DRIVING PAYBACK THROUGH INDUSTRIAL INTERNET OF THINGS

In this issue

Driving Payback Through Industrial Internet of Things 2

IIoT Platforms in an Industrial Environment 3

The Layers of an IIoT Platform 4

Identification of Key Use Cases 5

Applicability and Payback Period of IIoT Use Cases for Specific Verticals 7

Principles That Govern a Successful IIoT Implementation 12

Methodology 15

Research from Gartner: Survey Analysis: As More Companies Deploy IoT, They Increasingly Focus on Best Practices and Payback 16

About Altizon 24

Driving Payback Through Industrial Internet of Things

The Internet of Things or IoT is a network of physical

devices or ‘things’ that are interconnected and can

exchange information about their operation and about

the environment in which they function.

The application of IoT to industrial assets is termed

as The Industrial Internet of Things or IIoT. These

assets could be within a manufacturing facility,

such as manufacturing plants or remote assets

distributed across a geographical area. IIoT platforms

are specialized forms of generic IoT platforms that

offer enhanced functionality in asset connectivity,

data analytics and AI models that are more suited to

industrial assets.

The rise of IIoT has also given rise to several business

initiatives that are interrelated and significantly

overlap, as outlined below.

■ Smart Manufacturing: Connecting manufacturing

processes and assets and using analytics for

improved performance and quality

■ Digital Transformation: Digitizing and

transforming processes, specifically in related

manufacturing operations

■ Smart Factories: Building a production facility that

is connected and leverages smart manufacturing

and digital transformation

■ Industry 4.0: An umbrella term that is equivalent

to smart factory

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IIoT platforms act as a foundation layer for these

initiatives.

The rapid expansion of IoT and its wide application

in smart manufacturing can be attributed to

advancement in several complimentary areas.

■ Network: Improving data bandwidth along with

the rise of efficient and low power networking

and communication protocols such as LoRa and

ZigBee that are suited for connected devices

■ Sensors: Robust and affordable sensor

technology that has the ability to communicate

built in

■ Cloud Computing: On-demand availability of

compute and storage resources that can process,

store and analyze the tremendous amount of

information generated by IoT

■ Edge Computing: Technology that enables

processing of sensor data and related

information close to its source thus saving on

bandwidth and improving response time

■ Big Data Analytics: Software frameworks and

analysis techniques that can be used to make

sense of the tremendous amounts of data being

generated by IoT

■ Machine Learning: Advancements in AI that let

you build a ‘digital twin’ of an asset and predict

outcomes on how that asset will behave

The focus of this whitepaper is to help provide a clear

perspective to organizations on IIoT, offer insights on

realizing payback across various verticals and provide

a framework for a successful IIoT implementation.

IIoT Platforms in an Industrial Environment

An industrial environment is extremely complex with

several elements that can be broadly classified into:

■ Industrial Machinery and Equipment: These

perform the core functions and operations within

a factory

■ Sensors: These measure the performance of the

machinery and the environment or condition in

which they function

■ Automation, Control and Operations Systems:

These are software systems such as SCADA,

DCS, MES, Historians and Quality Management

Systems that are used for the day-to-day

operations of a plant

■ IT Systems: These are software systems used

for managing business processes – these include

PLM, ERP and Business Intelligence solutions

In this multi-process milieu, an IIoT platform acts

as a convergence layer, with IT Systems on one side

and Operations Systems, machines and sensors on

the other. Information from these systems is unified

to create a manufacturing operations data lake.

Analysis of this data allows for wide-ranging and

global decision making.

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Existing IT and Operations Systems have started

acquiring IIoT platform-like capabilities. Such

approaches, however, are topical at best and

cannot scale as the scope of the implementation

expands. It is, therefore, important to understand

what constitutes an IIoT platform so that the right

parameters for putting the system in place are

established.

The Layers of an IIoT Platform

An IIoT platform consists of the following layers or

sub-components:

The Edge

The Edge is the software component installed at the

edge of the network, close to the source of data. It

might run on dedicated IoT gateway hardware or on

servers within a plant network.

