Presented at ASQ World Conference 2009

1. Prioritizing Process Improvements
Govind Ramu
JDS Uniphase Corporation
ASQ World Conference on Quality and Improvement
W07

2. Group Interaction
(3 to 5 minutes)
How effective is your engineering meeting?
- Are you discussing the appropriate topics?
- Do you have the right audience?
- Do you have actionable discussions?
- Do you leave the meeting knowing exactly
what you need to do?
- Would you consider your time spent
productive and worthwhile?

3. Meetings - Are they productive?
• Overwhelming information.
• Tons of presentations.
• Information unrelated to agenda.
• Attendees not well prepared.
• Delegated attendees.
• Too many side conversations.
• Blackberry/laptop usage during discussions.
• Working on next meeting while listening.
• Engaging in arguments on “how to.”
• No clear conclusions as to “what to do.”
• No clear “actionable” discussions.

4. Productivity Survey
• People work an average of 45 hours a week; they
consider about 17 of those hours to be unproductive
(U.S.: 45 hours a week; 16 hours considered
unproductive).
• People spend 5.6 hours each week in meetings; 69
percent feel meetings aren't productive (U.S.: 5.5 hours;
71 percent feel meetings aren't productive).
• The most common productivity pitfalls are unclear
objectives, lack of team communication and ineffective
meetings – chosen by 32 percent of respondents overall
(U.S.: procrastination, 42 percent; lack of team
communication, 39 percent; ineffective meetings, 34
percent).
Source: The Microsoft Office Personal Productivity Challenge (PPC)
Responses from more than 38,000 people in 200 countries
http://www.microsoft.com/presspass/press/2005/mar05/03-15ThreeProductiveDaysPR.mspx

5. Typical Engineering Meeting
Product yield by part #
Product volume by year by part # Product volume and yield
Product by volume
Any actions?

6. What really matters at the
engineering meeting?
Engineers want to know:
• How the process health is doing.
• What the risks are for internal/external
customers.
• What process parameters to fix.
• What actionable intelligence exists.

7. The eight-step model for process
improvement:
• Step 1: Identify CTQs and CTPs.
• Step 2: Create a CTQ-CTP relationship matrix.
• Step 3: Conduct a process FMEA.
• Step 4: Develop a control plan.
• Step 5: Conduct gage R&R studies.
• Step 6: Set up statistical process control.
• Step 7: Use process capability & gage R&R to
identify improvements.
• Step 8: Prioritize improvement efforts.

8. Step 1: Identify CTQs and CTPs
Need Drivers CTQs CTPs
Customer
With right participants,
Needs engineering meetings to
focus heavily here for
Customer improved effectiveness
Drivers
I want…
VOC
Product
CTQs
CTP
CTQ CTP
need Business
Drivers CTQ CTP
CTQ
Business
Needs Currently
CTP
engineering
meetings may be CTP
Primary Needs
spending more time
at this level
CTQ
Secondary Needs CTQ
Tertiary Needs
General Specific
Hard to measure Easy to measure

9. Some Definitions
• Customer drivers – quality, cost, delivery, response.
• Business drivers – first-pass/rolled throughput yields,
work in progress material cost, inventory cost, cycle
time, etc.
• Primary, secondary and tertiary needs – customer needs
from abstract to tactical.
• CTQ – critical to quality characteristics of products that
customer expects from the product or service.
• CTP – critical process parameters that have cause and
effect relationship to one more CTQ.

10. Step 1: Identify CTQs and CTPs
Need Drivers CTQs/ CTCs CTPs
Oven process Control
I want… tempr. X1 deg +/- 5 deg F
VOC Taste Raw material aging (days)
Product Vegetable aging (days)
quality Oven process control
time. X2 min +/- 2 min
need Average
Delivery Order processing time
order-delivery Order handling time
I want tasty pizza time
I want hot pizza Cost (By type & volume) Order delivery time
Material cost
I want my pizza to be crispy
I want my pizza to have Processing cost
fresh toppings Selling price
Service Yield %
I want my pizza to be quicker
quality Margin %
Every time I get either the Quantity &
wrong pizza or wrong right product Order check
toppings! Customer
Not so expensive Complaint handling Response time
recovery
I want to get a replacement time Replacement time
for the mistake
General Specific
Hard to measure Easy to measure

11. Step 2: Create a CTQ-CTP Relationship Matrix
CTP Vs CTQ
Screening Engineering
Judgment
DOE

12. Step 2: Create a CTQ-CTP Relationship Matrix
Explore Interactions
CTP Vs CTQ
Interrelationships
Similar idea referenced by Mikel Harry : http://www.isixsigma.com/forum/ask_dr_harry.asp?ToDo=view&questId=82&catId=11

