7Epsilon for ISO9001:2015A Risk Based In-line Process Optimization Case Study for a

Connected Enterprise

Dr. Rajesh S. Ransing, Swansea University, UKDr. Meghana R. Ransing, p-matrix Ltd., UK

Presentation Id: TO5; Date: 15th November 2016; Time: 11-noon.

ASQ International Conference on Quality Standards, November 14-15, 2016 Pittsburgh, USA

Learning Objectives

Use in-process data and product specific process knowledge for hypothesis generation (clause 6.1).

Embed risk based thinking and organizational knowledge within your organization.

Learn 7Epsilon Vs Six Sigma.

Understand 7Epsilon in ISO 9001:2015 context Process Inputs:

In-line Process Optimization: Case Study

Objective: Discover an optimal range for sensor values – if one exists.

Process Output(s): %Downtime for a line calculated hourly (or other defined time limit), OR%Defective components produced in a batch.

Process Inputs (Categorized as Sensors): Data for all sensors and tags collected for an hour (or a defined time limit). An ‘on-off’ switch - the number of times it is ‘on’ or ‘off’ in an hour. Continuous or ordinal variables - a median, 5th and 95th centile values per hour. Categorical variables - one value for every process output value.

Product Specific Process Knowledge

Organisational process knowledge for a given product is i. the actionable information;

ii. in form of optimal list of measurable factors and their ranges(Sensor 1 value 0.026 – 0.05; Sensor 2 value: 0.007 – 0.009);

iii. in order to meet desired business goals (process outputs)(e.g. minimize deviation from expected results, reduce % downtime or rework time etc).

Risk Based Thinking

‘Risk Based Thinking’ is the DNA of ISO9001:2015 & AS9100:2016

It’s not contained or isolated in one section or clause …

Risk and Opportunities Based Thinking

Clause 4.4: Quality management system and its processes 4.4.1 f: address the risks and opportunities as

determined in accordance with the requirements of 6.1

Clause 9 Performance Evaluation (and 9.3 Management Review) 9.3.2.e: the effectiveness of actions taken to address

risks and opportunities (see clause 6.1)

Clause 6.1 Actions to address risks and opportunities in order to(i) give assurance that the QMS can achieve its intended

results,(ii) enhance desirable effects,(iii) prevent, or reduce undesired effects,

Clause 10 is Improvement.

What is Risk in Risk Based Thinking?

‘Risk’ in Risk Based Thinking should

• go beyond its usual interpretation in Risk Assessment Forms,

• help trigger innovation,

• create new organisational knowledge.

What is Risk in Risk Based Thinking?

To understand ‘Risk’ in ISO9001:2015 and

AS9100:2016 context, we need to understand

‘Uncertainty’.

Uncertainty or Deficiency of Knowledge

Uncertainty or Deficiency of Knowledge

Uncertainty or Deficiency of Knowledge

What are your options for:

Undertaking in-process quality improvement projects

Implementing plant wide process optimization strategies

Connected Enterprise, Industry 4.0, Big Data

Satisfying requirements of the Clause 6.1of ISO9001:2015• Prevent or Reduce Undesired (Negative) Effects• Enhance Desired (Positive) Effects

OR• Avoid BAD days • Repeat GOOD days

Option 1: Plot Process Output Vs Process Input Graphs

Option 2: Embed Risk Based Thinking via Penalty Matrices

Option 2: Embed Risk Based Thinking via Penalty Matrices

Option 2: Embed Risk Based Thinking via Penalty Matrices

Option 2: Embed Risk Based Thinking via Penalty Matrices

Option 2: Embed Risk Based Thinking via Penalty Matrices

Option 2: Embed Risk Based Thinking via Penalty Matrices

Option 2: Embed Risk Based Thinking via Penalty Matrices

Penalty matrices using p-matrix software

p-matrix software analyses hundreds and thousands of penalty matrices among factors.

Classifies factor settings as Optimal, Avoid or No Effect.

Ranks them in order of importance.

At end of analysis, a process engineer would get a list of top 15-20 matrices he/she needs to look at.

Analyses discrete and continuous parameters together in one analysis.

Up to 200 factors and 10 responses can be analysed at once.

The algorithm is scalable for Big Data and In-line Analysis.

Lets make a Solemn Promise …

Confirmation Trial Plan

Process Inputs

Minimum Value

Maximum Value

Optimal Range Optimal Values

Sensor 1 0.019 0.05 Top 50% > 0.026 & < 0.05

Sensor 2 0.007 0.012 Bottom 50% > 0.007 & < 0.009

Sensor 3 6.204 6.527 Top 75% < 6.299 & < 6.527

Sensor 4 7.714 8.028 Bottom 50% < 7.714 & < 7.847

In-process Quality Improvement using 7Epsilon steps

What’s New? Organisational knowledge management (Clause 7.1.6)Actions to address risks and opportunities (Clause 6.1)7Epsilon = Six Sigma + Industrial Internet of Things (IIoT)

Conclusions

Risk - An effect of uncertainty (on an expected result)

Uncertainty == deficiency of knowledge.

Deficiency of knowledge are opportunities for creating additional knowledge (e.g. Risk Based Tolerance Synthesis).

The effect of uncertainty manifests itself as deviation(s) from expected results.

7Epsilon’s penalty matrix approach

quantifies the effect of uncertainty by penalizing deviation from desired response (Clause 6.1), and

embeds risk based tolerance synthesis and creates new organizational knowledge (Clause 7.1.6).

Further Reading

Ransing R.S., Batbooti R.S., Giannetti C, and Ransing M.R., “A quality correlation algorithm for tolerance synthesis in manufacturing operations”, Computers and Industrial Engineering, Volume 93, Pages 1–11, March 2016.

Ransing R. S., Giannetti, C., Ransing, M. R., & James, M. W. (2013). A coupled penalty matrix approach and principal component based co-linearity index technique to discover product specific foundry process knowledge from in-process data in order to reduce defects. Computers in Industry, 64(5), 514-523.

Giannetti, C., Ransing, M. R., Ransing, R. S. et.al (2015) “Organisational knowledge management for defect reduction and sustainable development in the foundry industry”, International Journal of Knowledge and Systems Science (IJKSS), 6(3), 18-37, July-September 2015.

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7Epsilon: In-process Optimization in a Connected Enterprise Environment