The Edge layer is responsible for collecting data from

machines and operations systems and sending it

reliably to the manufacturing data lake. IIoT data can

be extremely large so the Edge is also responsible for

processing the data locally for improved throughput

and scale. The Edge can perform advanced

analytics and AI, and has the ability to power local

applications.

The Manufacturing Data Lake

The Manufacturing Data Lake is the repository for all

machine and manufacturing operations data across

the enterprise. The Edge layer streams data in real-

time to this layer. It is necessary for the Data Lake

to be highly scalable to deal with the sheer volume

of data.

Advanced Analytics and Machine Learning

This layer provides the ability to analyze

manufacturing operations data. It is powered by

big-data technology designed to handle the volume,

velocity and variety of data across the enterprise.

This layer should have the ability to perform

standard and advanced analytics. It should also have

the capability to build machine learning models

of the data to create a digital twin of the asset or

operation being analyzed.

Figure 1. IIoT Platform in an Enterprise

Source: Altizon

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Manufacturing Intelligence

This is a vertical-specific layer that provides standard

implementations of critical KPIs across Productivity,

Quality, Planning and Maintenance. It allows for

rapid implementation and fast payback for IIoT in an

enterprise.

Integrations

The integrations layer provides standard modules

to rapidly connect manufacturing operations

data to IT systems such as ERP, PLM and BI.

Open data accessibility is key to a successful IIoT

implementation.

Security and Deployment Flexibility

It is important for an IIoT platform to run on any

infrastructure — including all major public and

private cloud vendors. It should also have the ability

to be deployed fully on-premise. The platform should

be multi-tenant, allowing for rapid rollouts across

the enterprise. The platform should also be fully

compliant when it comes to global information

security standards.

Having established the layers of an IIoT platform, it

is important to identify those use cases that will help

drive payback.

Identification of Key Use Cases

Within manufacturing plants, the following IIoT use

cases have the most significant business impact:

Productivity: Significant system-level throughput

improvement with optimal conversion cost

■ Real-time and accurate measurement of Overall

Equipment Effectiveness (OEE) of critical

machines and bottlenecks

■ Identification of top reasons behind unplanned

downtime, followed by the identification of root

causes to minimize them

■ Instantaneous production monitoring, booking

and inventory updates in ERP/Planning Systems

■ Close monitoring and control to sustain actions

for improvement

Figure 2. Sub-Components of an IIoT Platform

Source: Altizon

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Predictive Maintenance: Extended asset life,