13. Step 3: Conduct a Process FMEA
FMEA
Establish
severity,
occurrence Creating customized severity, occurrence, and detection scales
detection scales for the nature of your business or industry can make a
difference – one size does not fit all!
Identify Identify the critical process flow of the product line
process steps and hold a team brainstorming session to identify all
to perform FMEA probable failure modes, causes, and interim and end
effects.
Identify failure
Document current controls as you would in standard
modes, causes,
effects, FMEA practice.
current controls,
& risks Further develop the FMEA to include the severity,
occurrence, and detection ratings from your customized
Identify scales.
critical process
variables to Calculate risk priority numbers (RPNs) by multiplying
monitor, assign the severity, occurrence, and detection ratings.
RPN Prioritize risks based on RPN values.
Develop
control (See more detailed flow next slide)
plan (CTQ,CTP)
Reference: http://www.qualitytrainingportal.com/resources/fmea/index.htm

14. Brainstorming all potential causes for failure modes.
Inputs:
Process flow charts, manufacturing work instructions,
historical process defect Pareto, lessons learned, etc.
Reference: March 2009 QP article, “FMEA Minus the Headache.”

15. Populating the FMEA table with discussion outputs.
Reference: March 2009 QP article, “FMEA Minus the Headache.”

16. Step 4: Develop a Control Plan
Measurement system that
may require R&R – ensure
if adequate before
proceeding with SPC.
Inputs from Step 1 (Identify CTQs and CTPs) and FMEA
recommended actions corresponding to CTQ, CTP merge here.

17. Step 5: Conduct Gage R&R Studies
Back to Basics
• Let us refresh our memory on some basic definitions:
– Repeatability: Variation in measurements obtained with one measuring
instrument when used several times by an appraiser (operator) while
measuring the identical characteristic on the same part.
– Reproducibility: Variation in the average of the measurements made
by different appraisers (operators) using the same gage when
measuring a characteristic on one part.
– Process capability compares the output of an in-control process to the
specification limits by using capability indices (Cp, Cpk). The
comparison is made by forming the ratio of the spread between the
process specifications (the specification quot;widthquot;) to the spread of the
process values, as measured by 6 process standard deviation units (the
process quot;widthquot;).
– Process performance indices (Pp, Ppk) basically try to verify if the
sample generated from the process is capable to meet customer CTQs
(requirements). Process performance differs from process capability
and is only used when process control cannot be evaluated.

18. Step 5: Conduct Gage R&R Studies
Measurement Systems Analysis (MSA)
n
∑ f (X )
2
X Definition of standard i i
− X
i=1
deviation: S =
n -1
SGage Standard deviation of gage (one
appraiser)
Width
0.5% 0.5%
5.15 SGage
Gage 99% of measurements fall
repeatability in the gage repeatability
range
Standard deviation of gage
(more than one appraiser)
Appraiser 1
Appraiser 3
Appraiser 2
(Back to Basics)

19. Step 5: Conduct Gage R&R Studies
Sources of Variation
100.00%
(Back to Basics)
Reproducibility
σ 2 Total = σ 2Part to Part + σ 2 Repeatabil ity + σ 2 Operator + σ 2 OperatorBy Part
Overall Product Repeatability Operator Operator by Part
Reference Minitab Help: GR & R Study (Crossed)- ANOVA Method

20. Step 5: Conduct Gage R&R Studies
How can a measurement system contribute in accepting
BAD product and rejecting GOOD product ?
True measurement of the product True measurement of the product
Operator variation
(E.g., reading from
analog panel)
+/- 10 Deg F
Oven instrument
variation
+/- 5 deg F
LSL USL
Accepting BAD product Rejecting GOOD product
(Back to Basics)

21. Step 5: Conduct Gage R&R Studies
Effects of Sources of Variation
Overall production variation
What was
Product produced
Operator variation—reading from analog panel
Oven instrument variation What
was
observed
Target Process adjustment
(Back to Basics)

22. Step 6: Statistical Process Control (SPC)
Assign Unique
Man
ID XXX
+ Machine
Material
Method
Environment
+
Extended free text about
Special cause
Inputs from FMEA OCAP
Brainstorming Data base

23. Step 6: Statistical Process Control (SPC) – Calculate Cp, Cpk if the process is stable.
Short-term Vs Long-term
Capability
Over long term conditions,
a “typical” process will “Short-term
shift and drift by capability” (Cp, Cpk)
approximately 1.5
standard deviations*.
Time 1
Time 2
Time 3
Time 4
“Long-term performance” that includes changes
to material, multiple shifts, Operators,
environmental changes (Pp, Ppk)
Target
(Back to Basics) LSL USL

