dow chemical presentation at the chief analytics officer forum east coast usa (#caoforum)
TRANSCRIPT
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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.
L.H.C. 11/10/2
0089 The Dow
Chemical
Company
Pg. 9
Data in context:
“The Signals in-between the Data”
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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
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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.
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)
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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
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“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.”
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What engagement do you want to facilitate?
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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.
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