reduced downtime and reduced cost of spare parts

and tooling

■ Real-time monitoring of critical machine

parameters, enabling a change from time-based

to condition-based maintenance followed by

predictive maintenance

■ Applying machine learning techniques on

historical operational data of an asset to build

a digital twin – this represents the normal

operating condition of the asset

■ Leveraging the twin to predict failure when the

performance of the asset deviates from normal

■ Multi-dimensional modeling where data from

traditional techniques such as vibration

monitoring, thermography, tribology and visual

inspection is correlated with machine operating

variables such as load, speed and product

quality, leading to the root cause of mechanical

and electrical failures

Quality: Significant reduction in direct material costs

and expenses owing to poor quality; improvement in

overall rolled throughput yield and cutback on rework

■ Real-time monitoring of machine process

parameters that impact part quality

characteristics

■ Correlation between input – process – output

leading to a more informed root cause and CAPA

analysis

■ In-process Poka-Yoke to eliminate product

defects

■ Machine learning-based models to predict

potential quality failures and prescription of

settings to avoid them

■ Digital audit compliance and baseline for FMEA

Genealogy and Traceability: Significant reduction

in liabilities linked to non-compliance with various

standards

■ Linking of critical process and operational data

to the product as made in the manufacturing

value chain from upstream to downstream

■ Early warning signals of potential process

capability deterioration

■ Complete operations traceability that enables

compliance in the event of product quality audit,

withdrawal or recall

■ Extension of the system to critical part suppliers

in the supply chain

Energy and Utilities: Significant reduction in energy

and utilities consumption leading to lower conversion

cost, reduction in system-level energy leakages,

compliance with ISO 50001 and lower carbon

footprint

■ Monitoring and analysis of critical utilities such

as electrical energy, compressed air, steam and

water

■ Establishment of specific consumption patterns

across product categories

■ Analysis and identification of opportunities for

reduction in consumption

■ Deployment of sensors at critical points in the

energy network to identify leakages in real-time

■ Prediction of sub-optimal consumption patterns

Planning: Leveraging operations data for improved

planning reliability and effectiveness, enhanced

fill rate, dynamic master data updates and better

customer satisfaction

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■ Real-time visibility into status of planned orders

and likely deviations from promised due dates

■ Notification on significant events that may need

re-planning

■ Monitoring and reporting of significant changes

in master data linked to bill of materials,

routings and lead times

■ Machine learning-based decision support system

for planning and scheduling optimization

■ Enabling a system to reach near-zero latency

state in supply chain planning and optimization,

problem identification, re-planning for feasibility

and optimality, and implementation of changes

Having identified these IIoT use-cases, their

applicability and potential to generate rapid payback

across various industry verticals can be examined.

Applicability and Payback Period of IIoT Use Cases for Specific Verticals

Altizon has been closely working with manufacturing

companies across various industrial verticals since

2013. In this period, repeatable patterns have

emerged on the kind of problems that IIoT can

solve. This section provides an analysis of problem

classification and payback periods for key use cases

across Automotive, Tire, Chemicals, Textiles and CPG.

Automotive Plants

In this world of connected cars and electric

powertrains, automotive plant operations are gearing

to go digital across their entire supply chain. The

adoption of industrial IoT has mainly been focused

around removing the bottlenecks in operations and

controlling conversion costs.

Productivity is the most prevalent use case followed

by predictive maintenance.

Productivity has been the use case with the earliest

payback period of 6 months on average followed by

Quality and Traceability.

Figure 3.0 Use Case Percentage in Automotive Plants

Source: Altizon

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Figure 4. Automotive Use Cases With Associated Payback Period in Months

Source: Altizon

Figure 5. Use Case Percentage in Tire Plants

Source: Altizon

Tire Plants

Electric vehicles are presenting a unique opportunity to the tire industry in design and manufacturing.

Increasing product variety, volatile raw material prices and asset and energy-heavy production

processes are putting significant pressure on traditional tire manufacturers to innovate.

Altizon has deployed its platform at multiple tire manufacturing sites to introduce predictability in

operations, reduce energy and utilities cost, and improve productivity.

Energy and Utilities cost reduction in the Tire industry vertical has been the use case with the

earliest payback period of 10 months on average.

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Figure 6. Tire Manufacturing Use Cases With Associated Payback Period in Months

Source: Altizon

Chemical Manufacturing Plants

Chemical manufacturing plants engage in continuous process or batch manufacturing. Most of these

plants have been connected and controlled for decades with data being generated in silos.

Primary use cases at chemical manufacturing plants revolve around improving overall rolled

throughput yield, optimizing golden batch parameters, reducing energy and utilities consumption,

and predictive maintenance. Quality is the most prominent use case followed by energy and utilities

cost reduction. Quality Improvement in chemical manufacturing plants has been the use case with

the earliest payback period of 6 months on average.

Figure 7. Use Case Percentage in Chemical Manufacturing Plants

Source: Altizon

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Textile Manufacturing Plants

Textile manufacturing is characterized by increasing product variety, decreasing batch sizes and immense

pressure to reduce material and conversion cost. There is also an endeavor to reduce impact on the

environment and focus on employee health.

Primary use cases at textile manufacturing plants include unplanned downtime reduction and process

adherence. Productivity is the most prominent use case followed by quality and process adherence.

Textile plants often require capex investment in machine and infrastructure upgrades to be IIoT ready. Due to

this, average payback periods tend to be longer, with a minimum 15 months for all use cases.

