Pre-DMAIC Tools SWOT PDCA 5 S Model. SWOT Analysis Strengths – Weaknesses – Opportunities and...

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!

10Not to be reprinted without the expressed written permission by SSD Global, Inc.

5s Wheel


5s Model Sort Set in Order Shine Standardize Sustain


Japanese Words

1.) Sort (Seiri)

2.) Straighten (Seiton)

3.) Shine (Seiso)

4.) Standardize (Seiketsu)

5.) Sustain (Shitsuke)


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.


Student Exercise Workbook Page 12 – In Your Own Words

Sort Set in Order Shine Standardize Sustain


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


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

Not to be reprinted without the expressed written permission by SSD Global, Inc.

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Not to be reprinted without the expressed written permission by SSD Global, Inc.

FMEA Example

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Additional FMEA Information

Severity Occurrence Detection


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.

9

Very HighMajor disruption to production line. 100% of product may have to be scrapped. Vehicle / item inoperable, loss of primary function. Customer very dissatisfied.

8

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


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


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


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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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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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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CYCLES: Short trends in the data that occur in repeated patterns so that the pattern becomes predictable or systematic.

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TRENDS: Long series of points that lack a change of direction.

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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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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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MIXTURE: Un-natural length of the lines joining the points to create a "saw tooth" effect.

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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

Pre-DMAIC Tools SWOT PDCA 5 S Model. SWOT Analysis Strengths – Weaknesses – Opportunities and...

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Transcript of Pre-DMAIC Tools SWOT PDCA 5 S Model. SWOT Analysis Strengths – Weaknesses – Opportunities and...