture is further elaborated. The design period and execution time columns of Figure 6 are discussed separately to manage the inherent complexity of the system.

3.3. HoRSE Design Time Architecture Level 2This section elaborates on the design-time column of the HORSE System,

which is shown in Figure 7. On the global layer, the HORSE Design Global subsystem provides functionality to design manufacturing processes across multiple work cells populated by multiple, possibly heterogenous, actors. As such, the subsystem contains two modules dedicated to the design of processes and agents (terminology used to denote any independent process participant), respectively. For the Process Design module, existing business process management (BPM) technology is used as the basis and extended to accommodate the physical nature of manufacturing processes as opposed to the administrative processes for which this technology is traditionally used [32]. Most significantly, the location of manufacturing operations must be considered to accommodate the time it takes for material to flow between locations. The Agent Design module is a graphical user interface that enables the user to create new agent profiles or edit existing agent profiles. Such a profile comprises attributes that describe the agent including abilities, skills, authorization, cost and performance.

HORSE Config Local provides functionality for defining the operations performed within a work cell. The terminology of ‘task’ and ‘step’ is used here to distinguish between the actions of multiple and single agents. The task design module is used to specify the synchronization of a team of agents within a work cell like the interplay between a human worker and a robot. The actions performed by the individual members of a team are specified using the two-step design modules. The human step design module is used to create work instructions. These work instructions may range from simple textual descriptions to technology-supported guidance like augmented reality. The automated agent step design module is used to create execution scripts for robot, automated guided vehicles or any other non-human agents. Several different instances of

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to accommodate the physical nature of manufacturing processes as opposed to the administrativeprocesses for which this technology is traditionally used [32]. Most significantly, the location ofmanufacturing operations must be considered to accommodate the time it takes for material to flowbetween locations. The Agent Design module is a graphical user interface that enables the user to createnew agent profiles or edit existing agent profiles. Such a profile comprises attributes that describe theagent including abilities, skills, authorization, cost, and performance.


processes for which this technology is traditionally used [32]. Most significantly, the location of manufacturing operations must be considered to accommodate the time it takes for material to flow between locations. The Agent Design module is a graphical user interface that enables the user to create new agent profiles or edit existing agent profiles. Such a profile comprises attributes that describe the agent including abilities, skills, authorization, cost, and performance.

Figure 7. HORSE architecture, design time aspect, aggregation level 2.

HORSE Config Local provides functionality for defining the operations performed within a work cell. The terminology of ‘task’ and ‘step’ is used here to distinguish between the actions of multiple and single agents. The task design module is used to specify the synchronization of a team of agents within a work cell like the interplay between a human worker and a robot. The actions performed by the individual members of a team are specified using the two-step design modules. The human step design module is used to create work instructions. These work instructions may range from simple textual descriptions to technology-supported guidance like augmented reality. The automated agent step design module is used to create execution scripts for robot, automated guided vehicles or any other non-human agents. Several different instances of this module may exist in the same enterprise, which corresponds to the different types of automated agents used and supports textual scripting, graphical scripting, and scripting by physical manipulation (physically showing the robot what to do, which is also called programming by demonstration [9]). The latter requires a direct connection to the involved robot, which is shown in Figure 7. Lastly, the work cell simulator module is used to digitally define and evaluate the physical constraints within which a task will be executed. Physical constraints may include inter alia, the space available for agents to move, the location of the work cell, and the mounted position of the automated agents.

Figure 7. HORSE architecture, design time aspect, aggregation level 2.

HORSE Config Local provides functionality for defining the operations performed within a workcell. The terminology of ‘task’ and ‘step’ is used here to distinguish between the actions of multipleand single agents. The task design module is used to specify the synchronization of a team of agentswithin a work cell like the interplay between a human worker and a robot. The actions performed bythe individual members of a team are specified using the two-step design modules. The human stepdesign module is used to create work instructions. These work instructions may range from simpletextual descriptions to technology-supported guidance like augmented reality. The automated agentstep design module is used to create execution scripts for robot, automated guided vehicles or anyother non-human agents. Several different instances of this module may exist in the same enterprise,which corresponds to the different types of automated agents used and supports textual scripting,graphical scripting, and scripting by physical manipulation (physically showing the robot what to do,which is also called programming by demonstration [9]). The latter requires a direct connection to theinvolved robot, which is shown in Figure 7. Lastly, the work cell simulator module is used to digitallydefine and evaluate the physical constraints within which a task will be executed. Physical constraintsmay include inter alia, the space available for agents to move, the location of the work cell, and themounted position of the automated agents.

Figure 7. HORSE architecture, design time aspect, aggregation level 2

this module may exist in the same enterprise, which corresponds to the different types of automated agents used and supports textual scripting, graphical scripting and scripting by physical manipulation (physically showing the robot what to do, which is also called programming by demonstration [9]). The latter requires a direct connection to the involved robot, which is shown in Figure 7. Lastly, the work cell simulator module is used to digitally define and evaluate the physical constraints within which a task will be executed. Physical constraints may include inter alia, the space available for agents to move, the location of the work cell and the mounted position of the automated agents.

3.4. HoRSE execution time architecture Level 2This section elaborates the execution-time column of the

HORSE System, as shown in Figure 8. Within the global and local layers, each contain an execution and an awareness module. HORSE Exec Global provides the functionality to enact and monitor manufacturing processes across multiple work cells. Analogous to global design subsystem, the HORSE Exec Global subsystem is based on BPM technology with extensions to make it suitable for manufacturing processes [33].


HORSE Exec Local provides the functionality to control and monitor the activities of a team of agents in a single work cell (a team may be only one agent). This subsystem directly interacts with agents by sending commands and receiving responses. The interaction differs between humans and automated agents. Work instructions are displayed on handheld devices or fixed monitors and responses are received via physical or virtual buttons. For example, an operator may receive the instruction to perform a task via an instant message. Once at the station, the augmented reality guidance is displayed via the local awareness module and hand movements are detected via a sensor. Automated agents receive execution scripts and respond according to predefined parameters. The Local Awareness module is concerned with regular activity monitoring and exception detection.

An instance of HORSE Exec Local is created for each team formed to perform a task. Therefore, multiple instances of the subsystem may exist at the same time and may even build on different technology platforms. The abstraction layers facilitate the communication between the HORSE Exec Global and multiple instances of HORSE Exec Local. Integration between the global and local layers, as facilitated by the abstraction layers, is illustrated

with a video available on the project website (http://www.horse-project.eu/Media). The HORSE Exec Local subsystem is discussed in more detail in Section 3.5.

3.5. HoRSE Exec Local at Aggregation Level 3Figure 9 shows the refinement of the HORSE Exec Local system

module at aggregation level 3. It is, in this part of the HORSE architecture, that the cyber-physical character of the system becomes most apparent because this subsystem is responsible for the real-time control of physical agents.

The Local Execution module, as shown on the left side of Figure 9, is responsible for driving the execution of manufacturing tasks. The Hybrid Task Supervisor module delivers the human-robot collaboration capability of the HORSE System. The module controls synchronize the actions of multiple human and/or robotic workers (in HORSE terminology, human agents and automated agents, respectively). These workers receive their instructions via Step Execution modules that manage individual work steps for individual agents and Execution Interfaces that abstract from specific agent control characteristics (such as specific robot control interfaces). Local Execution implements actuation controls from an IoT perspective.

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3.4. HORSE Execution Time Architecture Level 2

This section elaborates the execution-time column of the HORSE System, as shown in Figure 8.Within the global and local layers, each contain an execution and an awareness module. HORSE ExecGlobal provides the functionality to enact and monitor manufacturing processes across multiple workcells. Analogous to global design subsystem, the HORSE Exec Global subsystem is based on BPMtechnology with extensions to make it suitable for manufacturing processes [33].

HORSE Exec Local provides the functionality to control and monitor the activities of a teamof agents in a single work cell (a team may be only one agent). This subsystem directly interactswith agents by sending commands and receiving responses. The interaction differs between humansand automated agents. Work instructions are displayed on handheld devices or fixed monitors andresponses are received via physical or virtual buttons. For example, an operator may receive theinstruction to perform a task via an instant message. Once at the station, the augmented realityguidance is displayed via the local awareness module and hand movements are detected via asensor. Automated agents receive execution scripts and respond according to predefined parameters.The Local Awareness module is concerned with regular activity monitoring and exception detection.