Figure 8. Chemical Manufacturing Use Cases With Associated Payback Period in Months

Source: Altizon

Figure 9. Use Case Percentage in Textile Plants

Source: Altizon

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CPG

Predictability in CPG/Food/Beverage supply chain and operations is critical for the industry to improve sales

at the lowest possible cost. This industry usually has a growing product mix and small packaging sizes, with

customers demanding a digital trace to raw material sources and processing conditions. Industrial IoT use

cases in CPG span the supply side (raw materials processing, storage), core manufacturing and downstream

packaging operations.

Productivity optimization has been the most prominent use case in CPG (focused primarily on line speed and

changeover analysis) followed by Quality and Traceability.

Productivity has been the use case with the earliest payback period of 6 months on average followed by

product Genealogy and Traceability.

Figure 10. Textile Manufacturing Use Cases With Associated Payback Period in Months

Source: Altizon

Figure 11. Use Case Percentage in CPG Plants

Source: Altizon

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Principles That Govern a Successful IIoT Implementation

As organizations get started with their IIoT initiatives,

it is critical to keep best practices in mind to ensure

success and maximum payback. Here’s how:

■ Select one end-to-end qualified partner for the

complete digital transformation initiative.

■ Avoid over-strategizing. Define KPIs best suited

to the vertical in question and begin small. Fail

fast, learn faster.

■ Establish a strong Project Management Office

that is multi-disciplinary.

■ Use the first implementation to build a template

for every subsequent implementation. This

methodology will provide insights and highlight

issues in infrastructure, process and people,

which is its intended purpose.

■ Adhere to First Principles of Manufacturing

Excellence (Toyota Production Systems, Lean

Six Sigma, Industrial Engineering, Theory of

Constraints) to bring focus to the use case and

its benefits.

Figure 12. CPG Use Cases With Associated Payback Period in Months

Source: Altizon

■ Have local plant leaders and managers

take responsibility for behavioral change

management, training and implementation. The

IT and the Chief Digital Office should advise and

facilitate the plant’s IIoT journey.

■ Integrate with IT systems such as ERP, Planning,

QCM and CRM early on to ensure that operations

data forms an integral part of the decision

making process.

■ Start with the power of correlations before going

down the path of elusive root cause analysis.

■ Become an engineer again. In the long term,

the responsibility of change management must

shift from managers to engineers and should be

driven by data. Technical discipline matters.

Digital Transformation With DMAIC

Follow a DMAIC (Define – Measure – Analyze –

Improve – Control) data-driven approach to drive,

improve and stabilize business processes. DMAIC

is an integral part of Six Sigma and has proven to

be effective in IIoT-driven digital transformation

initiatives.

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Figure 13. DMAIC Model for IIoT

Source: Altizon

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The various stages of DMAIC are detailed below:

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Having a combination of technology, domain

understanding, process improvement techniques and

rapid deployment will help ensure a successful IIoT

implementation with payback that is predictable and

meaningful.

Methodology

The information presented is based on Altizon’s IIoT

driven Digital Transformation and Smart Factory

implementations. The primary research was done

using a combination of data analysis and workshops

with key customer personnel involved in these

initiatives.

Disclaimer: The results mentioned in this study is an

average across implementations in each vertical.

Source: Altizon

Survey Analysis: As More Companies Deploy IoT, They Increasingly Focus on Best Practices and Payback

Gartner survey results reveal enterprises have

an increasingly mature approach to Internet of

Things initiatives. Application leaders should

leverage these insights to help their enterprises

improve the rate of success and relevance for

their IoT projects.

Key Findings

■ Twenty-three percent of enterprises surveyed

had deployed Internet of Things (IoT)

projects before 2018, and another 37%

had deployed by year-end 2018 (YE18),

highlighting the still-relatively early stages of

IoT adoption.

■ Eighty-six percent of enterprises have

specified a time frame for financial payback

for their IoT projects. The average IoT project

requires three years to achieve financial

payback.

