1

Lloyd F. Colegrove

Mary Beth Seasholtz

Bryant LaFreniere Chief Analytics Officer East Coast forum.

A 2015 Golden Mousetrap Award Winner Design Tools Hardware & Software: Analysis & Calculation Software

Dow Chemical for NWA Focus EMI solution from Northwest Analytics

Read more: http://www.cnbc.com/id/102415149

2015 Manufacturing Leadership Award Big Data and Advanced Analytics Leadership

Winners in this category will have transformed the mountains of data generated by the

typical manufacturing enterprise into actionable insights that can be used to achieve

competitive advantage. Winners, for example, will have assembled the platforms, tools,

data models, applications, processes, and skills needed to mine meaningful and timely

information from data http://www.dow.com/news/press-releases/article/?id=10743

Enterprise Manufacturing Intelligence

Mountains of Data

Wisdom

Planning

Improvement

Internal Marketing Trailer

Enterprise Manufacturing Intelligence-

Mountains of Data

Wisdom

Planning

Improvement

Internal Marketing Trailer

Data: Is it any good? How do you know?

UCL or USL

LCL or LSL

UCL or USL

LCL or LSL

Data must be analyzed in context.

Data in context.

Y

X

Z

Is that all there is to your data?

Data in context.

X

Y

Z

Data in context Can “rules of thumb” apply to your data?

Y

X

L.H.C. 11/10/2

0089 The Dow

Chemical

Company

Pg. 9

Data in context:

“The Signals in-between the Data”

32

34

36

38

40

42

44

46

48

Variab

le 3

8 16 24 32 40 48 56 64 72 80 88 96 104

Avg=40.13

LCL=33.30

UCL=46.96

Multivariate analysis can reveal a

change in the correlation

structure not visible with

univariate analysis. 7

8

9

10

11

12

Variab

le 1

35 36 37 38 39 40 41 42 43 44 45 46

Variable 3

6

7

8

9

10

11

12

13

14

Variab

le 1

8 16 24 32 40 48 56 64 72 80 88 96 104

Avg=10.02

LCL=6.70

UCL=13.34

Enterprise Manufacturing Intelligence.

The Journey Out of Darkness

Wisdom

Planning

Improvement

Internal Marketing Trailer

Enterprise Manufacturing Intelligence.

The Journey Out of Darkness

Wisdom

Planning

Improvement

Internal Marketing Trailer

Current Data Use – Poor coordination, no obvious plan. We work, data sits.

Manufacturing Products

Monitor

Safety

Product

Release Process Control

Data

Data

Data

R&D

Reports

System.

Historic

“Local”

knowledge

Newly

generated

knowledge

Future Data Use: Data will work for us!

Manufacturing Products

Data

Data

Data

Analytics

Platform (aka

Focus EMItm)

R&D

Reports

System.

Historic

“Local”

knowledge

Newly

generated

knowledge

Motivation

• Use DATA to

– Justify* actions to FIX

– Guide* actions to IMPROVE

– Prescribe* actions to make BREAKTHROUGH CHANGES

“Largest impediment to becoming more data-driven is lack of understanding of how to use analytics*” “*Analytics: The New Path to Value”, MIT Sloan Management Review, October 2010

What this means to us is …

– We must learn how to better listen to the signals that our plants are sending us and how to respond to them.

Journey to the SOLUTION….

Analy

tic C

om

ple

xity

SIMPLE

COMPLEX

Dashboards for

Improvements

Organized

Data $ $$$$ Data

Alarms

Automated

Actionable

Analytics

Manufacturing

Analytics

Knowledge

Enterprise

Information

Value Delivery

Implementation of LIMS / Data Historian/ Etc.

Data

Establish new rules as to how the data “lives”

Guiding Principles: (1) data lives in one spot only and (2)

every piece of data is owned by one entity and

uniquely identifiable.

Reveal data and new relationships

Why was this graph so hard to make?

D from 100% is good product being flushed away

Looking at more than Control Charts

– Need next step of what all of this data means in the bigger context

• More than linear grabbing of data

• It is the relationship/interaction of the data among the business information, collaborative troubleshooting, and other important aspects in the plant/process.

– Clay Shirky: “… It’s not information overload. It’s filter failure …”

• Need to cull out the relationships

Many Control Charts Control Charts •Good info, useful BUT…

• Only answers questions

about individual variables

Future Workflow – as dreamed up on a paper napkin

Retrieve

Data

Analyze

Data

Join

Data

Quality

Analyst

A

Wonderful

tool

SIMCA-P

Matlab

“Services Layer”

This services Layer will

know how to interact with

all the different databases (1) Discover what is available

& show it to the user

(2) Retrieve data once user

says what s/he wants

Manually or unattended.