3.4. HORSE Execution Time Architecture Level 2

This section elaborates the execution-time column of the HORSE System, as shown in Figure 8. Within the global and local layers, each contain an execution and an awareness module. HORSE Exec Global provides the functionality to enact and monitor manufacturing processes across multiple work cells. Analogous to global design subsystem, the HORSE Exec Global subsystem is based on BPM technology with extensions to make it suitable for manufacturing processes [33].

HORSE Exec Local provides the functionality to control and monitor the activities of a team of agents in a single work cell (a team may be only one agent). This subsystem directly interacts with agents by sending commands and receiving responses. The interaction differs between humans and automated agents. Work instructions are displayed on handheld devices or fixed monitors and responses are received via physical or virtual buttons. For example, an operator may receive the instruction to perform a task via an instant message. Once at the station, the augmented reality guidance is displayed via the local awareness module and hand movements are detected via a sensor. Automated agents receive execution scripts and respond according to predefined parameters. The Local Awareness module is concerned with regular activity monitoring and exception detection.

Figure 8. HORSE architecture, execution time aspect, aggregation level 2.

An instance of HORSE Exec Local is created for each team formed to perform a task. Therefore, multiple instances of the subsystem may exist at the same time and may even build on different technology platforms. The abstraction layers facilitate the communication between the HORSE Exec Global and multiple instances of HORSE Exec Local. Integration between the global and local layers, as facilitated by the abstraction layers, is illustrated with a video available on the project website (http://www.horse-project.eu/Media). The HORSE Exec Local subsystem is discussed in more detail in Section 3.5.


An instance of HORSE Exec Local is created for each team formed to perform a task. Therefore,multiple instances of the subsystem may exist at the same time and may even build on differenttechnology platforms. The abstraction layers facilitate the communication between the HORSE ExecGlobal and multiple instances of HORSE Exec Local. Integration between the global and local layers,as facilitated by the abstraction layers, is illustrated with a video available on the project website(http://www.horse-project.eu/Media). The HORSE Exec Local subsystem is discussed in more detailin Section 3.5.



The Local Awareness module, on the right side of Figure 9, is responsible for monitoring the work cell. This module is coupled to sensors and cameras in the work cell that provide real-time information about the status of the work cell such as the position of robotic arms and manipulated products. These sensors and cameras may be attached to robots such as for measuring torque or applying pressure sensors on robot arms. Part of this information can be fed to displays that provide information to human workers. Local Awareness implements sensing controls from an IoT perspective.

4. Physical view of the HORse systemThe logical view of the HORSE System architecture is explained

in Section 3. In this section, the physical view of the Kruchten 4 + 1 framework is applied. The goal of this section is to analyze the role of Industry 4.0 technologies in the HORSE System instead of specifying the actual hardware, infrastructure and physical systems. Given the primary context of small and medium manufacturing enterprises, it is first determined, in Section 4.1, which modules of the system can be situated in the cloud. Since the design time sub-system and the execution time sub-system of the HORSE system have very different technical requirements, we perform this analysis per sub-system. This analysis naturally establishes the divide between the cloud-supported modules of the system and the Things on the factory floor. The divide is used to construct an IoT-view of the HORSE System, which is presented in Section 4.2.

4.1. Cloud Support for the HoRSE SystemTo determine which modules of the HORSE System can be

hosted in the cloud, we consider the performance expectations of various modules. For example, modules used during the design period generally do not require guaranteed sub-second response times. Hence, such modules can be hosted as cloud applications and be subjected to the normal performance impact of distance and Internet traffic. To add some nuance to the discussion, we distinguish between two cloud-based models and a non-cloud model for software deployment [34].

l The Software-as-a-Service (SaaS) model provides a software application as a hosted service on the Internet, eliminating the need to install and run the application on local computers.

l The Platform-as-a-Service (PaaS) model provides an application environment in which users can create their own application that will run on the cloud.

l The on-premise model represents traditional computing with no part of the software or computer hardware hosted in the cloud.

Rimal et al. [35] advocates for scalability, performance, multi-tenancy, configurability and fault-tolerance as the primary considerations for cloud support of applications. Table 1 lists the advantages and disadvantages inherent to each cloud-support model in relation to the five considerations.

We apply the five considerations listed in Table 1 to determine which modules of the HORSE System can be hosted with the SaaS or PaaS cloud computing models. The results of the analysis are listed in Table 2.

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3.5. HORSE Exec Local at Aggregation Level 3

Figure 9 shows the refinement of the HORSE Exec Local system module at aggregation level 3.It is, in this part of the HORSE architecture, that the cyber-physical character of the system becomesmost apparent because this subsystem is responsible for the real-time control of physical agents.


3.5. HORSE Exec Local at Aggregation Level 3

Figure 9 shows the refinement of the HORSE Exec Local system module at aggregation level 3. It is, in this part of the HORSE architecture, that the cyber-physical character of the system becomes most apparent because this subsystem is responsible for the real-time control of physical agents.


The Local Execution module, as shown on the left side of Figure 9, is responsible for driving the execution of manufacturing tasks. The Hybrid Task Supervisor module delivers the human-robot collaboration capability of the HORSE System. The module controls synchronize the actions of multiple human and/or robotic workers (in HORSE terminology, human agents and automated agents, respectively). These workers receive their instructions via Step Execution modules that manage individual work steps for individual agents and Execution Interfaces that abstract from specific agent control characteristics (such as specific robot control interfaces). Local Execution implements actuation controls from an IoT perspective.

The Local Awareness module, on the right side of Figure 9, is responsible for monitoring the work cell. This module is coupled to sensors and cameras in the work cell that provide real-time information about the status of the work cell such as the position of robotic arms and manipulated products. These sensors and cameras may be attached to robots such as for measuring torque or applying pressure sensors on robot arms. Part of this information can be fed to displays that provide information to human workers. Local Awareness implements sensing controls from an IoT perspective.

4. Physical View of the HORSE System

The logical view of the HORSE System architecture is explained in Section 3. In this section, the physical view of the Kruchten 4 + 1 framework is applied. The goal of this section is to analyze the role of Industry 4.0 technologies in the HORSE System instead of specifying the actual hardware, infrastructure, and physical systems. Given the primary context of small and medium manufacturing


The Local Execution module, as shown on the left side of Figure 9, is responsible for driving theexecution of manufacturing tasks. The Hybrid Task Supervisor module delivers the human-robotcollaboration capability of the HORSE System. The module controls synchronize the actions ofmultiple human and/or robotic workers (in HORSE terminology, human agents and automatedagents, respectively). These workers receive their instructions via Step Execution modules that manageindividual work steps for individual agents and Execution Interfaces that abstract from specific agentcontrol characteristics (such as specific robot control interfaces). Local Execution implements actuationcontrols from an IoT perspective.

The Local Awareness module, on the right side of Figure 9, is responsible for monitoring the workcell. This module is coupled to sensors and cameras in the work cell that provide real-time informationabout the status of the work cell such as the position of robotic arms and manipulated products. Thesesensors and cameras may be attached to robots such as for measuring torque or applying pressuresensors on robot arms. Part of this information can be fed to displays that provide information tohuman workers. Local Awareness implements sensing controls from an IoT perspective.

4. Physical View of the HORSE System

The logical view of the HORSE System architecture is explained in Section 3. In this section, thephysical view of the Kruchten 4 + 1 framework is applied. The goal of this section is to analyze therole of Industry 4.0 technologies in the HORSE System instead of specifying the actual hardware,infrastructure, and physical systems. Given the primary context of small and medium manufacturingenterprises, it is first determined, in Section 4.1, which modules of the system can be situated in thecloud. Since the design time sub-system and the execution time sub-system of the HORSE system have



Figure 10 shows the result of the five considerations as an overlay on the logical view of the HORSE System architecture. The modules at the global layer support process design, agent design, process enactment and monitoring. Process and agent design are interactive modules but do require any guaranteed performance. A further advantage of the SaaS model is simplified versioning and upgrade of the software since manufacturing is becoming more flexible [36,37]. Process enactment and monitoring has a real-time character but without very strict timing requirements. Consequently, it is possible to deploy all global layer modules and both the data stores in the cloud. An important requirement for the cloud environment is a very high quality of service (QoS) in terms of availability: unavailability of global functionality typically brings a process-oriented manufacturing plant to a halt within minutes if not seconds.

The modules at the local layer control the activities of agents—either individually or in teams—are very much real-time cyber-physical systems. This means that part of their functionality has very strict response-time requirements. A good example is safety management, which requires fast synchronization between sensors, local awareness functionality, local execution functionality and agents (humans and robots). Figure 11 shows the communication path in the system (as a simplified view of Figure 9 to make things clearer) when a human agent enters the operating space of a robotic agent and the robotic agent must immediately stop.