■ A quarter of enterprises are using

enterprisewide best practices for their

IoT initiatives, including key performance

indicators (KPIs), to track their business

outcomes and establishing a center of

excellence (COE) with responsibility for all IoT

implementations.

Recommendations

Application leaders responsible for IoT

implementations should:

■ Avoid making mistakes that are common to

complicated new IoT deployments by first

conducting market and internal project reviews

to identify lessons learned and best practices.

Research from Gartner

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■ Align IoT implementations with a well-defined

strategic business initiative by creating clear,

measurable goals, such as project payback

timelines and targets.

■ Mandate the use of best practices like the use of

KPIs, a COE and a shared solution architecture

to help improve the success of their IoT projects.

Strategic Planning Assumptions

By 2024, 35% of enterprises that invest in IoT

projects will set a payback target of one year or less,

up from 10% today.

By 2024, 90% of all companies that invest in IoT

projects will have well-defined KPIs to help them

measure the success of their IoT investments.

Survey Objective

The purpose of this survey was to test our

working hypotheses regarding the maturity of IoT

deployments. The main elements factored into this

maturity analysis of enterprises, across sectors from

government to retail to pharmaceutical to utilities,

included:

■ The year these enterprises deployed their first

IoT project and thus the time to develop IoT-

centered experience and skills

■ The use of financial payback targets for their

IoT projects and the average number of years

needed to achieve a payback

■ Best practices for use of KPIs, COEs and

an enterprise solution architecture for IoT

deployments

Results presented are based on a Gartner study

to help companies that implement IoT to better

understand what kinds of business benefits IoT

delivers and how they need to organize IoT to best

deliver on those benefits. The primary research was

conducted online from 15 May through 27 June

2019 among 511 respondents from the U.S. For

more details, see the methodology section.

Data Insights

Highlights of this survey indicate that, while IoT

deployments remain complex, key lessons and

best practices are emerging from enterprises that

have been deploying IoT capabilities, both in their

products and their operations. These include:

■ Enterprises increasingly adopt IoT but have

a limited history with it: While IoT adoption

is proliferating, 23% of enterprises that have

deployed it report doing so before 2018, which

means that experience with the innovation is still

relatively immature.

■ IoT has migrated from being a technology

initiative to a business initiative: A majority

of IoT implementers (86%) report specifying

a time frame for financial payback of their IoT

investments, and on the average the time frame

is three years.

■ Best practices are emerging for IoT initiatives:

Although implementing IoT is still a relatively

new endeavor, a majority of implementers of

IoT (61%) report adopting best practices (e.g.,

leveraging KPIs to monitor IoT success).

Enterprises Increasingly Adopt IoT but Have a Limited History With It

Enterprises working on IoT initiatives that cross

functional areas to build end-to-end IoT-enabled

business solutions often require time and practice

to build maturity. Enterprises need this time

and opportunity to practice across business unit

operational or engineering teams, the IT organization

and corporate leadership groups. Our hypothesis

from speaking with IoT implementers has been that

many types of IoT projects have been implemented

over the past few years, providing enterprises with a

core portfolio of lessons learned. But to gain more

insight into how experienced IoT implementers are

overall, we asked survey respondents when their

organization had completed or planned to complete

at least one IoT project. Figure 1 shows current

status of IoT use and year of first deployment.

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Key Survey Result Finding and Analysis

■ Finding: Respondents indicated that 75% of the

organizations surveyed had deployed IoT projects

by the first half of 2019. However, only 23% of

enterprises had deployed IoT projects before

2018.

■ Analysis: While IoT has been a topic of

interest for over a decade, these survey results

emphasize that:

Proportionately few enterprises to date have extensive experience with IoT.