24. Step 7: Using Process Capability and GR&R to Identify Improvements
Baseline GR&R Baseline Capability
Pp= 0.8 Ppk= 0.6 Pp= 1 Ppk= 0.93 Pp= 1 Ppk= 1
Oven temperature Order processing time Order handling time
Pp= 0.8 Ppk= 0.7 Pp= 0.8 Ppk= 0.8 Pp= 0.6 Ppk= 0.6
Oven time Order handling time Order response time
Raw material aging Vegetable aging Order replacement time
*Pp= 0.9 Ppk= 0.7 *Pp= 1.2 Ppk= 1.1 *Pp= 1.3 Ppk= 1.2
* Data transformed

25. Step 7: Using Process Capability and GR&R to Identify Improvements
Capability & GR&R Grid
High
>24%
% GR& R*
Low
<24%
Low High
Cpk/Ppk*
<1.1 >1.1
* GR&R 24%, Cp, Cpk 1.1 are an example. Decide what is acceptable for your organization.

26. High
% GR& R
Low
Scenario — High GR&R + Low Cp/Pp & Cpk/Ppk
Low High
True value
Cpk/Ppk True value
Process of the part
of the part
shift
Operator variation Operator variation
reproducibility reproducibility
Instrument variation
Instrument variation repeatability
repeatability
LSL USL
Accepting BAD product Rejecting GOOD product
(Back to Basics)

27. High
% GR& R
Low
Scenario — Low GR&R + Low Cp/Pp & Cpk/Ppk
Low High
Cpk/Ppk True value True value
of the part Process of the part
Shift
Operator variation
reproducibility Operator variation
reproducibility
Instrument variation Instrument variation
repeatability repeatability
LSL USL
Accepting BAD product Rejecting GOOD product
(Back to Basics)

28. High
% GR& R
Low
Scenario — High GR&R + High Cp/High Cpk
Low High
True value
Cpk/Ppk True value
of the part
of the part
Operator variation Operator variation
reproducibility reproducibility
Instrument variation
Instrument variation repeatability
repeatability
LSL USL
Accepting BAD product Rejecting GOOD product
(Back to Basics)

29. High
% GR& R
Low
Scenario — Low GR&R + High Cp/Pp & Cpk/Ppk
Low High
Cpk/Ppk True value True value
of the part of the part
Operator variation
reproducibility Operator variation
reproducibility
Instrument variation Instrument variation
repeatability repeatability
LSL USL
Accepting BAD product Rejecting GOOD product
(Back to Basics)

30. Step 7: Using Process Capability and GR&R to Identify Improvements
Capability & GR&R Grid
CTQ1 CTQ3
High CTP3
% GR&R
CTQ2 CTP1
Low
CTP2
Low High
Cpk/Ppk
Now that we know the baseline data of performance indices/capability & GR&R of
our pizza-making CTQ & CTP, let us place them in the appropriate quadrants.

31. Step 8 - Prioritizing Improvement Efforts (Process Health Card)
CTQ 1 2 3
CTP
1
2 Date GRR/ CL
Date CL Alpha/Beta Next due
3 GRR* GRR% ESTB. LCL UCL Stability** Cp/Pp Cpk/Ppk Risk% Date
01/07 37% 01/07 20 24 NO 0.8 0.6 07/07
CTQ1
Product
01/07 8% 01/07 1.30 1.80 YES 0.9 0.88 07/07
CTQ2
01/07 25% 01/07 15 18 NO 1.2 1.15 07/07
CTQ3
CTP1 01/07 7% 01/07 200 208 YES 1.3 1.25 07/07
Process
01/07 12% 01/07 1.5 1.7 YES 1.00 0.92 07/07
CTP2
CTP3 01/07 25% 01/07 1.7 2.0 NO 0.95 0.82 07/07
Relationship
Significant Moderate Weak
•If new test station/equipment added, operator changed, equipment overhauled, new GR&R study is required. If no changes, 6 months frequency of
GR&R monitoring is a good practice.
** If there has been sudden change in process variation (for good or bad), extended period of lack of stability, a new study has to be conducted
and control limits recalculated. If no changes, 6 months frequency of review of control limits is a good practice.
CTQ: critical to quality characteristics. CTP: critical to process parameters. Relationship between CTP and CTQ to be established up front.
Alpha/Beta errors can be obtained from statistical software misclassification feature, or by using simulation software.