4.2. The HoRSE System as an IoT applicationWith HORSE Exec Global in the cloud and HORSE Exec Local

on-site, the HORSE System relies heavily on communication between the cloud and the Things that perform manufacturing activities. Significant disagreement exists regarding the nature and scope of the IoT [38], but Wortmann and Flüchter [39] offer a complete overview of the concept by bringing together computing, connectivity and devices in a single technology stack. The technology stack follows the concept of functional abstraction [31], i.e., each layer builds on the functionality of the layer below. For example, the control software of a thing/device makes use of the actuating and sensing components to exert control over the hardware of the thing/device.

The technology stack draws attention to the division between the things or devices (including its embedded control system) on the factory floor and software situated in the IoT cloud. The connectivity layer facilitates communication between the IoT Cloud and one or more things/devices.

Functional abstraction and the division between the cloud and the device is applied to construct a technology stack for the HORSE System. The resulting technology stack, which is shown in Figure 12, serves two purposes: (1) to describe how modern technologies contribute to realize the HORSE System and (2) to justify the designation of the HORSE System as an IoT application.

Starting from the bottom of Figure 12, to simplify the explanation of the assimilation of technologies, the various things and devices are shown as simple icons. Human interfaces are represented with

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very different technical requirements, we perform this analysis per sub-system. This analysis naturallyestablishes the divide between the cloud-supported modules of the system and the Things on thefactory floor. The divide is used to construct an IoT-view of the HORSE System, which is presented inSection 4.2.

4.1. Cloud Support for the HORSE System

To determine which modules of the HORSE System can be hosted in the cloud, we consider theperformance expectations of various modules. For example, modules used during the design periodgenerally do not require guaranteed sub-second response times. Hence, such modules can be hostedas cloud applications and be subjected to the normal performance impact of distance and Internettraffic. To add some nuance to the discussion, we distinguish between two cloud-based models and anon-cloud model for software deployment [34].

• The Software-as-a-Service (SaaS) model provides a software application as a hosted service on theInternet, eliminating the need to install and run the application on local computers.

• The Platform-as-a-Service (PaaS) model provides an application environment in which users cancreate their own application that will run on the cloud.

• The on-premise model represents traditional computing with no part of the software or computerhardware hosted in the cloud.

Rimal et al. [35] advocates for scalability, performance, multi-tenancy, configurability, andfault-tolerance as the primary considerations for cloud support of applications. Table 1 lists the advantagesand disadvantages inherent to each cloud-support model in relation to the five considerations.

Table 1. Advantages and disadvantages of SaaS, PaaS, and on-premise models in relation to the fiveconsiderations for cloud-support.

Consideration SaaS PaaS On-Premise

ScalabilitySoftware and computingresources can be scaledquickly.

Computing resourcescan be scaled quickly.

Scaling requiresinstallation of newsoftware and hardware.

Performance

Cannot be guaranteedbecause it is subject tonetwork quality, traffic, anddistance.

Cannot be guaranteedbecause it is subject tonetwork quality, traffic,and distance.

Best response-timeperformance attainable.

Multi-tenancyTenancy can easily beextended to any agent withInternet access.

Additional tenantsadded with additionalsoftware installation orexpansion.

Only within its localenvironment.

Configurability

Software and computingresource changes are subjectto the agreements with theservice provider.

Computing resourcechanges are subject toagreements with theservice provider.

All changes are underthe control of the user.

Fault-toleranceFault-tolerance is defined aspart of the quality-of-serviceagreements.

Fault-tolerance isdefined as part of thequality-of-serviceagreements.

Fault-tolerance is underthe control of the user.

We apply the five considerations listed in Table 1 to determine which modules of the HORSESystem can be hosted with the SaaS or PaaS cloud computing models. The results of the analysis arelisted in Table 2.

Table 1. Advantages and disadvantages of SaaS, PaaS, and on-premise models in relation to the five considerations for cloud-support


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Table 2. Application of the five considerations for cloud-support for the modules of the HORSE System.

System Module orData Store Cloud Support Rationale

Process DesignAgent DesignProcess/Agent DataGlobal ExecutionGlobal AwarenessTask/Step/Cell Data

SaaS

The scalability and multi-tenancy afforded by SaaS is anopportunity to extend service to additional production areas,sites, or even enterprises.Singular deployment implies no configurability requirements.No strict performance guarantees required.Fault-tolerance can be addressed with the quality-of-serviceagreements with the service provider.

Exec GlobalAbstraction LayerExec Local AbstractionLayer

SaaS

The scalability, multi-tenancy, and configurability of aSaaS-based abstraction layer makes it possible to extend theglobal functionality across multiple production areas, sites, orenterprises.No strict performance guarantees required.Fault-tolerance can be addressed with quality-of-serviceagreements with the service provider.

Task DesignHuman Step Design PaaS

The platform can be scaled to additional production areas, sites,or enterprises.Tenancy can be extended with additional software deploymentson the same platform.Technology-heterogeneity requires extensive configuration ofsoftware on the same platform.No strict performance guarantees required.Fault-tolerance can be addressed with the quality-of-serviceagreements with the service provider.

Automated StepDesign

PaaS orOn-premise

PaaS or On-premise model depends on the direct connection toan automated agent.For textual execution scripts, the PaaS model can providescalability and multi-tenant support fortechnology-heterogenous deployments with no strictperformance or fault-tolerance requirements.Programming-by-demonstration requires on-premise hardwareand software to support immediate response to the movementsperformed by the human.

Work Cell Simulator PaaS

The platform can be scaled to additional production areas, sites,or enterprises.Tenancy can be extended with additional software deploymentson the same platform.Technology-heterogeneity requires extensive configuration ofsoftware on the same platform.No strict performance guarantees required.Fault-tolerance can be addressed with quality-of-serviceagreements with the service provider.

Local ExecutionLocal Awareness On-premise

Multiple, technology-heterogeneous realizations for each localdeployment requires no scalability or multi-tenancy.The configuration is done during the design-time.The control of multiple, interacting agents require strictperformance guarantees in the millisecond domain andnear-zero fault tolerance.

Figure 10 shows the result of the five considerations as an overlay on the logical view of theHORSE System architecture. The modules at the global layer support process design, agent design,process enactment, and monitoring. Process and agent design are interactive modules but do requireany guaranteed performance. A further advantage of the SaaS model is simplified versioning andupgrade of the software since manufacturing is becoming more flexible [36,37]. Process enactment

Table 2. Application of the five considerations for cloud-support for the modules of the HORSE System.


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and monitoring has a real-time character but without very strict timing requirements. Consequently,it is possible to deploy all global layer modules and both the data stores in the cloud. An importantrequirement for the cloud environment is a very high quality of service (QoS) in terms of availability:unavailability of global functionality typically brings a process-oriented manufacturing plant to a haltwithin minutes if not seconds.


Figure 10 shows the result of the five considerations as an overlay on the logical view of the HORSE System architecture. The modules at the global layer support process design, agent design, process enactment, and monitoring. Process and agent design are interactive modules but do require any guaranteed performance. A further advantage of the SaaS model is simplified versioning and upgrade of the software since manufacturing is becoming more flexible [36,37]. Process enactment and monitoring has a real-time character but without very strict timing requirements. Consequently, it is possible to deploy all global layer modules and both the data stores in the cloud. An important requirement for the cloud environment is a very high quality of service (QoS) in terms of availability: unavailability of global functionality typically brings a process-oriented manufacturing plant to a halt within minutes if not seconds.

Figure 10. Overview of cloud support for the HORSE System.

The modules at the local layer control the activities of agents—either individually or in teams—are very much real-time cyber-physical systems. This means that part of their functionality has very strict response-time requirements. A good example is safety management, which requires fast synchronization between sensors, local awareness functionality, local execution functionality, and agents (humans and robots). Figure 11 shows the communication path in the system (as a simplified view of Figure 9 to make things clearer) when a human agent enters the operating space of a robotic agent and the robotic agent must immediately stop.

Figure 11. Local communication path in case of an observed safety breach.


The modules at the local layer control the activities of agents—either individually or in teams—arevery much real-time cyber-physical systems. This means that part of their functionality has verystrict response-time requirements. A good example is safety management, which requires fastsynchronization between sensors, local awareness functionality, local execution functionality, andagents (humans and robots). Figure 11 shows the communication path in the system (as a simplifiedview of Figure 9 to make things clearer) when a human agent enters the operating space of a roboticagent and the robotic agent must immediately stop.





