Introduction to Lean Six Sigma Yellow Belt

Intro to Six Sigma Yellow Belt
Background
Six Sigma was developed by Bill Smith as a systematic method to solve manufacturing problems during his career as an engineer at Motorola and combines methods from Statistics, Project Management and Risk Management.
The main tools for Six Sigma, “Control Charts”, “Statistical Process Control” and “Plan Do Check Act”, were developed in the 1920’s by Walter Shewart at Bell Labs and were adopted by the War Department during World War II as “Army War Standards” to manage quality in long run production of armaments.
Plan, Do, Check, Act was a standard procedure for managers to sequence their problem solving into a repeatable cycle that evolved into Six Sigma’s DMAIC (Define, Measure, Analyze, Improve, Check)
Control Charts are measurements of results written on a chart horizontally that creates a visual graphic of datapoints that enable an operator to spot trends as a process proceeds through its cycle.
Statistical Process Control (SPC) is a quality assurance method that measures samples during a production run to test for variations in finished parts so that machines and processes can be adjusted before the entire batch of parts become nonconforming. Making adjustments based on measurements of samples during a production run reduces scrap because machines and processes are adjusted as soon as a trend towards nonconformance is identified. Without SPC during a production run, nonconforming parts will not be identified until a batch is complete and this results in increased scrap.
6 Sigma is a statistical term to denote a “Standard Deviation” of 3.4 defects out of 1 million opportunities (DPMO) and yields 99.9997%.
Most companies operate between 4 Sigma (66,807 DPMO) which yields 99.34% and 3 Sigma (6210 DPMO) which yields 93.3%.
Other 6 Sigma tools include:
Project Charters – Six Sigma version of Kaizen but much more formal and costly
Data Collection Plans – Who, What, When, Where and How of collecting data for analysis
5 Why Root Cause Analysis – Keep asking “Why?” until you find the root cause.
Fishbone Cause and Effect Diagram for 7M’s of Money, Materials Methods, Manpower, Machines, Mother Nature, and Management)
Performance Boards – Hour by Hour tracking to collect data on problems as they occur
Pareto Charts – Graphical interpretation of data in columns so biggest issues are visible.
Failure Modes Effects Analysis (FMEA) – Risk Management tool with Probability, Impact and Likelihood of Detection multiplied together to compare and prioritize a range of risks.
Brainstorming Decision Tools: SWOT/TOWS, Affinity Process, PICK Charts, Ranking, Voting
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5 Shifts of Performance Boards for Machine 1
Machine 1

Goal

Actual

Difference

Minutes Down

Cause/Reason

7-8

60

50

10

10 min

Changeover to new run

8-9

60

60

0



9-10

60

30

30

30 min

No Parts

10-11

60

0

60

60 min

Breakdown/Machine Jam

12-1

60

60

0



1-2

60

60

0



2-3

60

60

0



3-4

60

60

0



Machine 1

Goal

Actual

Difference

Minutes Down

Cause/Reason

7-8

60

50

10

10

Changeover to new run

8-9

60

60

0



9-10

60

50

10

10 min

No Parts

10-11

60

60

0



12-1

60

60

0



1-2

60

30

30

30 min

No Parts

2-3

60

0

60

60 min

Breakdown/Machine Jam

3-4

60

60

0



Machine 1

Goal

Actual

Difference

Minutes Down

Cause/Reason

7-8

60

50

10

10 min

Changeover to new run

8-9

60

60

0



9-10

60

50

10

10 min

No Parts

10-11

60

60

0



12-1

60

60

0



1-2

60

30

30

30 min

No Parts

2-3

60

0

60

60 min

Breakdown/Jam

3-4

60

60

0



Machine 1

Goal

Actual

Difference

Minutes Down

Cause/Reason

7-8

60

60

0



8-9

60

60

0



9-10

60

60

0



10-11

60

50

10

10 min

No Parts

12-1

60

60

0



1-2

60

30

30

30 min

No Parts

2-3

60

0

60

60 min

Break Down/Machine Jam

3-4

60

0

60

60 min

Operator Injury/clearing jam

Machine 1

Goal

Actual

Difference

Minutes Down

Cause/Reason

7-8

60

40

20

20 min

Safety Training (LOTO)

8-9

60

60

0



9-10

60

60

0



10-11

60