Pre-DMAIC Tools
SWOT
PDCA
5 S Model
SWOT Analysis
Strengths – Weaknesses – Opportunities and Threats
Time Management
Urgent Important Important Not Urgent Urgent Not Important Not Important – Not Urgent
Plan Do Check Act
Reminder DMAIC is the flagship When Would it Make Sense to Use PDCA?
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
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.
Plan Do Check Act - CHECK
Check whether the small scale or experimental changes are achieving the desired result or not.
Plan Do Check Act - ACT
Act to implement changes on a larger scale if the experiment is successful. Abandon if not.
5s Model
Mini-Presentation
5s Prevents This!
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5s Wheel
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5s Model Sort Set in Order Shine Standardize Sustain
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Japanese Words
1.) Sort (Seiri)
2.) Straighten (Seiton)
3.) Shine (Seiso)
4.) Standardize (Seiketsu)
5.) Sustain (Shitsuke)
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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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Student Exercise Workbook Page 12 – In Your Own Words
Sort Set in Order Shine Standardize Sustain
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Control Phase
Control Plan vs. Transition Plan
Design a Transition Plan - Control
What Components Should Be in a Good Transition Plan?
Transition Plan Template
Where information can be found How to use the tools Historical Account Resources Best practices
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FMEA Thinking
FMEA Forms
Control Phase
FMEA Form
Can Be Used in Define 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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FMEA Example
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Additional FMEA Information
Severity Occurrence Detection
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SeverityEffect Criteria: Severity of Effect Defined Ranking
Hazardous: Without WarningMay endanger operator. Failure mode affects safe vehicle operation and / or involves noncompliance with government regulation. Failure will occur WITHOUT warning.
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Hazardous: With WarningMay endanger operator. Failure mode affects safe vehicle operation and / or involves noncompliance with government regulation. Failure will occur WITH warning.
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Very HighMajor disruption to production line. 100% of product may have to be scrapped. Vehicle / item inoperable, loss of primary function. Customer very dissatisfied.
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HighMinor 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
ModerateMinor 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
LowMinor 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 LowMinor 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
MinorMinor 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 MinorMinor 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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OccurrenceProbability of Failure Possible Failure Rates Cpk Ranking
Very High: 1 in 2 < 0.33 10
Failure is almost inevitable 1 in 3 0.33 9
High: Generally associated with processes similar to previous 1 in 8 0.51 8
processes that have often failed 1 in 20 0.67 7
Moderate: Generally associated with processes similar to 1 in 80 0.83 6
previous processes which have 1 in 400 1.00 5
experienced occasional failures, but not in major proportions 1 in 2,000 1.17 4
Low: Isolated failures associated with similar processes 1 in 15,000 1.33 3
Very Low: Only isolated failures associated with almost identical processes
1 in 150,000 1.5 2
Remote: Failure is unlikely. No failures ever associated with almost identical processes
1 in 1,500,000 1.67 1
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DetectionDetection 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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Additional Charts
Popular in Lean Six Sigma
ReminderSeven PI Tools
Cause and Effect Diagrams Flow Charts Check Sheets Histograms Pareto Charts Scatter Diagrams Control Charts
Popular Charts/Maps
Swim Lane Chart Spaghetti Diagram Gantt Chart Pert Charts Value Stream Map (VSM)
Popular Charts/Maps
Swim &Spaghetti
Swim Lane
Spaghetti DiagramFlow of Metals (Traditional)
Spaghetti DiagramForest Products (More Modern)
Popular Charts
Gantt Chart and Pert Charts
Gantt Chart
Jan 2010 Feb 2010 March 2010 April 2010 May 2010 June 2010 July 2010
PERT (Australian Jobbing Process)Critical Path – What Has To Happen First?What Can Happen in Tandam
Student Exercise – Choose a Topic
Show a Brief WBS with Bars Representing Timeline (Gantt)
Show the Same Example as a PERT
Mapping and Analysis
Takes the Components of Swim Lane, PERT, and a Process Map
Value Stream
VSM Symbols Used to Map Only the Value-Added Processes
Remember
Only the Processes that Directly Contribute to the Improvement
Are Considered Value Add
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
Example Value Stream Map
Example #2: Value Stream Map
Example #3 Value Stream Map
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
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Statistical Process ControlChart Analysis
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CONTROL CHARTS (SPC Charts)
STATISTICAL TOOLS FOR QUALITY IMPROVEMENT
UCL
LCL
CL
1 2 3 4 5 6 7 8 9
Qu
alit
y P
ara
met
er
Sample No.
"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).
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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
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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.
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STATISTICAL TOOLS FOR QUALITY IMPROVEMENT
CYCLES: Short trends in the data that occur in repeated patterns so that the pattern becomes predictable or systematic.
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STATISTICAL TOOLS FOR QUALITY IMPROVEMENT
TRENDS: Long series of points that lack a change of direction.
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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.
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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.
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STATISTICAL TOOLS FOR QUALITY IMPROVEMENT
MIXTURE: Un-natural length of the lines joining the points to create a "saw tooth" effect.
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STATISTICAL TOOLS FOR QUALITY IMPROVEMENT
STRATIFICATION: un-naturally small fluctuations and an absence of points near the control limits
What Am I Measuring?
Attribute Data Counting Whole Numbers How Many
Variable Data Measuring Numbers with Decimals How Much
Can I have More Than One Defect?
Attribute Data
Flipping Coin Sport Contests Lotteries
Variable Data
Height Weight Time
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
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
c Control ChartNumber of Defects
Number of OSHA Injuries
Np Control Chartsplots the number of defects in each subgroup of the same size – how many Red Beads in Each Sample
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