Figure 11. Local communication path in case of an observed safety breach. Figure 11. Local communication path in case of an observed safety breach.

4.2. The HORSE System as an IoT Application

With HORSE Exec Global in the cloud and HORSE Exec Local on-site, the HORSE System reliesheavily on communication between the cloud and the Things that perform manufacturing activities.


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Machines 2018, 6, 62 14 of 25
















displays and handheld devices. The sensors are represented with a camera and microphone while robots are represented with a robotic arm and a vehicle. Lastly, augmented reality is represented with wearable glasses and a sensor to track human movements. The distinction between hardware and components is omitted here because it adds no value to the current discussion. The displayed things or devices are only representative since the actual set may differ in a factory. On the second layer from the bottom, the various devices and things are controlled by their respective software systems. These software systems are left purposefully abstract to signify the open nature of the HORSE System. Many different technologies can be utilized in conjunction with the HORSE System. The software systems of the devices are connected to the HORSE System via the local area network of the factory.

The HORSE Local Execution subsystem serves as thing/device communication and management. This is the most significant deviation from the IoT technology stack of Wortmann and Flüchter [39]. The thing/device communication and management is not included in the Cloud grouping of the technology stack because of the requirement for guaranteed, sub-second communication and synchronization between teams of agents, which is discussed in Section 3.4. The HORSE System currently has two realizations of the

Local Execution subsystem based on the Robot Operating System (ROS) and Kuka Sunrise software, respectively. Both these variations can control various technologies involved in smart manufacturing processes and can even be deployed simultaneously in the same factory.

A singular block named Internet is shown to represent the connectivity between Local and Global features. This implies all standard infrastructure and protocols because the current realization of the HORSE System uses standard internet protocols to transport JavaScript Object Notation (JSON) messages. These messages are directed via the message bus of the Open Services Gateway initiative (OSGi) application platform, which allows all subsystems of the HORSE System to subscribe to it and publish messages. The OSGi application platform ties in well with the PaaS model, which is explained in Section 4.1. With an OSGi-based platform and accompanying computing resources hosted in the cloud, the user can install a variety of thing/device communication and management systems based on different technologies to control the activities of different robots and sensors.

HORSE Global Execution, which represents the process management technology, publishes messages and subscribes to listen for responses. Similarly, the HORSE Global Awareness

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Significant disagreement exists regarding the nature and scope of the IoT [38], but Wortmann andFlüchter [39] offer a complete overview of the concept by bringing together computing, connectivity,and devices in a single technology stack. The technology stack follows the concept of functionalabstraction [31], i.e., each layer builds on the functionality of the layer below. For example, the controlsoftware of a thing/device makes use of the actuating and sensing components to exert control overthe hardware of the thing/device.

The technology stack draws attention to the division between the things or devices (including itsembedded control system) on the factory floor and software situated in the IoT cloud. The connectivitylayer facilitates communication between the IoT Cloud and one or more things/devices.

Functional abstraction and the division between the cloud and the device is applied to construct atechnology stack for the HORSE System. The resulting technology stack, which is shown in Figure 12,serves two purposes: (1) to describe how modern technologies contribute to realize the HORSE Systemand (2) to justify the designation of the HORSE System as an IoT application.



With HORSE Exec Global in the cloud and HORSE Exec Local on-site, the HORSE System relies heavily on communication between the cloud and the Things that perform manufacturing activities. Significant disagreement exists regarding the nature and scope of the IoT [38], but Wortmann and Flüchter [39] offer a complete overview of the concept by bringing together computing, connectivity, and devices in a single technology stack. The technology stack follows the concept of functional abstraction [31], i.e., each layer builds on the functionality of the layer below. For example, the control software of a thing/device makes use of the actuating and sensing components to exert control over the hardware of the thing/device.

The technology stack draws attention to the division between the things or devices (including its embedded control system) on the factory floor and software situated in the IoT cloud. The connectivity layer facilitates communication between the IoT Cloud and one or more things/devices.

Functional abstraction and the division between the cloud and the device is applied to construct a technology stack for the HORSE System. The resulting technology stack, which is shown in Figure 12, serves two purposes: (1) to describe how modern technologies contribute to realize the HORSE System and (2) to justify the designation of the HORSE System as an IoT application.

Figure 12. Technology stack showing the HORSE System as an IoT application.

Starting from the bottom of Figure 12, to simplify the explanation of the assimilation of technologies, the various things and devices are shown as simple icons. Human interfaces are represented with displays and handheld devices. The sensors are represented with a camera and microphone while robots are represented with a robotic arm and a vehicle. Lastly, augmented reality is represented with wearable glasses and a sensor to track human movements. The distinction between hardware and components is omitted here because it adds no value to the current discussion. The displayed things or devices are only representative since the actual set may differ in a factory. On the second layer from the bottom, the various devices and things are controlled by their

Figure 12. Technology stack showing the HORSE System as an IoT application.

Starting from the bottom of Figure 12, to simplify the explanation of the assimilation oftechnologies, the various things and devices are shown as simple icons. Human interfaces arerepresented with displays and handheld devices. The sensors are represented with a camera andmicrophone while robots are represented with a robotic arm and a vehicle. Lastly, augmented realityis represented with wearable glasses and a sensor to track human movements. The distinctionbetween hardware and components is omitted here because it adds no value to the current discussion.The displayed things or devices are only representative since the actual set may differ in a factory.On the second layer from the bottom, the various devices and things are controlled by their respectivesoftware systems. These software systems are left purposefully abstract to signify the open natureof the HORSE System. Many different technologies can be utilized in conjunction with the HORSESystem. The software systems of the devices are connected to the HORSE System via the local areanetwork of the factory.

Figure 12. Technology stack showing the HORSE System as an IoT application


subsystem represents the data analytics technology by listening for various pre-defined events that may indicate unwanted or unexpected factory floor conditions. Thus, the HORSE System is an IoT application with multiple layers of functionality covering the complete technology stack of Wortmann and Flüchter [39].

5. scenarios as proof of conceptThe HORSE System is positioned as a manufacturing operations

management system for Industry 4.0. Therefore, it is claimed that the HORSE System can be used to manage operations that involve modern manufacturing technologies. To substantiate this claim, manufacturing processes that utilize the smart technologies are demonstrated to serve as proof of concept of the HORSE System. For each process, the involvement and role of various technologies are highlighted in the process models and are subsequently discussed.

The three pilot cases considered in the HORSE Project represent a wide range of problems encountered in the manufacturing industry. These pilot cases are analyzed to articulate clear scenarios where smart manufacturing technology is applied to solve common problems. Each scenario is described to give context, represented as a process model and demonstrated with video footage.

5.1. Pilot case 1: Tool assemblyThe first pilot site of the HORSE Project is a medium-sized

factory in The Netherlands that produces highly configurable metal products used in furniture assembly. The pilot case features two processes including tool assembly and surface treatment of metal profiles. The first process involves a human operator who attaches tool parts to a base-plate to assemble a configurable tool used for deformation operations. In parallel, a mobile robotic arm fetches bins containing the parts needed by the operator. To

alleviate the complication and variability of the assembly task, the human assists by augmented reality technology that highlights which parts are needed and how to attach those parts. The model of this process is specified using the Business Process Model & Notation 2.0 (BPMN2.0) and shown in Figure 13. Video footage of the executed process with augmented reality and robotic support is available online (https://youtu.be/bqTDEZvOdVI).

The model shown in Figure 13 is created using the Design Global subsystem of the HORSE System (see Section 3.3). Thereafter, the process model is parsed by the Global Execution to enact the process during the demonstration. Tasks, as specified in the process model, are globally instantiated and assigned to agents. The task instructions are sent to the Exec Local subsystem via the message bus. The Exec Local subsystem ensures synchronization between the robot and human and is guided by augmented reality.

The full stack of technology, as shown in Figure 12, is utilized to execute the tool assembly process. As a summary, the various technologies have the following roles in the process.l Cloud-based process management to coordinate the activities

of human and automated agents.l IoT-enabled connectivity between cloud-based process

management and multiple production agents.l Augmented reality to guide the human agent through the tool

assembly steps.l Smart robotics to fetch and return tooling parts from the storage

zone.Pilot case 1 is a particularly compelling case for the HORSE

Project because it is not a simple replacement of a human operator with a robot. Instead, the operations are granularized to determine which activities are more suited to human or robot execution. The cognitive and sensory abilities of the human are exploited for the complicated assembly task, but the monotonous fetching and

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Figure 13. Process model used to enact the demonstration of Case 1a.