■ This survey highlights the caution that a

limited percentage of enterprises have had

IoT deployments for more than one year. Few

enterprises have had a chance to conduct

extensive numbers of IoT projects across the

enterprise and obtain lessons learned. These

lessons would run the gamut of business,

cultural and technical range. Lessons could be as

basic as establishing the risk level of IoT projects

and the implications this has for the internal rate

Figure 1. Current Status of IoT Use, Year of First Deployment

of return these projects have to meet. It can be

as complex developing a strategy to socialize

the trustworthiness of IoT-generated data and

convincing line workers in another geography,

culture and language to use this in their daily

processes.

IoT implementations require time. Objectives need

to be set (business and financial). Assets need to

be connected. Data needs to be integrated into

business processes. People need to be trained. Since

few enterprises have extensive experience adopting

IoT-based digital technologies, this will take time

for all enterprises. IT has the opportunity to help

the business units drive digital transformation and

become more competitive and differentiated using

IoT capabilities.

Recommendations

Application leaders responsible for IoT enabling their

applications should:

■ Avoid making mistakes that are common to

complicated new IoT deployments by first

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conducting market and internal project reviews

to identify lessons learned and best practices.

This may require an audit of projects from your

business units and IT and engagement with

comparable companies to obtain their lessons

learned.

■ Use these lessons and best practices to develop

a step-by-step roadmap that starts small and

accelerates as you accrue expertise. Verify that it

reflects your business unit’s digital literacy.

■ Capture lessons learned in a knowledge bank,

and combine that information with lessons

from other enterprises conducting IoT projects.

Communicate lessons learned and success

cases extensively in the enterprise. Work with

executive leadership, both IT and business

units, to leverage these insights into your IoT

adoption roadmap. Mine your past for lessons of

cross-enterprise collaboration, particularly from

business transformation initiatives and from your

ERP implementations.

■ Work with IT leadership and the business unit

leadership to establish policies and procedures

to not just forgive failure but to reward the

implementers and to obtain the lessons learned

from these IoT deployments. Encourage agile

approaches and a fast-fail/fast-learn mentality.

IoT Has Migrated From Being a Technology Initiative to a Business Initiative

Historically, IoT initiatives have been the purview

of technology departments, focused on proving the

technology. Yet since the majority of enterprises

are driven by business results, IT leadership needs

to align how it views IoT projects. We hypothesized

that IoT projects have to provide a clear payback for

their costs and that they have to drive new business

capabilities.

Our starting hypothesis was that enterprises and

decision makers are getting smarter about planning

a time frame for the ROI related to their IoT

implementations and that over time we should expect

the IoT ROI to occur earlier. Figure 2 shows the time

frame for expected payback of an IoT project.

Figure 2. IoT Projects Require a Well-Defined Financial Payback

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Key Survey Result Finding and Analysis

■ Finding: A significant proportion of

organizations (86%) have specified a time

frame to achieve financial payback for their IoT

projects.

■ Analysis: This indicates the IoT projects

are increasingly no longer just technology

demonstration projects or proofs of concepts

(POCs). They are part and parcel of a business

unit’s set of initiatives that align to its business

objectives. In a business unit, meeting or

exceeding a financial payback target is just a

starting point for an initiative.

Business stakeholders, not IT, tend to own IoT projects, and their expectations of ROI reflect both business objectives and the realities of the enterprise.

These projects require scarce skilled employees.

So these IoT projects have to also meet key

business objectives for cost optimization or

process improvement or revenue generation and

so forth. This highlights the central tension for IoT

projects in general. These projects leverage internet

technologies, which tend to be the purview of IT. But

they are most often sponsored, approved and paid

for by the business unit that determines how fast

the enterprise will use these digital capabilities in its

business process.

■ Finding: Our survey respondents indicated that

an IoT project requires three years on average to

achieve financial payback. Data not shown: 32%

of organizations estimated they would achieve

financial payback for their IoT projects in one to

less than two years. Ten percent of organizations

estimated they would achieve financial payback

in less than one year for their IoT projects. Only

8% of enterprises estimated they would have

project financial paybacks that exceeded five

years.

■ Analysis: These results emphasize that, although

IoT proliferation remains in the early stages of

adoption on a global basis, organizations are

assessing IoT projects and competing for funding

using standard business metrics such as project

payback. This business payback is nuanced by

the usual sets of internal financial targets and

their risk analysis of IoT projects.

