The present and the future of Analytical Manufacturing

Dell EMEA Pre-Sales Senior Manager

Nuno Antonio

Nuno_antonio@dell.com

Follow me on twitter @nunocruzantonio

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“There are near infinite numbers of ways by which well-intended and sometimes

planned projects can drive off the rails. But in our experience, it almost always has

to do with the difficulty to connect to the right data at the right time, to deliver the

right results to the right stakeholder within the actionable time interval where the

right decision can make a difference, or to incorporate the predictions and

prescriptions into an effective automated process that implements the right

decisions.”

Tom Hill at (Hill, T., 2014) http://techpageone.dell.com/technology/completing-the-value-chain-data-insight-action/#.U3-u_vlkR8G ,

25/06/2014

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3 Source: EMA Demystifying the Internet of Things, 2015.

Top Industries Adopting IoT

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The impact of analytics on IoT

Industrial Automation and Manufacturing

.

Building Automation, Energy, Utilities

Healthcare Life Sciences

Transport Logistics Retail

Cost Savings via

Automation

43%

Opportunity for

Innovation

48%

Process

Improvements

50%

Process Improvements

38%

Opportunity for

Innovation

40%

Cost Savings via

Automation

52%

Cost Savings via

Automation

35%

Opportunity for

Innovation

38%

Demand From

End Users

40%

Need Competitive

Advantage

33%

Need for Faster Decision

Making

41%

Opportunity for

Innovation

43%

Process Improvements

42%

Cost Savings via

Automation

46%

Opportunity for

Innovation

57%

Source: Vodafone: the M2m Adoption Barometer 2014. @nunocruzantonio

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Bbarriars to Successarriers to adoption

Source: EMA Demystifying the Internet of Things, 2015.

Barriers to Success

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Native Distributed Analytics Architecture

Dell Boomi Date/Time

Trans type

Velocity Trigger

Private Cloud

JVM Salesforce

JVM

JVM

Model build

Model build

Model build

Dell Statistica

Statistica Big Data Analytics

Neural Net

SQL Server

Hadoop

Export model as: 1. Java 2. PMML 3. C 4. C++ 5. SQL

JVM

JVM

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CI & Statistica – management, security, governance.

Source Model Type Version

CRAN Btree v1.0

CRAN Btree v1.1

CRAN Btree v1.2

AML NN v10

Algo LGR v5.0

Aperv Ensemble V1.0

EM NN V2.0

Experfy CART V3.0

Chicago

Singapore

Sao Paolo

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Data Flow Gateway

Edge Analytics Core Analytics

Cloud

Cloud

Data center

Device/Sensor Analytics

Internet of Things – Edge Analytics

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Key takeaways Dell has a comprehensive, agnostic, and modular

portfolio, from infrastructure to information management,

to advanced analytics.

Statistica enables organizations to embed analytics everywhere, innovate faster, and empower more people.

Dell Statistica is a complete, easy-to-use, scalable, and affordable predictive analytics platform that delivers business insights faster.

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Statistica Analytic Capabilities • Insurance: fraud detection - identifying groups of motor insurance policy holders with a high average claim cost

• Finance: anomaly detection – detecting transactions that are too similar or dis-similar

• Marketing: understand who is buying your products

• Bioinformatics: grouping like data into similar families

Clustering &

Segmentation

• Finance/banking: credit risk decisions to grant/deny credit

• Healthcare: given information about patients, decide which medications will be beneficial

• Customer Service: automating marketing, customer contact centers, or inbound customer emails.

• Manufacturing: ensuring the correct parcel/letter is delivered to the correct local sorting office.

Decision Trees

• Retail: forecasting which SKUs need to be in which stores and when, anticipate demand spikes with holidays

• Insurance : assigning risk of incidents to policy holders from information obtained from the policy holder

• Healthcare: predicting readmissions, virus spread or contamination outbreaks

• Finance: predict anticipated revenue, product sales, risk of default

Predictive

Models &

Forecasting

• Warranty Analytics: Predict reason for defect to appropriately assign fault to responsible party.