The model shown in Figure 13 is created using the Design Global subsystem of the HORSE System (see Section 3.3). Thereafter, the process model is parsed by the Global Execution to enact the process during the demonstration. Tasks, as specified in the process model, are globally instantiated and assigned to agents. The task instructions are sent to the Exec Local subsystem via the message bus. The Exec Local subsystem ensures synchronization between the robot and human and is guided by augmented reality.

The full stack of technology, as shown in Figure 12, is utilized to execute the tool assembly process. As a summary, the various technologies have the following roles in the process.

• Cloud-based process management to coordinate the activities of human and automated agents. • IoT-enabled connectivity between cloud-based process management and multiple

production agents. • Augmented reality to guide the human agent through the tool assembly steps. • Smart robotics to fetch and return tooling parts from the storage zone.

Pilot case 1 is a particularly compelling case for the HORSE Project because it is not a simple replacement of a human operator with a robot. Instead, the operations are granularized to determine which activities are more suited to human or robot execution. The cognitive and sensory abilities of the human are exploited for the complicated assembly task, but the monotonous fetching and returning tasks are allocated to a mobile robot. Therefore, instead of being replaced by automation, the human is rather supported by automation and allowed to focus on the task that requires deep concentration, which increases process throughput.

The primary limitation in this pilot case is related to the definition of augmented reality tasks. The images and instructions displayed during augmented reality supported tool assembly are currently manually programmed for a particular subset of tool configurations. This is perfectly adequate for demonstration purposes in a research and innovation project, but it must be simplified or at least streamlined for commercial purposes.

5.2. Pilot Case 2: Final Assembly of Automotive Parts

The second pilot site of the HORSE Project is a medium-sized factory in Spain that assembles highly customizable automotive parts. The case includes the final inspection and packaging of the assemblies before distribution to customers. The process involves three agents: (1) a robotic arm to pick up an assembly from the conveyor belt, present it for inspection, and, if accepted, place it in a box, (2) a sophisticated, bespoke camera system to inspect the assemblies, and (3) a human operator to evaluate assemblies flagged by the camera system to determine whether to discard or repair it. To deal with the complication of inspecting mass customized products, the human operator is assisted


The model shown in Figure 13 is created using the Design Global subsystem of the HORSE System(see Section 3.3). Thereafter, the process model is parsed by the Global Execution to enact the processduring the demonstration. Tasks, as specified in the process model, are globally instantiated andassigned to agents. The task instructions are sent to the Exec Local subsystem via the message bus.The Exec Local subsystem ensures synchronization between the robot and human and is guided byaugmented reality.

The full stack of technology, as shown in Figure 12, is utilized to execute the tool assembly process.As a summary, the various technologies have the following roles in the process.

• Cloud-based process management to coordinate the activities of human and automated agents.• IoT-enabled connectivity between cloud-based process management and multiple production agents.• Augmented reality to guide the human agent through the tool assembly steps.• Smart robotics to fetch and return tooling parts from the storage zone.

Pilot case 1 is a particularly compelling case for the HORSE Project because it is not a simplereplacement of a human operator with a robot. Instead, the operations are granularized to determinewhich activities are more suited to human or robot execution. The cognitive and sensory abilities of thehuman are exploited for the complicated assembly task, but the monotonous fetching and returningtasks are allocated to a mobile robot. Therefore, instead of being replaced by automation, the human israther supported by automation and allowed to focus on the task that requires deep concentration,which increases process throughput.

The primary limitation in this pilot case is related to the definition of augmented reality tasks.The images and instructions displayed during augmented reality supported tool assembly are currentlymanually programmed for a particular subset of tool configurations. This is perfectly adequate fordemonstration purposes in a research and innovation project, but it must be simplified or at leaststreamlined for commercial purposes.

5.2. Pilot Case 2: Final Assembly of Automotive Parts

The second pilot site of the HORSE Project is a medium-sized factory in Spain that assembleshighly customizable automotive parts. The case includes the final inspection and packaging of theassemblies before distribution to customers. The process involves three agents: (1) a robotic arm topick up an assembly from the conveyor belt, present it for inspection, and, if accepted, place it in abox, (2) a sophisticated, bespoke camera system to inspect the assemblies, and (3) a human operator toevaluate assemblies flagged by the camera system to determine whether to discard or repair it. To deal



returning tasks are allocated to a mobile robot. Therefore, instead of being replaced by automation, the human is rather supported by automation and allowed to focus on the task that requires deep concentration, which increases process throughput.

The primary limitation in this pilot case is related to the definition of augmented reality tasks. The images and instructions displayed during augmented reality supported tool assembly are currently manually programmed for a particular subset of tool configurations. This is perfectly adequate for demonstration purposes in a research and innovation project, but it must be simplified or at least streamlined for commercial purposes.

5.2. Pilot case 2: Final assembly of automotive partsThe second pilot site of the HORSE Project is a medium-sized

factory in Spain that assembles highly customizable automotive parts. The case includes the final inspection and packaging of the assemblies before distribution to customers. The process involves

three agents: (1) a robotic arm to pick up an assembly from the conveyor belt, present it for inspection and, if accepted, place it in a box, (2) a sophisticated, bespoke camera system to inspect the assemblies and (3) a human operator to evaluate assemblies flagged by the camera system to determine whether to discard or repair it. To deal with the complication of inspecting mass customized products, the human operator is assisted by an augmented reality system that indicates where and what the operator should inspect. Figure 14 shows the model used to enact the process of the second pilot case. Additional information and photos of pilot case 2 in operation are available online (http://www.horse-project.eu/Pilot-Experiment-1).

As can be seen from the lanes in Figure 14, the process involves four different roles, which are potentially performed by as many agents. The following technology is involved in this process.l Cloud-based process management across multiple work cells.l IoT-enabled connectivity between cloud-based process

Machines 2018, 6, 62 18 of 25

with the complication of inspecting mass customized products, the human operator is assisted by anaugmented reality system that indicates where and what the operator should inspect. Figure 14 showsthe model used to enact the process of the second pilot case. Additional information and photos ofpilot case 2 in operation are available online (http://www.horse-project.eu/Pilot-Experiment-1).


by an augmented reality system that indicates where and what the operator should inspect.Error! Reference source not found. shows the model used to enact the process of the second pilot case. Additional information and photos of pilot case 2 in operation are available online (http://www.horse-project.eu/Pilot-Experiment-1).

Figure 14. Assembly inspection and packaging process model of pilot case 2.

As can be seen from the lanes in Error! Reference source not found., the process involves four different roles, which are potentially performed by as many agents. The following technology is involved in this process.

• Cloud-based process management across multiple work cells. • IoT-enabled connectivity between cloud-based process management and multiple production

agents. • Synchronized collaboration between smart robot and camera system to detect product defects. • Augmented reality to guide the human agent with an inspection of highly customizable

assemblies.

Pilot case 2 is a complex scenario involving four process participants and various eventualities that affect process execution. Communication from the factory floor is of utmost importance here to allow the HORSE System, track progress, and adjust accordingly. Communication is facilitated by handheld devices and sensors connected to the local network. The most compelling part of pilot case 2 is the human-robot collaborative task to inspect assemblies that are flagged as defective by the


As can be seen from the lanes in Figure 14, the process involves four different roles, which arepotentially performed by as many agents. The following technology is involved in this process.

• Cloud-based process management across multiple work cells.• IoT-enabled connectivity between cloud-based process management and multiple production agents.• Synchronized collaboration between smart robot and camera system to detect product defects.• Augmented reality to guide the human agent with an inspection of highly customizable assemblies.

Pilot case 2 is a complex scenario involving four process participants and various eventualitiesthat affect process execution. Communication from the factory floor is of utmost importance hereto allow the HORSE System, track progress, and adjust accordingly. Communication is facilitatedby handheld devices and sensors connected to the local network. The most compelling part of pilotcase 2 is the human-robot collaborative task to inspect assemblies that are flagged as defective by thecamera system. The Hybrid Task Supervisor module (see Section 3.5), in this case, is realized as a ROSapplication and uses state-machine models to synchronize the actions of the human and robot.



management and multiple production agents.l Synchronized collaboration between smart robot and camera

system to detect product defects.l Augmented reality to guide the human agent with an inspection

of highly customizable assemblies.Pilot case 2 is a complex scenario involving four process

participants and various eventualities that affect process execution. Communication from the factory floor is of utmost importance here to allow the HORSE System, track progress and adjust accordingly. Communication is facilitated by handheld devices and sensors connected to the local network. The most compelling part of pilot case 2 is the human-robot collaborative task to inspect assemblies that are flagged as defective by the camera system. The Hybrid Task Supervisor module (see Section 3.5), in this case, is realized as a ROS application and uses state-machine models to synchronize the actions of the human and robot.

