Six Sigma Predictive Decision Engine to provide a real time view of a process using statistical process control methodologies to look at changes to product quality and process variations and the real time impact such changes/variations may have on cost and revenue

1. SIX SIGMA REAL TIME
DECISION
ENGINE(SSRTDE)
OBJECT ORIENTED SIX SIGMA BUSINESS INTELLIGENCE DECISION
ENGINE
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2. DEFINE
MEASURE
ANALYSEIMPROVE
CONTROL
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SIX SIGMA METHODOLOGY - DMAIC

3. Develop a Six Sigma Predictive Decision Engine to
provide a real time view of a process using statistical
process control methodologies to look at changes to
product quality and process variations and the real
time impact such changes/variations may have on cost
and revenue
Primary Impacted Areas
• Project Management
• IT
• Finance/Accounting - Business
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PROJECT OBJECTIVE

4. SSRTDE
Management
Dashboard
Six Sigma Real Time
Decision Engine
(SSRTDE) - Layer
Business
Intelligence (BI)
Layer
ERP Layer
Physical Layer
I/O Layer
BI Meta Data Database Layer
Physical Real Time Database Layer
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PROPOSED HIGH LEVEL ARCHITECTURE FOR SSRTDE DECISION ENGINE
SSRTDE Dashboard
SSRTDE Server

5. • Management decides to embark on a six sigma project to
reduce the failure rate of a sub process - example - grinding
process.
• It is projected that if the failure rate is reduced by 15% a
cost savings of $5M will be realized and revenue will
increase by $10M as a result of increased throughput
realized in meeting customer demand at a faster rate
Scenario
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DEFINE - SIX SIGMA BUSINESS CASE

6. How to measure or
validate the justifications
in the numbers?
• failure rate reduction of
15%
• cost savings of $5M
• revenue increase of
$10M
During
Production one
or more
processes and
or product
specifications
fall out of
control:• How might we show in real time what
adjustments to make to bring the processes
and or product back within control/specs ?
• How to show in real time the dynamic
implications on the projected numbers
What if
scenarios:
• Improve one or more processes - what
if any effect might be seen in
projected results?
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DEFINE - THE PROBLEM

7. LOOK AT A CONCEPTUAL MANUFACTURING PROCESS
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8. • Attribute Data – Extract information from BI Layer
• Count of how many oversized rocks
• C-chart to show the defects (oversized rocks) and to calculate UCL & LCL values in
any given time period
• Variable Data – Extract information from BI Layer – (Raw Material) quality levels
• Some Typical Process Control Data being monitored
• Hardness of the soil
• Moisture content
• Magnesium (Mg) Levels in soil
• Iron (Fe) Levels in the soil
• Mean (X-bar) charts & Range (R) charts and to calculate UCL & LCL values in any
given time period
Statistical Process Control-
MEASURE - CONSIDER THE RAW MATERIAL FEED TO GRINDING – RAW MATERIAL
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9. MEASURE - TYPICAL DATA COLLECTED ON RAW
MATERIAL FEED TO GRINDING – RAW MATERIAL
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Define Attribute Data to
be measured
Oversize Rocks
Input Raw
Material
Define Variable Data to be measured
• Hardness of the soil
• Moisture content
• Magnesium (Mg) Levels in
soil
• Iron (Fe) Levels in the soil

10. MEASURE - GRINDING – PROCESS 1
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Define Attribute Data to
be measured
Proportions of
ruptured rubber
linings in mill
Grinding - Process 1
Define Variable Data to be measured
• Thickness of Rubber Linings
• Inside Temperature of the mill
• Size of particles (Intermediate
Product1) Exiting the mill

11. • Decisions today are static and discrete
• Product is out of spec – stop the process and maybe discard product (input &
output)?
• Cost implications arrived at after the fact:
• Batch discarded material cost = ?
• Downtime cost = ?
• Personal/Transportation cost = ?
• Process out of control
• stop the process and fix issues to bring process back in control?
• Reduce process throughput to try and contain process within control limits?
• Loss Production Time – unable to meet production quantities within specified
time limits to satisfy customer demand
Grinding Process
ANALYZE - CONSIDER THE GRINDING/MILLING PROCESS
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12. Grinding Process
• Move to scenario where decisions are real time, dynamic and continuous
• Continue process and make adjustments downstream (e.g. - settling process 2 ) in real
time to compensate for the upstream out of spec product feed
• Downstream processes adjustment cost
• Management makes strategic decision
• Over the next 3 months need production volumes to be increased by 20%
• What changes if any must be made in processes to achieve this objective?
• What are the cost implications and the effects to product and or processes under
constraints to meet this objective?
• What are the measureable revenue impacts – short/medium/long term?
IMPROVE - MOVE TO A MORE REAL TIME AND DYNAMIC PREDICTIVE MODEL
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MEASURE - TYPICAL DATA COLLECTED ON INTERMEDIATE PRODUCT
FEED TO SETTLING PROCESS – INTERMEDIATE PRODUCT 1

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MEASURE - SETTLING – PROCESS 2

15. • Decisions today are static and discrete
• What adjustment could have been made in real in the settling process to compensate
for the upstream (Grinding Process) having been out of control
• Cost implications arrived at after the fact:
• Would it have cost less to make the adjustment here in the Settling Process rather
than to have discarded the defective Batch coming from the grinding process = ?
• Additional material/overhead cost = ?
• Additional downstream process(s) cost = ?
• Process out of control
• stop the process and fix issues to bring process back in control?
• Reduce process throughput to try and contain process within control limits?
• Loss Production Time – unable to meet production quantities within specified
time limits to satisfy customer demand
Settling Process
ANALYZE - CONSIDER THE SETTLING PROCESS
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Systems are already out there that provide real time
monitoring and control but very few are integrated with a
decision engine to enable real time and dynamic
improvements and control from a revenue/cost perspective
Systems that are already out there are good at telling that a
product and or process is out of control but how do you use
these systems in real time management decisions on
projects such as Six Sigma?
What real time validation engines are out there that will
use Business Intelligence to provide management with real
time guidance on the business case presented that justified
the “go ahead” for the Six Sigma project?
Can we establish a Six Sigma Decision Engine that will
provide management with quantifiable financial measures
for each Sigma change in product and or quality in the
manufacturing process? For example can we a decision
engine to determine that a One Sigma Change =
• 10% revenue increase,
• 15% cost reduction,
• 25% return on investment
PERSPECTIVE

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Six Sigma Real Time System Analysis (SSRTDE) will offer a number of benefits to a Six Sigma
projects.
• Understanding the “science” of the processes and the products made and having real time insight into cost
and revenue changes, is essential to an organization to control, modify or improve its products and processes.
• Having real time data on the product and processes and seeing in real time which variables are affecting the
quality of the products and or processes will allow management to see immediate impact on cost and
revenue.
• This type of real time knowledge on profitability and the variables affecting the process allows for real time
system manipulations in a controlled fashion to optimize performance.
• SSRTDE will also provide a real time simulation decision engine to measure the relative effects of
improvement alternatives without undertaking expensive real time experimentation. The optimum alternative
may be selected and implemented in real time with confidence minimizing the risks to the process and the
customer.
• SSRTDE will provide a real time decision engine to support the Define, Measure, Analyze, Improve and Control
phases of Six Sigma in projects with a real time video camera view on the economics of the manufacturing
process to meet customer conformity .
BENEFITS THE SSRTDE WILL BRING