Time to payback expectations may be set too high

for enterprises still in the early phases of adoption.

The risk factors that may require a shorter

payback include the lack of IoT experience by the

enterprise, lack of trust for the vendors involved,

and the potential for the culture to reject IoT project

implementation. Internally focused IoT projects,

where the focus centers on operational efficiency,

may have short paybacks due to clearer objectives

with more parameters under the enterprise’s control.

Externally focused IoT projects, such as smart

connected projects, may have longer paybacks as

business leaders understand they have to educate

their customers on how to engage the product and

its data.

This puts pressure on the IoT team to have solutions,

not just IoT technologies, to meet enterprise

requirements. This requires understanding their

enterprise’s business, organizational and technology

maturity.

Recommendations

Application leaders responsible for IoT enabling their

applications should:

■ Develop a clear understanding of the enterprise’s

business objectives and the strategic imperatives

the CEO is espousing. You must work closely with

business stakeholders to develop IoT initiatives

and projects to ensure the outcomes align with

the strategy.

■ Build a roadmap of your enterprise’s IoT

maturity using a business, organizational and

technology framework to help frame the types of

IoT projects and the types of paybacks likely to

be needed.

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■ Seek guidance from the CFO’s team to establish

a clear set of financial payback metrics based

on analysis of the reality of IoT maturity of your

enterprise and the risk factors working against

success. Initially favor smaller, leaner projects

with as short as possible a payback so the

business unit and IT leaders can more quickly

achieve and emphasize successful outcomes (or

fail fast and learn from mistakes).

Best Practices Are Emerging for IoT Initiatives

While few enterprises have deployed IoT at scale,

there have been a variety of concerted efforts to

develop technology expertise for IoT initiatives. We

hypothesized that most enterprises are not mature

Figure 3. Enterprises Adopting Key Business, Organization, Architecture Metrics (Percent)

enough and have variances in maturity from a

business, cultural and technology perspective to

adopt IoT on a wide-scale basis. Thus, we asked

IoT implementers to what degree they had adopted

IoT project KPIs, used an IoT COE or adopted a

shared IoT solution architecture. Figure 3 shows

organizations’ overall maturity of IoT adoption.

Key Survey Result Finding and Analysis

■ Finding: Sixty-one percent of the participating

organizations conducting IoT projects indicated

they had a mature approach to IoT adoption.

These involved using key parameters for business,

organizational and technology maturity: KPIs, a

COE and a solution architecture.

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■ Analysis: Enterprise IT organizations are

leveraging best-practice approaches that have

worked for them in other IT initiatives such

as adopting cloud computing or deployment

of consolidated business applications (e.g.,

ERP). This is a fundamental requirement for

IoT as it requires extensive coordination and

engagement among IT, the operating and

business unit elements of the enterprise, and

senior leadership. Thus, KPIs enable IoT projects

to focus on business outcomes. A COE approach

facilitates sharing goals, lessons and resources,

and it provides a venue to resolve disagreements

among stakeholders. Finally, an IoT solution

architecture points to opportunities to optimize

technical deployments and to help avoid

reinventing the wheel throughout the enterprise.

A caution on this measure of maturity is that

we may see these best practices appear in only

parts of the enterprise or even just in IT.

Recommendations

Application leaders responsible for IoT enabling their

applications should:

■ Engage an existing IoT or digital COE, or help

establish a new COE for IoT to share goals,

lessons and resources. This will require teams

from senior leadership to help drive goals

and allocate teams and budgets, operations

groups that have responsibility for the assets or

products, and IT for its process and technical

expertise.

■ Incorporate the business objectives and related

KPIs that the business leadership or COE shared

into the IT strategy and roadmap to support IoT

initiatives. Decline to support projects that are

not aligned to corporate objectives and do not

have KPIs unless there is a specific strategic

reason to do so. To limit wasted effort by your

teams, stop any IT-based IoT initiatives that do

not clearly align back to the business objectives.