As with the first pilot case, pilot case 2 involves automation of some tasks in the process. Picking and placing is automated, but a human operator is still necessary to respond to detected defects and ensure that packaging material is available. This automation is beneficial but constraining. It is beneficial to the health of the human because the repetitive lifting and manipulation of heavy parts is eliminated. However, the robot can only handle one assembly at a time, which forces it to wait for the human if he or she is not immediately available. Therefore, the introduction of the HORSE System and associated smart technologies, in this case, improves operator health but not necessarily process performance.

5.3. Pilot case 3: Separation of castingsThe third pilot site of the HORSE Project is a large foundry in

Poland. The foundry uses sand casting to produce a large range of products for several industries including automotive, rail transport and industrial equipment. Due to excess metal in the mold, four to eight castings are physically attached to each other after solidification.

Those castings must be separated before further surface processing is performed. The separation can be automated. However, the high customizability of the product makes it infeasible to define cutting plans for all products. Each production run is ostensibly unique and, therefore, necessitates a new cutting plan. To handle such high product variability, a robot equipped with teaching-by-demonstration technology is deployed to lift some of the human operator. The operator physically moves the arm and the end-effector of the robot through the necessary cutting trajectories to teach the cutting plan instead of labor intensive approaches such as computer-aided manufacturing modeling or programming. Once the cutting plan is recorded, then the operator enables an execution mode to process the batch of products. Figure 15 shows the model used to enact the grape separation process, which highlights the teaching-by-demonstration task. Additional information and photos of pilot case 2 in operation are available online (http://www.horse-project.eu/Pilot-Experiment-2).

Although teaching-by-demonstration is the most noticeable feature of this process, several other technologies contribute to such a smart manufacturing process. The following technologies are involved in this process.l Cloud-based process management to invoke the actions of humans,

robots and computer services.l IoT-enabled connectivity between cloud-based process

management and multiple production agents.l Synchronized collaboration between human and robot during the

teaching-by-demonstration task.l Smart robotics with human intrusion detection to halt the execution

in the case of safety risks.Pilot case 3 is a clear demonstration of smart manufacturing with

the utilization of teaching-by-demonstration and modern process management technology. More importantly, these technologies are demonstrated in an environment that is not particularly conducive to sophisticated and delicate computer systems. The factory floor is

Machines 2018, 6, 62 19 of 25

As with the first pilot case, pilot case 2 involves automation of some tasks in the process. Pickingand placing is automated, but a human operator is still necessary to respond to detected defectsand ensure that packaging material is available. This automation is beneficial but constraining. It isbeneficial to the health of the human because the repetitive lifting and manipulation of heavy partsis eliminated. However, the robot can only handle one assembly at a time, which forces it to waitfor the human if he or she is not immediately available. Therefore, the introduction of the HORSESystem and associated smart technologies, in this case, improves operator health but not necessarilyprocess performance.

5.3. Pilot Case 3: Separation of Castings

The third pilot site of the HORSE Project is a large foundry in Poland. The foundry usessand casting to produce a large range of products for several industries including automotive,rail transport, and industrial equipment. Due to excess metal in the mold, four to eight castingsare physically attached to each other after solidification. Those castings must be separated beforefurther surface processing is performed. The separation can be automated. However, the highcustomizability of the product makes it infeasible to define cutting plans for all products. Eachproduction run is ostensibly unique and, therefore, necessitates a new cutting plan. To handle suchhigh product variability, a robot equipped with teaching-by-demonstration technology is deployedto lift some of the human operator. The operator physically moves the arm and the end-effectorof the robot through the necessary cutting trajectories to teach the cutting plan instead of laborintensive approaches such as computer-aided manufacturing modeling or programming. Oncethe cutting plan is recorded, then the operator enables an execution mode to process the batch ofproducts. Figure 15 shows the model used to enact the grape separation process, which highlights theteaching-by-demonstration task. Additional information and photos of pilot case 2 in operation areavailable online (http://www.horse-project.eu/Pilot-Experiment-2).


camera system. The Hybrid Task Supervisor module (see Section 3.5), in this case, is realized as a ROS application and uses state-machine models to synchronize the actions of the human and robot.

As with the first pilot case, pilot case 2 involves automation of some tasks in the process. Picking and placing is automated, but a human operator is still necessary to respond to detected defects and ensure that packaging material is available. This automation is beneficial but constraining. It is beneficial to the health of the human because the repetitive lifting and manipulation of heavy parts is eliminated. However, the robot can only handle one assembly at a time, which forces it to wait for the human if he or she is not immediately available. Therefore, the introduction of the HORSE System and associated smart technologies, in this case, improves operator health but not necessarily process performance.

5.3. Pilot Case 3: Separation of Castings

The third pilot site of the HORSE Project is a large foundry in Poland. The foundry uses sand casting to produce a large range of products for several industries including automotive, rail transport, and industrial equipment. Due to excess metal in the mold, four to eight castings are physically attached to each other after solidification. Those castings must be separated before further surface processing is performed. The separation can be automated. However, the high customizability of the product makes it infeasible to define cutting plans for all products. Each production run is ostensibly unique and, therefore, necessitates a new cutting plan. To handle such high product variability, a robot equipped with teaching-by-demonstration technology is deployed to lift some of the human operator. The operator physically moves the arm and the end-effector of the robot through the necessary cutting trajectories to teach the cutting plan instead of labor intensive approaches such as computer-aided manufacturing modeling or programming. Once the cutting plan is recorded, then the operator enables an execution mode to process the batch of products. Figure 15 shows the model used to enact the grape separation process, which highlights the teaching-by-demonstration task. Additional information and photos of pilot case 2 in operation are available online (http://www.horse-project.eu/Pilot-Experiment-2).

Figure 15. Grape separation process model of pilot case 3.

Although teaching-by-demonstration is the most noticeable feature of this process, several other technologies contribute to such a smart manufacturing process. The following technologies are involved in this process. • Cloud-based process management to invoke the actions of humans, robots, and

computer services. • IoT-enabled connectivity between cloud-based process management and multiple

production agents. • Synchronized collaboration between human and robot during the

teaching-by-demonstration task. • Smart robotics with human intrusion detection to halt the execution in the case of safety risks.


Although teaching-by-demonstration is the most noticeable feature of this process, several othertechnologies contribute to such a smart manufacturing process. The following technologies areinvolved in this process.

• Cloud-based process management to invoke the actions of humans, robots, and computer services.• IoT-enabled connectivity between cloud-based process management and multiple production agents.• Synchronized collaboration between human and robot during the teaching-by-demonstration task.• Smart robotics with human intrusion detection to halt the execution in the case of safety risks.



Machines 2018, 6, 62 21 of 25


Figure 16. HORSE technology stack expanded to include multiple sites.

Cross-organizational manufacturing is an ongoing research topic due to its numerous potential benefits [40,41]. The concept has been demonstrated in the CrossWork project [42,43], albeit without complete vertical integration down to the factory floor as in the HORSE project. In the CrossWork project, multiple manufacturing enterprises in the same supply chain network use a single, centralized process management system to synchronize their activities. These connected enterprises temporarily form an instant virtual enterprise, i.e., non-permanent collaborations with the sole purpose of producing a single product series. The CrossWork approach has been prototyped in the automotive industry, which provides a similarity with one of the pilot cases of the HORSE project. The CrossWork concept is illustrated in Figure 17 and it shows a global process that flows across four autonomous enterprises in a single supply chain network [42,43].

Figure 17. Networked manufacturing process crossing the boundaries of multiple organizations.





Figure 17. Networked manufacturing process crossing the boundaries of multiple organizations. Figure 17. Networked manufacturing process crossing the boundaries of multiple organizations.

An alternative approach that allows more distribution of autonomy is the use of a multi-tenantSaaS process management solution that embodies the global execution module of HORSE for severalenterprises in a chain and that also synchronizes the links between their manufacturing processes.Both alternatives lead to networked process management [44]. This is a relevant development sinceinter-organizational processes in manufacturing are receiving more attention in an Industry 4.0setting [45,46]. This trend is required because of increasing product complexity, increasing producerspecialization, and increasing mass customization of products [1].

Machines 2018, 6, 62 21 of 25




Figure 17. Networked manufacturing process crossing the boundaries of multiple organizations.





Figure 17. Networked manufacturing process crossing the boundaries of multiple organizations. Figure 17. Networked manufacturing process crossing the boundaries of multiple organizations.