32. FMEA SPC GR&R
Establish Identify equipment
severity,
occurrence Data query/retrieve
detection scales (real time – where possible)
By process date?
Validate data By lot sequence?
Identify Plan GR&R
sequence By measure date?
process steps experiment
to perform FMEA
Control chart Measure GR&R
Measure
Identify failure Analyze data capability Cp/Cpk
modes, causes,
(if stable)
effects,
current controls Identify
& risks special causes
(If not stable)
Yes Continue monitoring
Identify Cp, Cpk
acceptable? stability &
critical process Estimate process
process capability
variables to performance indices
monitor, assign No
RPN Summarize Prioritize improvement efforts
the Cp, Cpk, Pp,
Develop
Ppk, GR&R%, Health
control
CTQ vs CTP false acceptance, card
plan (CTQ,CTP) matrix false reject

33. Triggering Actionable Discussions
• Out of all CTQ and CTP from a given product
line, prioritize a vital few for improvement
actions. In this example, CTQ1 and CTP3 are
prioritized.
• By improving CTQ1 and CTP3, we can reduce
the producer/consumer risks to a set goal
acceptable by customers and manufacturing.
• Improve gage R&R to <10%. Improve process
capability indices >1.5.
• Move items from red, yellow, and blue zones to
green based on prioritization.

34. One might ask…
• Why go through these process steps? Why not
focus on low process capability to start with?
• Answer: This process helps …
– Understand whether the CTQs and CTPs that are
measured are traceable to customer needs.
– Prioritize improvements using the CTQ-CTP
relationship matrix.
– Review priority for improvement in relationship to
measurement capability. (As a containment,
organizations would rather risk losing yield than
sending nonconforming products to customers. 1% of
incorrectly accepted products is 10,000 PPM.)

35. About Engineering Meetings
• Meeting discipline issues narrated in this presentation
are common to any organization in general and not
targeted on any specific organization.
• There is more to engineering meetings than Cpk and
Gage R&R: e.g., engineering changes, machine
maintenance issues, budget control, etc.
• This presentation is targeted to help quality professionals
and engineering professionals involved in quality
improvement and does not suggest replacing the entire
engineering meeting.

36. Acronyms & Definitions
• CTQ: critical to quality (characteristics)
• CTC: critical to cost/customer (characteristics)
• CTP: critical to process (parameter)
• GR&R: gage repeatability & reproducibility
• LSL: lower specification limit
• USL: upper specification limit
• FMEA: failure mode effects analysis
• RPN: risk priority number
• Alpha risk: probability of rejecting good products
• Beta risk: probability of accepting bad products

37. 55
Acknowledgements, References, & Bibliography
• Acknowledgements:
– Ms. Noel Wilson, ASQ - Review, feedback, and support.
– Mr. Steven Hunt- @ Risk Misclassification & Simulation.
– Ms. Cathy Akritas, Minitab Inc- Help with Misclassification macro.
– Mr. Ed Russell, Mr. John Noguera, Mr. Andrew Sleeper - Suggestions
and guidance for Misclassification Simulation.
• References:
– Concepts for R&R Studies, ASQ Press, by Larry B. Barrentine.
– FMEA Minus the Headache,” QP, April 2009, by Govind Ramu.
– Measurement Systems Analysis Manual,AIAG.
– MINITAB 15 Help Menu.
• Bibliography:
– http://www.isixsigma.com/forum/ask_dr_harry.asp?ToDo=view&questId=82&catId=11
– AIAG Statistical Process Control – SPC
– http://www.onesixsigma.com/crystalball/Misclassification-Rates-in-Measurement-Systems-
Analysis-Gauge-RR-01011970
– http://www.qualitytrainingportal.com/resources/fmea/index.htm

38. Questions and Answers
Thanks:Govind Ramu

39. 45
Probability of rejecting good products and accepting bad products for
Ppk=0.43 and GR&R 30%: E.g., CTQ1 - Red Quadrant
Product Distribution Probability of accepting
Good Parts 88.08%
Probability of
rejecting bad parts
8.99% both tails
GRR Error Distribution
Probability of incorrectly
Probability of incorrectly
Accepting bad parts
rejecting good parts
Incorrectly accepted = 12.02% (Beta Risk) Incorrectly rejected = 1.80% (Alpha Risk)
Note: The exact percentage of errors was calculated simulating the distribution with 100,000 random data points using @ Risk software
and MINITAB Macro.
Reference AIAG Manual Measurement System Analysis 3rd Edition- Pages 16-22

40. Probability of rejecting good products and accepting bad products for
Ppk=0.43 and GR&R 10%: E.g., CTQ2 - Yellow Quadrant
Product Distribution
Probability of accepting
Good Parts 89.28%
Probability of
rejecting bad parts
9.77% both tails
GRR Error Distribution
Probability of incorrectly Probability of incorrectly
Accepting bad parts rejecting good parts
Incorrectly accepted = 5.22% (Beta Risk) Incorrectly rejected = 0.68% (Alpha Risk)
Note: The exact percentage of errors was calculated simulating the distribution with 100,000 random data points using @ Risk software
and MINITAB Macro.
Reference AIAG Manual Measurement System Analysis 3rd Edition- Pages 16-22