1. Pre-DMAIC Tools
SWOT
PDCA
5 S Model

2. Strengths – Weaknesses – Opportunities and Threats
SWOT Analysis
Strengths – Weaknesses – Opportunities and Threats

3. Time Management Urgent Important Important Not Urgent
Urgent Not Important
Not Important – Not Urgent

4. Plan Do Check Act Reminder DMAIC is the flagship
When Would it Make Sense to Use PDCA?

5. Plan Do Check Act - PLAN
Plan to improve your operations first by finding out what things are going wrong
Identify the problems faced
Come up with ideas for solving these problems

6. Plan Do Check Act – DO
Do changes designed to solve the problems on a small or experimental scale first.
This minimizes disruption to routine activity while testing whether the changes will work or not.

7. Plan Do Check Act - CHECK
Check whether the small scale or experimental changes are achieving the desired result or not.

8. Plan Do Check Act - ACT
Act to implement changes on a larger scale if the experiment is successful. Abandon if not.

9. 5s Model 5S Mini-Presentation
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10. 5s Prevents This!
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11. 5s Wheel
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12. 5s Model Sort Set in Order Shine Standardize Sustain
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13. Japanese Words 1.) Sort (Seiri) 2.) Straighten (Seiton)
3.) Shine (Seiso)
4.) Standardize (Seiketsu)
5.) Sustain (Shitsuke)
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14. 5Cs Clear Out: Separate the essential from the non essential
Configure: A place for everything and everything in its place.
Clean and Check: Manually clean to spot abnormal conditions.
Conformity: Ensures that the standard is maintained and improved.
Custom and Practice: Everyone follows the rules, understands the benefits and contributes to the improvement.
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Not to be reprinted without the expressed written permission by SSD Global, Inc.

15. Student Exercise Workbook Page 12 – In Your Own Words
Sort
Set in Order
Shine
Standardize
Sustain
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Not to be reprinted without the expressed written permission by SSD Global, Inc.

16. Control Plan vs. Transition Plan
Control Phase
Control Plan vs. Transition Plan

17. Design a Transition Plan - Control
What Components Should Be in a Good Transition Plan?
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18. Transition Plan Template
Where information can be found
How to use the tools
Historical Account
Resources
Best practices
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19. FMEA Forms Control Phase
FMEA Thinking
FMEA Forms
Control Phase

20. FMEA Form Can Be Used in Define Can Be Used in Measure
As part of the historical data
Can Be Used in Measure
To determine what areas should be measured
Can Be Used in Analyze
To study variation
Can Be Built in Control
To sustain the process
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21. FMEA Example
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Not to be reprinted without the expressed written permission by SSD Global, Inc.

22. Additional FMEA Information
Severity
Occurrence
Detection
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Not to be reprinted without the expressed written permission by SSD Global, Inc.

23. Severity
Effect
Criteria: Severity of Effect Defined
Ranking
Hazardous: Without Warning
May endanger operator. Failure mode affects safe vehicle operation and / or involves noncompliance with government regulation. Failure will occur WITHOUT warning. 10
Hazardous: With Warning
May endanger operator. Failure mode affects safe vehicle operation and / or involves noncompliance with government regulation. Failure will occur WITH warning. 9
Very High
Major disruption to production line. 100% of product may have to be scrapped. Vehicle / item inoperable, loss of primary function. Customer very dissatisfied. 8
High
Minor disruption to production line. Product may have to be sorted and a portion (less than 100%) scrapped. Vehicle operable, but at a reduced level of performance. Customer dissatisfied. 7
Moderate
Minor disruption to production line. A portion (less than 100%) may have to be scrapped (no sorting). Vehicle / item operable, but some comfort / convenience item(s) inoperable. Customers experience discomfort. 6
Low
Minor disruption to production line. 100% of product may have to be reworked. Vehicle / item operable, but some comfort / convenience item(s) operable at reduced level of performance. Customer experiences some dissatisfaction. 5
Very Low
Minor disruption to production line. The product may have to be sorted and a portion (less than 100%) reworked. Fit / finish / squeak / rattle item does not conform. Defect noticed by most customers. 4
Minor
Minor disruption to production line. A portion (less than 100%) of the product may have to be reworked on-line but out-of-station. Fit / finish / squeak / rattle item does not conform. Defect noticed by average customers. 3
Very Minor
Minor disruption to production line. A portion (less than 100%) of the product may have to be reworked on-line but in-station. Fit / finish / squeak / rattle item does not conform. Defect noticed by discriminating customers. 2
None
No effect. 1
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24. Possible Failure Rates
Occurrence
Probability of Failure
Possible Failure Rates
Cpk
Ranking
Very High:
³ 1 in 2
< 0.33 10
Failure is almost inevitable
1 in 3
³ 0.33 9
High: Generally associated with processes similar to previous
1 in 8
³ 0.51 8
processes that have often failed
1 in 20
³ 0.67 7
Moderate: Generally associated with processes similar to
1 in 80
³ 0.83 6
previous processes which have
1 in 400
³ 1.00 5
experienced occasional failures, but not in major proportions
1 in 2,000
³ 1.17 4
Low: Isolated failures associated with similar processes
1 in 15,000
³ 1.33 3
Very Low: Only isolated failures associated with almost identical processes
1 in 150,000
³ 1.5 2
Remote: Failure is unlikely. No failures ever associated with almost identical processes
£ 1 in 1,500,000
³ 1.67 1
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25. Detection
Detection
Criteria: Likelihood the existence of a defect will be detected by test content before product advances to next or subsequent process
Ranking
Almost Impossible
No known control(s) available to detect failure mode 10
Very Remote
Test content must detect 60 % of failures 9
Remote
Test content must detect 65 % of failures 8
Very Low
Test content must detect 70 % of failures 7
Low
Test content must detect 75 % of failures 6
Moderate
Test content must detect 80 % of failures 5
Moderately High
Test content must detect 85 % of failures 4
High
Test content must detect 90 % of failures 3
Very High
Test content must detect 95 % of failures 2
Almost Certain
Test content must detect 99.5 % of failures 1
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26. Popular in Lean Six Sigma
Additional Charts
Popular in Lean Six Sigma