An alternative approach that allows more distribution of autonomy is the use of a multi-tenantSaaS process management solution that embodies the global execution module of HORSE for severalenterprises in a chain and that also synchronizes the links between their manufacturing processes.Both alternatives lead to networked process management [44]. This is a relevant development sinceinter-organizational processes in manufacturing are receiving more attention in an Industry 4.0setting [45,46]. This trend is required because of increasing product complexity, increasing producerspecialization, and increasing mass customization of products [1].

Figure 16. HORSE technology stack expanded to include multiple sites. Figure 17. Networked manufacturing process crossing the boundaries of multiple organizations.

teaming with fine dust particles from the grinding operations. In this case, the cloud-based nature of the HORSE System proves valuable because only the robot is exposed to the dusty environment. However, this proves to be a limiting factor as well because the operator does not have access to any troubleshooting functionality. If something goes wrong, production management must be contacted to address the problem. Thus, Industry 4.0 technologies present many opportunities but also many new considerations when deploying smart technologies.

6. inter-Organizational Process PerspectiveThus far, we explored how parts of the HORSE system can be

deployed to achieve advantages in software management and computing infrastructure investments, i.e., advantages that do not influence the nature of the functionality offered. The focus of these developments is within one manufacturing enterprise. However, the technology employed in the HORSE System may offer improved functionality: cloud computing and the IoT can improve interoperability within manufacturing networks where a manufacturing process takes place across multiple sites or even autonomous enterprises.

Software modules that are used in a SaaS paradigm are typically of a standardized kind so that the same functionality can be used by multiple parties. Applying functional standardization to the modules at the global layer of the HORSE System in the context of multiple manufacturing enterprises that collaborate can improve interoperability between those enterprises. Consequently, it may be simpler to set up supply chains or supply networks with automated support for manufacturing processes (and related enterprise processes as discussed in Section 3.1) across enterprises. The current realization of the global layer of the HORSE System is run as a single application instance with multiple tenants for the three pilot cases. While the current pilot cases do not participate in the same supply chain, it does prove the feasibility of a single global layer serving

multiple instances of the local layer across geographically separated sites. Figure 16 shows an expansion of the HORSE technology stack (see Figure 12) to include two sites with a different, illustrative set of things/devices.


An alternative approach that allows more distribution of autonomy is the use of a multi-tenant SaaS process management solution that embodies the global execution module of HORSE for several enterprises in a chain and that also synchronizes the links between their manufacturing processes. Both alternatives lead to networked process management [44]. This is a relevant development since inter-organizational processes in manufacturing are receiving more attention in an Industry 4.0 setting [45,46]. This trend is required because of increasing product complexity, increasing producer specialization and increasing mass customization of products [1].

7. ConclusionsThis article outlines the architecture design of the HORSE System.

It is shown how structured, hierarchical design produces a modular architecture with clearly defined subsystems and interfaces. The system embodies the assimilation of traditional enterprise information


systems (e.g., explicit process management) and advanced manufacturing technology (e.g., human-robot collaboration and the IoT). The HORSE System architecture is proposed as a reference architecture for a manufacturing operations management system for Industry 4.0. Thus, the architecture model of the HORSE System serves two purposes: (1) it can be used as a template or framework to position and develop a commercial-grade manufacturing operations management system for Industry 4.0 and (2) it helps frame the scientific inquiry into the management of manufacturing operations involving cloud computing, the IoT and smart devices.

The proposed reference architecture model is the main contribution of this paper. Due to its complexity, it is presented and discussed in two views including the logical and physical views. The logical view details the functional operation of the system without any commitment to specific technologies. The logical view is elaborated at four levels of aggregation (two additional levels are covered in the full design report [29]). The physical view explores how the various technologies enabling Industry 4.0 are used to realize the HORSE System. Each module of the system is considered in order to determine whether it can be hosted in the cloud. To summarize, we show the leading drivers for cloud deployment for each of the four HORSE main sub-systems (as in Figure 6) in Table 3.

Machines 2018, 6, 62 22 of 25

7. Conclusions

This paper outlines the architecture design of the HORSE System. It is shown how structured,hierarchical design produces a modular architecture with clearly defined subsystems and interfaces.The system embodies the assimilation of traditional enterprise information systems (e.g., explicitprocess management) and advanced manufacturing technology (e.g., human-robot collaboration andthe IoT). The HORSE System architecture is proposed as a reference architecture for a manufacturingoperations management system for Industry 4.0. Thus, the architecture model of the HORSE Systemserves two purposes: (1) it can be used as a template or framework to position and develop acommercial-grade manufacturing operations management system for Industry 4.0 and (2) it helps framethe scientific inquiry into the management of manufacturing operations involving cloud computing,the IoT, and smart devices.

The proposed reference architecture model is the main contribution of this paper. Due to itscomplexity, it is presented and discussed in two views including the logical and physical views.The logical view details the functional operation of the system without any commitment to specifictechnologies. The logical view is elaborated at four levels of aggregation (two additional levels arecovered in the full design report [29]). The physical view explores how the various technologiesenabling Industry 4.0 are used to realize the HORSE System. Each module of the system is consideredin order to determine whether it can be hosted in the cloud. To summarize, we show the leadingdrivers for cloud deployment for each of the four HORSE main sub-systems (as in Figure 6) in Table 3.

Table 3. Leading drivers for cloud deployment.

Design-Time Execution-Time

Global layer Flexibility AvailabilityLocal layer Configurability Responsiveness

The HORSE System has three notable embedded characteristics that have potential advantagesand disadvantages. First, cloud computing is emphasized as a fundamental enabling technology of thesystem. Cloud-based software lowers the barriers-to-entry for Industry 4.0. SMEs can focus on the useof smart devices to solve problems on the factory floor instead of being concerned with the installationand maintenance of software for those smart devices. However, cloud-based software especially withthe SaaS model limits the flexibility available to SMEs. It is more time-consuming or expensive tomake changes to a SaaS application than an application entirely under the control of the enterprise.

Second, the execution-time subsystem of the local layer of the system delivers real-time,sub-second synchronization between humans and robots. The time-critical nature of human-robotcollaboration results in a clear separation between the cloud-based modules and modules for directcontrol of the things/devices on the factory floor. This separation contributes to the modular natureof the reference architecture and allows for technology heterogeneity on the factory floor. Multipleinstances of local execution can be realized with different technology underpinnings. However, theseparation places further importance on the reliability of Internet connectivity. If the cloud-based orInternet connectivity services are unavailable, all operations will be uncoordinated at best or suspendedat worst.

Lastly, cross-functional, configurable manufacturing process management opens new ways forsmart manufacturing by supporting flexible process definitions, dynamic allocation of tasks to humanand robotic workers, and real-time coupling of work cell events for manufacturing processes. Suchprocess management processes also hold promise beyond a single site or enterprise. Cloud-basedprocess management supports improved interoperability in manufacturing chains and networks.

The HORSE Project is still ongoing and seeks to further enhance the system that bears its name.The design science research approach adopted in the project ensures practical relevance but it alsoincreases the risk of overlooked problems or missed opportunities. The system design is informed by

The HORSE System has three notable embedded characteristics that have potential advantages and disadvantages. First, cloud computing is emphasized as a fundamental enabling technology of the system. Cloud-based software lowers the barriers-to-entry for Industry 4.0. SMEs can focus on the use of smart devices to solve problems on the factory floor instead of being concerned with the installation and maintenance of software for those smart devices. However, cloud-based software especially with the SaaS model limits the flexibility available to SMEs. It is more time-consuming or expensive to make changes to a SaaS application than an application entirely under the control of the enterprise.

Second, the execution-time subsystem of the local layer of the system delivers real-time, sub-second synchronization between humans and robots. The time-critical nature of human-robot collaboration results in a clear separation between the cloud-based modules and modules for direct control of the things/devices on the factory floor. This separation contributes to the modular nature of the reference architecture and allows for technology heterogeneity on the factory floor. Multiple instances of local execution can be realized with different technology underpinnings. However, the separation places further importance on the reliability of Internet connectivity. If the cloud-based or Internet connectivity services are unavailable, all operations will be uncoordinated at best or suspended at worst.

Lastly, cross-functional, configurable manufacturing process management opens new ways for smart manufacturing by supporting flexible process definitions, dynamic allocation of tasks to human

and robotic workers and real-time coupling of work cell events for manufacturing processes. Such process management processes also hold promise beyond a single site or enterprise. Cloud-based process management supports improved interoperability in manufacturing chains and networks.