• Sentiment Analysis: Detect trends in social media to determine if a product or service is being mentioned favorably or unfavorably.

• Fraud Detection: Improve predictive power of existing models by leveraging unstructured information such as adjuster narratives of email text.

Text

Analytics

• Manufacturing: Optimize factory settings to reduce waste and replace parts only when needed.

• Power Generation: Find the optimal settings across an entire environment to maximize the efficiency of power generation.

• Environment: Reduce NOx in coal-based power plants.

Optimization

& Simulation

Bre

ad

th &

De

pth

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Enchancing a Foams

Production Unit with

STATISTICA Enterprise

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• Need to reduce the number of non-conformities

• Need to access data which was unavailable at the time

• Logistic problems

• Monthly Quality reports were an heavy load task

• CEO needed easy accessible reports

Challenge

• Raw materials

• Production

• Lab

• Post-production

• Warehouse

• Shipping

• Sales & Marketing

• Waste, etc

Data used

• Overall improvements

• Exectutive reports for the CEO

• Quality report

• Design of Experiments for new products development

• Root causes of non-conformities detected (up to 100000 € loss per unit )

Wins

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Raw materials entry Production

Cut & Shaping Storage

Cure

Shipping

Waste

management

Secondary products (eg:

aglomerates)

Material reusage

Marketing&Sales

Lab

Looper

Quality

Customer complaints management

HR

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Dell Statistica Usecase Optimizing shipments logistics in Manufacturing

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• Shipping trucks with full occupation. • Find typical trucks distribution during the day (24h). • Identify, on a weekly basis, the most critical deliveries per customer.

Challenge

• Dates from trucks in and out from the facilities.

• Packaging and delivery dates by customer including tracking or Delivery Confirmation information.

Data used

• Statistica Enterprise

• Statistica Quality Control

Dell Statistica capabilities used

• Optimized and increased shipment rates to external and internal markets (Reports were created for Fill Rate).

• Optimized the number of collaborators per shipment.

• Increased the number of deliveries on time

• Delivery reports created in Statistica.

Wins

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Packaging and delivery

Trucks distribution

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Packaging and delivery

Table from Report: Packaging and Delivery on time

External and Internal Occupancy Rate

Packaging and delivery

Table from Report: Fill Rate per Market and Customer

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Large diamonds company

Detecting vulnerabilities and crime

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Business objectives

1. People

2. Process

3. Product

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Timely Indicators of

theft/fraud

Detection of

vulnerabilities

Objectives Data Used

Security

Incidents

Freight

Quick Wins

Suspect behaviours

DB quality improvement

Patterns of Incidents

Security vulnerabilities

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Timely Loss Report

Timely Identification

of root cause of

losses

Reports on

deviations from

operational norms &

standards

Objectives Data Used

Process Data

Incidents Data

Stoppages

Quick Wins

Process Performance vs.

Targets

Process problems found

Process Stoppages Analysis

DB cleanup

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Solving a specific problem in the quantity of mineral moisture

Very tough challenge at start.

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• Mineral moisture levels

• Considerable accumulated losses Challenge

• Geological data

• Process data

• Extraction coordinates

• Port loading parameters

Data used

• Discovery of the factors causing the problem

• Losses stopped

• Improvements in the process

Wins

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• Unprecise main KPI predictions causing too many product rejections

• Having predictions available in an actionable time

• Ovens fuel consumption reduction and usage of secondary products

Challenge

• Drills

• Mineral stockpiles

• Lab samples analysis

• Process data

• Fuel consumption

Data used

• Metalization model estimation error reduced from 7% to 3,2%

• Serious biases detected on the data measurements

• Fuel over consumption root cause analysis started ( 1 000 000 R$/month per each reduction of 0,5%).