27. Reminder Seven PI Tools
Cause and Effect Diagrams
Flow Charts
Check Sheets
Histograms
Pareto Charts
Scatter Diagrams
Control Charts

28. Popular Charts/Maps Spaghetti Diagram Gantt Chart Pert Charts
Swim Lane Chart
Spaghetti Diagram
Gantt Chart
Pert Charts
Value Stream Map (VSM)

29. Popular Charts/Maps
Swim &Spaghetti

30. Swim Lane

31. Spaghetti Diagram Flow of Metals (Traditional)

32. Spaghetti Diagram Forest Products (More Modern)

33. Gantt Chart and Pert Charts
Popular Charts
Gantt Chart and Pert Charts

34. Jan 2010 Feb 2010 March 2010 April 2010 May 2010 June 2010 July 2010
Gantt Chart
Jan Feb March April 2010 May 2010 June July 2010

35. PERT (Australian Jobbing Process) Critical Path – What Has To Happen First? What Can Happen in Tandam

36. Student Exercise – Choose a Topic
Show a Brief WBS with Bars Representing Timeline (Gantt)
Show the Same Example as a PERT

37. Value Stream Mapping and Analysis
Takes the Components of Swim Lane, PERT, and a Process Map

38. VSM Symbols Used to Map Only the Value-Added Processes

39. Remember
Only the Processes that Directly Contribute to the Improvement Are Considered Value Add

40. Waste/Non Value Can Mean
Something to Reduce
Something Redundant
Something that does not Contribute to the main process
In this case it set aside and not shown on the map
Doesn’t mean “waste” in the traditional sense of the word

41. Example Value Stream Map

42. Example #2: Value Stream Map

43. Example #3 Value Stream Map

44. Value Stream Maps Map the Process
Take Out all the Things that Don’t Contribute to the Process
Establish as the Current Value Stream
Use the Components to Create a Future Picture
End up with a Before and After
Usually depicted by using VSM Symbols

45. Statistical Process Control
Chart Analysis

46. CONTROL CHARTS (SPC Charts)
STATISTICAL TOOLS FOR QUALITY IMPROVEMENT
CONTROL CHARTS (SPC Charts)
"Control Chart” is the most popular and effective statistical tool for process control.
It has a center line (CL) and upper & lower control limits (UCL / LCL).
UCL
Quality Parameter CL
LCL 1 2 3 4 5 6 7 8 9
Sample No.

47. There are some seven (7) common types of control chart patterns.
STATISTICAL TOOLS FOR QUALITY IMPROVEMENT
There are some seven (7) common types of control chart patterns.
1. Natural (Random)
2. Cycles
3. Trends
4.Grouping or Strays
5. Sudden shifts in the level
6. Mixtures
7. Stratification

48. STATISTICAL TOOLS FOR QUALITY IMPROVEMENT
NATURAL (RANDOM) : In this case there will be no systematic trends or patterns. Data points scatter randomly above and below the central line.

49. CYCLES: Short trends in the data that occur in
STATISTICAL TOOLS FOR QUALITY IMPROVEMENT
CYCLES: Short trends in the data that occur in
repeated patterns so that the pattern becomes
predictable or systematic.

50. TRENDS: Long series of points that lack a change of direction.
STATISTICAL TOOLS FOR QUALITY IMPROVEMENT
TRENDS: Long series of points that lack a change
of direction.

51. STATISTICAL TOOLS FOR QUALITY IMPROVEMENT
GROUPING / STRAYS: Grouping occurs when the data cluster together in a non-random pattern.
Strays result from a single measurement that is greatly different from the others.

52. STATISTICAL TOOLS FOR QUALITY IMPROVEMENT
SUDDEN SHIFT IN LEVEL: A sudden shift in level is shown by a change in one direction. A number of points will appear on one side of the control chart.

53. STATISTICAL TOOLS FOR QUALITY IMPROVEMENT
MIXTURE: Un-natural length of the lines joining the points to create a "saw tooth" effect.

54. STRATIFICATION: un-naturally small fluctuations
STATISTICAL TOOLS FOR QUALITY IMPROVEMENT
STRATIFICATION: un-naturally small fluctuations
and an absence of points near the control limits

55. What Am I Measuring? Attribute Data Variable Data
Counting
Whole Numbers
How Many
Variable Data
Measuring
Numbers with Decimals
How Much
Can I have More Than One Defect?

56. Attribute Data
Flipping Coin
Sport Contests
Lotteries

57. Variable Data
Height
Weight
Time

58. Common Attribute Control Charts
c Control Chart (Counting)
How many times does an event happen?
u Control Chart
Count by units (units can be varying sizes)
np Control Chart
Number of defects units per sample
p Control Chart
Percentage of defects per sample

59. Common Variable Charts
Looking at the Variation in a Process
Two Types:
Variation Between Samples
X charts
Within the Samples
R & S charts within the samples

60. c Control Chart Number of Defects
Number of OSHA Injuries

61. Np Control Charts plots the number of defects in each subgroup of the same size – how many Red Beads in Each Sample