The HORSE Project is still ongoing and seeks to further enhance the system that bears its name. The design science research approach adopted in the project ensures practical relevance but it also increases the risk of overlooked problems or missed opportunities. The system design is informed by the real-world problems encountered by the three pilot cases and every attempt is made to extrapolate to more general problems. However, completely unrelated problems may exist in other factories, which may not be considered in the HORSE Project. Seven additional cases were subsequently added to the HORSE Project to evaluate the effectiveness of the HORSE System and to identify any shortcomings.

Lastly, as a research and innovation project, the HORSE Project did not create a commercial-grade manufacturing operations management system. Instead, a prototype was developed and implemented to demonstrate the feasibility of a system incorporating cloud computing, the Internet-of-things and smart devices. Further complications will undoubtedly arise on the quest for a commercial-grade system, but, at least, the current system architecture can serve as a proven template for such a development.

Author ContributionsConceptualization, J.E. and P.G. Funding acquisition, P.G. and

I.V. Investigation, J.E., P.G. and I.V. Methodology, J.E. and P.G. Project administration, I.V. Software, J.E. and K.T. Validation, J.E. and K.T. Visualization, J.E. Writing—original draft, J.E. and P.G. Writing—review & editing, I.V. and K.T.

FundingThe HORSE project received funding from the European Union’s

Horizon 2020 Research and Innovation Program under Grant Agreement No. 680734.

AcknowledgmentsAll members of the HORSE Architecture Team are acknowledged

for their contribution to the system design process.

Conflicts of interestThe authors declare no conflict of interest.

Referencesdoi: 10.3390/machines6040062

originally published in: Machines

Published by MDPIdoi: 10.3390/machines6040062

Reproduced under CC BY Licence


The World Robotics Report 2018, published by the

International Federation of Robotics (IFR), revealed that sales of industrial robots in India reached the new record of 3,412 new units installed in 2017. That is an increase of 30 per cent compared to 2016. Between 2012 and 2017, India saw a compound annual sales growth rate of 18 per cent. Broken down by industry, India’s automotive sector is the main customer with a share of 62 per cent of the total supply. Sales rose by 27 per cent in 2017 compared to the previous year.

“The automotive industry will remain the main driver of the increasing robot installations in India,” says Junji Tsuda, president of the IFR. “Numerous new

projects are announced by the international and domestic car manufacturers aiming to expand production capacities. Moreover, OEMs increasingly require local supply of automotive parts.”

Since 2009, the number of robot installations has been growing rapidly in

India. In 2017, India ranked No. 14 regarding the global annual supply, following Thailand and Spain. Regarding the operational stock, India ranked thirteenth following Canada, Spain and Singapore. India has a rather low industrial robot density figure of 85 per 10,000 employees in the automotive industry. This number is less than one-fourth of Indonesia´s density (378 units) and far away from China´s (505 units).

News

KVM switch manufacturer Aten turns 40

ATEN reached another milestone this year, celebrating 40 years of successful global business. Initially established as HOZN

Automation Co. Ltd. in 1979 and later renamed in 1988 as ATEN International Co. Ltd, ATEN is one of the largest KVM switch manufacturers in the world. All ATEN products are ISO 9001 and ISO 14001 certified.

Sunayana Hazarika, Manager, Marketing and Branding, at ATEN India said "We have come a long way and stood the test of

time and have come up as leaders within the industry. The key to our perpetuity is in our ability to provide sustainable solutions that truly benefit our customers. Bringing the latest technology to our customers ensures our customers get a

dependable customer experience."The ATEN brand consists of innovative

solutions applied to connectivity, professional audio/video and green energy, for consumers, small/home offices (SOHO), small to medium sized businesses (SMB), and enterprise customers. The company distributes its products to Asia, America, Europe and other markets worldwide and has received the 15th "National standardization - corporate standardization" award by the Bureau of Standards, Metrology and Inspection, MOEA, which is the highest recognition for the standardization process followed by all of ATEN employees.

ATEN endeavors to deliver high-quality, zero-defect and cost-effective products and services to customers with faster technological design, manufacturing service, risk management and product quality protection responsibility focusing on its passion for excellence.

Industrial robot sales reach record level in India

Automation, Ai to triple india factory output to usD 1 trillion by 2025

Junji Tsuda, president of the IFR

Dilip Sawhney, Managing Director, Rockwell Automation India

Sunayana Hazarika, Manager,Marketing and Branding, at ATEN

Industrial automation and artificial intelligence (AI) will triple India's factory output

from USD 350 billion in 2018 into a little more than USD 1 trillion by 2025, said a study by Rockwell Automation, a service provider for industrial automation and infotech for manufacturing industries.

“We want to take the lead in building India’s first industry 4.0 ecosystem of partner companies that will create solutions for 'The Connected Enterprise', which can analyze machine conditions in advance to avoid breakdowns and enhance productivity and improve quality and compliance parameters,”

said Dilip Sawhney, Managing Director, Rockwell Automation India. Rockwell, which employs 23,000 people globally, said that the company was expanding its partner network in India to allowing local manufacturers to access

a collaborative network of companies focused on developing, implementing and supporting manufacturing solutions across industry verticals. Rockwell made the announcement at the annual 2019 Rockwell Automation on the Move conference, which showcases the latest digital transformation solutions.


Beyond Factory Walls

Five of the World's Most Incredible Road Trips Road trips are the ultimate form of travel. everyone knows travelling is about the journey, not the destination. this article lists five amazing destinations for awesome road trips.

Exploring the world by road is one of the best ways to travel, as often the journey is more memorable than the final destination. Experiencing the ups and downs of

travel with a group of friends in the close confines of a vehicle brings everyone together and forms bonds other types of travel doesn't. Deciding to embark on a road trip is easy, but with the furthest reaches of the world becoming more accessible, the hardest part of planning a trip is deciding where to go first.

1. Route 1, iceland Also known as the country's ring road, Iceland's Route 1

circles almost the entire island. The 1,332-kilometer stretch of tarmac starts in Reykjavik and connects nearly all of the Nordic country's inhabited areas while passing through some of the island's most stunning scenery along the way.

As one of the world's most beautiful drives, Route 1 takes road trippers past lava fields, mountains and glaciers. Iceland's

Travelling to Skeleton Coast through Damaraland, Namibia.


volcanic landscape is notoriously wild and rugged, but travellers can see it in style, behind the wheel of their car or van.

2. the karakoram Highway, China, india, Pakistan

The Karakoram Highway is a mainstay of road tripper bucket lists everywhere. It's the highest international paved road in the world and links China and Pakistan via the Karakoram mountain range from which it gets its name. The highway naturally offers incredible views of the landscape through which it travels, but it is also home to many pieces of rock art along its route which date back hundreds of years.

The Karakoram Highway is well known as a gruelling challenge for cyclists and is also home to the highest border crossing in the world. Travellers making the journey by car still may need time to adjust to the altitude of this truly awesome road trip.

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3. Route 61, UsA Route 61, while not as famous as its counterpart

Route 66, is a must-see for music lovers as well as more traditional travellers. The 2,300-kilometer highway runs between Wyoming, Minnesota and New Orleans, Louisiana, following the mighty Mississippi River along the way.

Route 61 is perfect for road trippers with a love of music and a good time. Also known as the Blues Highway, it passes through the blues capital of Memphis on its way south and ends in the party town of New Orleans.

4. the Pan-American Highway The Pan-American Highway is the longest driving

route in the world. Stretching from the southern-most tip of South America all the way to the northern US state of Alaska, this road trip is not for the faint hearted or having less time to spare. Passing through many different countries and ecosystems, the experience of driving the Pan-Am is as varied and diverse as the landscape itself.

However, the Pan-American Highway isn't technically just a road trip, as a small stretch of rainforest between Panama and Colombia necessitates the use of a boat to travel between the two countries. Boat or no boat, the Pan-American Highway is undoubtedly one of the world's greatest journeys.

5. skeleton Coast, Namibia The Skeleton Coast's name is not even the coolest

thing about it. The coast, named after the large number of historic shipwrecks off of it, is a surreal and isolated strip of mist covered sand-dunes. Driving along the Skeleton Coast is an experience like few others, as the dramatic and changing landscape captivates and impresses.

Lava ridges and granite peaks break out of the sand dunes periodically and animal-loving road trippers can spot rhinos and elephants from the safety of their vehicles. This trip along Africa's southwest coast is surreal and unmissable.

Road trips are a great way to see the world. Taking the time to explore and discover new routes is a wonderful part of road tripping and researching destinations is just the start of many a great adventure.

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Date post:	14-Aug-2020
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