By Lloyd F. Colegrove, Ph. D., Data Services Director, Fundamental Problem Solving Director, Analytical Technology Center - The Dow Chemical Company

1. 1
Lloyd F. Colegrove
Mary Beth Seasholtz
Bryant LaFreniere Chief Analytics Officer East Coast forum.

2. 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

3. Enterprise Manufacturing Intelligence
Mountains of Data
Wisdom
Planning
Improvement
Internal Marketing Trailer

4. Enterprise Manufacturing Intelligence-
Mountains of Data
Wisdom
Planning
Improvement
Internal Marketing Trailer

5. 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.

6. Data in context.
Y
X
Z
Is that all there is to your data?

7. Data in context.
X
Y
Z

8. Data in context
Can “rules of thumb” apply to your data?
Y
X

9. L.H.C.
11/10/2
0089
The
Dow
Chemic
al
Compa
ny
Pg. 9
Data in context:
“The Signals in-between the Data”
32
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36
38
40
42
44
46
48
Variable3
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
Variable1
35 36 37 38 39 40 41 42 43 44 45 46
Variable 3
6
7
8
9
10
11
12
13
14
Variable1
8 16 24 32 40 48 56 64 72 80 88 96 104
Avg=10.02
LCL=6.70
UCL=13.34

10. Enterprise Manufacturing Intelligence.
The Journey Out of Darkness
Wisdom
Planning
Improvement
Internal Marketing Trailer

11. Enterprise Manufacturing Intelligence.
The Journey Out of Darkness
Wisdom
Planning
Improvement
Internal Marketing Trailer

12. Current Data Use – Poor coordination,
no obvious plan. We work, data sits.
Manufacturing
Products
Monitor
Safety
Product
ReleaseProcess Control
Data Data Data
R&D
Reports
System.
Historic
“Local”
knowledge
Newly
generated
knowledge

13. 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

14. 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.

15. Journey to the SOLUTION….
AnalyticComplexity
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

16. 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.

17. Reveal data and new relationships
Why was this graph so hard to make?

18. D from 100% is good product
being flushed away

19. 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 ChartsControl Charts
•Good info, useful BUT…
•Only answers questions
about individual variables

20. 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

21. 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?

22. 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

23. 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
NWAFocusEMI
Data Integration & Analytics
IntelligenceERP
Collaboration
Center
Knowledge
Base
Manufacturing Intelligence
Historian
QC Test
Stations
Intelligence
SCM

24. 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. 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”

26. 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

27. • 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!

28. 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

29. 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

30. 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.

31. Get the data packaged right
31

32. 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

33. Thank you for your kind attention!