From connectedinfrastructure to AI

More thenadvanced statistics?

Ing. Verleden Korneel

Group automation engineer

Kortrijk

3/10/2019

Intro

140countries

6.000customers

Agriculture-

Automotive-

Commercial-

Construction-

Hygiene-

Industrial-

Public-

Residential

Global Company

PolymersProductsEngineered

SolutionsFlooring

Intro

29plants

> 5000employees

18sales offices &

distribution centers

17countries

FACTS & FIGURES

At a glance

2.000mio EUR turnover

1959Roger De ClerckFounder

1991Split into7 individual groups

2005Merger of 5 groups into B.I.G.

Plants

Sales Offices& Distribution Centres

Acquire talent for innovation for future technology & operational management!

Focus on innovation

Hiring, training, coaching & career

path

Competencedevelopment

program

Phd’s & Msc’shired since 2013

50of which 40 young

graduates

Innovation

RED: Research Engineering & Development

RED: Research Engineering & Development

- Senior innovation project & portfolio managers

- Process engineers

- Material specialists

- Chemistry engineers

- Bio-engineers

- PhD digital printing

- IP manager & patent engineer

- Engineering

- Utilities & infrastructure

- Electrotechnic & automation- Equipment engineer

State of the art machinery in BIG

Independent of machine vendors: BIG –IP!

Principles of Industry 4.0 in practice

Open controllers

Remote accessible

Manageable

More than “keeping the lights on”

Vision

6

Check investments for I4.0 readyness

Work on the group identity: ATF

Prolong life expectancy existing machines

Gather process knowledge &

put into code

Build/assemble our own new machines

Lead the way in new

automation technology

Mission

7

Build faith, trust and mutual respect

with business partners

Reverse engineer existing machines:

Excellent way to gather process knowledge

Put process knowledge in code:

Scientific and mathematic objective analysis

Lead the way in new technology

Build further to develop new capabilities

Lead the ATF: spread knowledge

Organise courses, share best practices

Leverage decisions with (machine)data

Maximize learnings from previous projects

Strategy

8

If your electrical cabinet looks like this: You are not AI-ready

If your control room looks like this: You are not AI-ready

If your control room looks like this: You are not AI-ready

If your boss doesn’t believe you have a problem here: You are not AI-ready

To become AI-ready: connect the dots

Make your OT – “AI-ready”

14

Bytes “in milliseconds”

Gigabytes“Per hour”

Step 1 to become AI-ready: upgrade infrastructure

Steps to become AI-ready: upgrade infrastructure

You connect to an open system: now next steps!

Wonderware Historian: Extract machine data

Extract data from sources- Wonderware Historian database- Wonderware MES database: BOM & batch numbers- Data from dosing units- Data from energy monitoring platform

Data is not (yet) information w/o a data scientist!Information+ insight + action = value

Wonderware Historian: Extract machine data

Transform data:

• Clean up• Missing values• Duplicates• Inconsistencies in format

e.g. data/time dd/mm to dd/mm• Fill gaps

• Metadata• Name variables (ISA95)• Add labels/attributes• E.g. Data/time format

• Merge• Filter• Sort

• Aggregate• Unique identifiers• Foreign keys• Similar to SQL

Transform data: From open source/freeware to 1.5 mio software

Source:BI magic quadrant Gartner

At this stage you’ll already get information & insights

Central infrastructure private cloud: ERP/BI/WW/OTConnect to ERP

• Recipe manager

• Monitoring of batches

Connect ERP to MES:

• Energy dashboards

• OEE Reporting

Connect MES to OT:

• Load parameters to machine

• Historise machine settings

Load data: train and validate models

24

Pick your battles:

- Optimize energy consumption

- Waste reduction

- Machine output

- Predict faillures

- Predict quality

Load data

25

Use or store the processed data for modelling & analytics:

- Machine learning:

- Clustering algorithms

- Random forest algorithms

- Neural networks

- Use historical data to predict future behaviour

- Decision support

- Anomaly detection

- Risk management

- Dashboarding

- Optimize processes

Deploy and operationalize

26

Clustering Decision tree

Write code back to controller Visualise

Deploy and operationalize

Next steps for Beaulieu:

Train neural networks/deep learning

Make tools avaiable for all PDE’s

Learn how to deploy more complex ML

Adapt infrastructure for realtime scoring

…

Even if you are on the right track,

You’ll get run overif you just sit there

Next steps for you: Ask yourself these questions

It takes more then just software to develop AI:

• Are your people ready?

• Is your equipment open? Will you be able to deploy?

• Is your equipment connected?

• Can we dedicate specialists & infrastructure to ETL?

• Will your company stick to an industry 4.0 plan?

Conclusion

You might find:

• Obvious insights

• Confirmation of presumptions

• The answer halfway

But you will:

• Know the process into detail

• Have a funded opinion

Excellence covered by peopleDelight our Customer • Strive for Excellence • Focus on People • Create added Value • Act with Integrity