Wins

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Large mining company (Chile)

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•Walkhrough all the main departments for problem-solving:

• Improving models to estimate the mineral quality

• Improvements on several areas of the process

Challenge

•Drills

• IR data

•Geological data

•Lixiviation racks

•Lab samples analysis

•Process data

Data used

•Boosted trees model to predict the Cooper quantity and quality

•Usage of much cheaper variables (minerals and IR data) to feed the predictive model

•Several other “small victories” Wins

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Data Mining challenge

How much improvement can the models offer

in comparison to the present methodologies?

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Critical area

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Name, Position, Department,

Day Month Year

Prediction map

Critical area @nunocruzantonio

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Predictions based on minerealogy data – MUCH CHEAPER!!!

The models, because of having been developed in Dell Statistica, were

extremelly easy to implement on their existing infrastructure.

Usage of advanced algorithms

Significant gains in objectivity

Removal of the addictive effect on the predictions

Why was it innovative?

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• Prediction of electronics products’ future failure and return rates

• General quality reporting

• Sales forecasting Challenge

• Sales totals

• Sales per SKU

• Returns and repairs data Data used

• Statistica Enterprise

• Statistica Data Miner

Dell Statistica capabilities used

• Insight into product quality and issues within 3 months of putting them on the shelves

• Ability to engage with suppliers proactively to ensure quality or lower cost when products are likely to cause many returns

• Sales forecasts for the next 12 months

Wins

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SKU 50XXXX

4/2013 6/2013 8/2013 10/2013 12/2013 2/2014 4/2014 6/2014 8/2014 10/2014 12/2014 2/2015 4/2015

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

CallR

ate

CR Max = 23.63% CR Final = 22.88%

SKU 50XXXX

Vendas Processos 2/2013 4/2013 6/2013 8/2013 10/2013 12/2013 2/2014 4/2014 6/2014 8/2014 10/2014 12/2014 2/2015 4/2015

-500

0

500

1000

1500

2000

2500

3000

3500

4000

4500

Sales and returns over time

Real vs. predicted return rates

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Comparison of return and defect rates

across various product categories

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Accenture, Spain

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• Price prediction

• Hierarchically coherent predictions

• Account for the cannibalization effect

Challenge

• SKU pricing data Data used

• Development of a predictive pricing appliction Wins

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Questions answered:

1. What’s the SKU price that

maximizes our margins?

2. What is the impact of the

price change on the other

SKUs under the same

category?

3. What are the expected sales

after the price changes?

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Large paper and pulp company (Brazil)

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• How to optimize the most critical product cycle steps?

• Is it possible to perfom and overall optimization?

• All of this per factory and per product

Challenge

• Factories process data Data used

• Overall process optimization carried out by the Engineering Development team – big win for them Wins

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• Find atypical factories and contribution

• Find best predictors linked to the risk

• Predict the risk of future factories Challenge

• KPIs accumulated on several months on different factories Data used

• Statistica MSPC

• Statistica Data Miner

Dell Statistica capabilities used

• Discovery of the factors causing the atypicity of certain factories

• Possibility to predict the risk of new factories Wins

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Large motorbike brand producer (Brazil)

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• Serious logistical problems caused by the chainsaw effect.

• Process too dependent on “educated guesses”

Challenge

• Historical sales data Data used

• New time series model to predict market demand per motorbike make/model

• Chainsaw effect smoothed Wins

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University of Columbia, NY

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• What is the immigrants perception of the government since 1974?

Challenge

• Survey data Data used

• Innovative results

• Publications on Reuters, Huffington post and some major news papers

• Invitations to radio interviews

• All the major political parties showed interested

Wins

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For More Info

• EMA Research

– IoT Infographic

› http://software.dell.com/ema-internet-of-things-infographic/

– Demystifying the Internet of Things

› https://software.dell.com/whitepaper/demystifying-the-

internet-of-things-iot875374/

• Dell Statistica

– dellsoftware.com/statistica

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Are you ready to predict the future?

/ Statistica /

Are you

/ future ready?/

@nunocruzantonio

Thank you!

@nunocruzantonio