Join data depending

on goals: • Continuous

• Batch

• Multiple plants

Pirouette

Etc.

What the User Sees: A Workflow Implementation Tool

Where to Start? Our First Hurdles: Accessing and Joining Data

• Data available in

– instrument software

– Lab information systems

– process historians

– SAP-like product systems

• Data collected at different time intervals

– Indexed differently; some in time, some in batchID

• Data integrity impacted by e.g.

– Natural plant variation

– Inappropriate plant operation

– Vagaries of chemical processes (reaction kinetics, etc.)

Once we create an appropriate “play space” for our data, what will we achieve?

From Very BIG Data to Very BIG Knowledge

Analyze

Report Prepare/

Distribute

Capture Data

Aggregation

Analyze

Report

Capture

V

A

L

U

E

Automated

Manual

Data + Analytics = Intelligence

Collaboration + Intelligence = Knowledge

Machine #1 Machine #2

Process #1

Instrumentation / Devices

HMI/SCADA

Historian

Machine #1 Machine #2

Process #2

Instrumentation / Devices

Laboratory

LIMS

Process

DCS

MES

Role-specific

clients/content

Executive Management

Business Unit

Management

Corporate

Engineering/Quality

Plant Management

Plant Quality

Process Engineers

Operators

Quality

System

NW

A F

ocu

s E

MI

Data Integration & Analytics

Intelligence ERP

Collaboration

Center

Knowledge

Base

Manufacturing Intelligence

Historian

QC Test

Stations

Intelligence

SCM

Partnership with Vendor

• Base Abilities

– Direct data-source connectivity

– Real-time data aggregation

– Comprehensive analytics

– Real-time, role-based dashboards

– Alarm & notification services

• “Accelerating” Modules

– Knowledge Base

• Key-word searchable enterprise-wide, collective knowledge store

– Collaboration Center

• Fully-integrated, role-based, problem-solving workspace (with rich-content visual communications capabilities)

25

Discussion triggered by Data between Technical and On-site Persons

Consult Existing Knowledge

Agree on Actions

Plant makes Changes

Integrate learning into enterprise

Real time Tracking and Notification Dashboard

Alert! The new cycle of data usage…

Data, Calculations,

Predictive models

“Big Data”

Example of Culture Change

Jul 2013 Plant

Trip

Internal Degradation

Post Mortem Analysis

Jan 2014 Plant

Trip

Dashboard Alert !

Conversation Initiated – how to protect the internals.

Internals Survives just fine

• Dashboards for similar plants in two countries

– Contains analytical & process data

• Calculations of relevant metrics

• Teaching SPC/SQC vs. specification cutoffs for plant monitoring

• Research and Manufacturing are engaged!

– Detected numerous plant drifts which have initiated conversations and actions

– Developing a collaborative culture of proactive intervention

• Situations being fixed before they become a concerns

Initial Results, ROI

Proactive rather than Reactive!

Ta-daa!

When we started Now

28

“I work from what is in front of me. If I can see something flashing, then

I will deal with it. If it is not right in front of me, I don’t deal with it until it

becomes a crisis!” – Typical Run Plant Engineer

Why all that red at the start?

• The variables identified by Technology Team had not been focused on historically

– We are looking at higher order things that the plant didn’t have inclination or resources to look at before.

• Medium and Long term trends are not typically what a Run Plant focuses on.

– Dashboard helps Technology Team show the plant these important variables and calculations; plant can now internalize the learnings from troubleshooting teams.

“When you’re up to your neck in alligators, it’s easy to forget

that the original goal was to drain the swamp.”

29

What engagement do you want to facilitate?

30

Strategic: Large changes in capital or chemistry or control in as systematic effects are revealed/discovered. Made quarterly to yearly.

Tactical: Technical Staff & Local engineers: Decisions on the weekly to monthly timeframe. Course corrections optimizing across multiple variables and phenomena.

Transactional: Plant Operators are changing inputs to the plant guided by plant procedures or automatic control. One variable at a time decisions made at the ~hourly time frame.

Get the data packaged right

31

Next Steps

– Roll-out of Enterprise systems to other BUs

– Continue to build our Knowledge Base concept

– Expand Collaboration Center usage

– Plot next steps to Manufacturing Analytics

– Continue to develop, partner and dream.

Because our goal is still:

TOTAL Data Domination

Thank you for your kind attention!