■ Establish an IoT solution architecture. Ensure it

is flexible enough to address two main challenges

from typical IoT deployments. First, it must be

able to address a distributed computing and

data topology from edge to the cloud. Second, it

must be able address IoT projects with different

cost or complexity levels (e.g., projects to

manage a series of homogeneous digital lighting

assets versus projects to manage data from

heterogeneous manufacturing assets).

Methodology

Results presented are based on a Gartner study

to help companies better understand what kinds

of benefits IoT delivers, how they need to organize

IoT to best deliver on those benefits, and how

to overcome the technical challenges of IoT. The

primary research was conducted online from 15 May

through 27 June 2019 among 511 respondents from

the U.S.

Companies were screened out for having annual

revenue less than $100 million. They were also

required to complete, or plan to complete,

deployment of at least one use case or project of IoT

by YE20.

Respondents were required to be at manager or

above and should have a primary involvement

and responsibility for making decisions in IoT

implementation.

The study was developed collaboratively by Gartner

analysts and the Primary Research team.

Disclaimer: Results of this study do not represent

the market as a whole but are a simple average

of results for the targeted country, industries and

company size segments covered in this survey.

Definitions

Internet of Things: The Internet of Things (IoT)

is a core building block for digital business and

digital platforms. IoT is the network of dedicated

physical objects that contains embedded technology

to communicate and sense or interact with its

internal states and/or the external environment. IoT

comprises an ecosystem of assets and products,

communication protocols, applications, and data

and analytics.

23

Additional research contribution and review:

Kanwarpreet Oberoi

Evidence

Results presented are based on a Gartner study

to help companies better understand what kinds

of benefits IoT delivers, how they need to organize

IoT to best deliver on those benefits, and how

to overcome the technical challenges of IoT. The

primary research was conducted online from 15 May

through 27 June 2019 among 511 respondents from

the United States.

The survey was developed collaboratively by a team

of Gartner analysts who follow the market, and it

was reviewed, tested and administered by Gartner’s

Research Data and Analytics team. The results of

this study are representative of the respondent base

and not necessarily the business/market as a whole.

Source: Gartner Research, G00428586, Alfonso Velosa, Benoit Lheureux, Martin Reynolds, 29 October 2019

Driving Payback Through Industrial Internet of Things is published by Altizon. Editorial content supplied by Altizon is independent of Gartner analysis. All Gartner research is used with Gartner’s permission, and was originally published as part of Gartner’s syndicated research service available to all entitled Gartner clients. © 2020 Gartner, Inc. and/or its affiliates. All rights reserved. The use of Gartner research in this publication does not indicate Gartner’s endorsement of Altizon’s products and/or strategies. Reproduction or distribution of this publication in any form without Gartner’s prior written permission is forbidden. The information contained herein has been obtained from sources believed to be reliable. Gartner disclaims all warranties as to the accuracy, completeness or adequacy of such information. The opinions expressed herein are subject to change without notice. Although Gartner research may include a discussion of related legal issues, Gartner does not provide legal advice or services and its research should not be construed or used as such. Gartner is a public company, and its shareholders may include firms and funds that have financial interests in entities covered in Gartner research. Gartner’s Board of Directors may include senior managers of these firms or funds. Gartner research is produced independently by its research organization without input or influence from these firms, funds or their managers. For further information on the independence and integrity of Gartner research, see “Guiding Principles on Independence and Objectivity” on its website.

For more information visit:

www.altizon.com

About Altizon

Altizon leverages the power of IoT to drive smart

manufacturing initiatives. Altizon’s Datonis Industrial

IoT Product Suite uses advanced analytics and

machine learning on operations data to generate

actionable insights.

Altizon is a leading Industrial IoT platform provider

recognized in the 2019 Gartner Magic Quadrant for

Industrial IoT Platforms.

Altizon is headquartered in Palo Alto (USA) with

offices in Boston (USA) and Pune (India).