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The Quality Improvement Model. Define Process. Select Measures. Collect & Interpret Data. Collect & Interpret Data: Diagnosing Variability. Is Process Stable ?. No. Investigate & Fix Special Causes. Purpose: Begin to understand why there is variability in data collected. Yes.

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the quality improvement model
TheQualityImprovementModel

Define Process

Select Measures

Collect & Interpret Data

Collect & Interpret Data: Diagnosing Variability

IsProcessStable?

No

Investigate & Fix Special Causes

Purpose: Begin to understand why there is variability in data collected

Yes

IsProcessCapable?

No

Improve Process Capability

Yes

Use SPC to Maintain Current Process

what size shoe do you wear

1 2 3 4 5 6 7 8 9 10 11 12

What size shoe do you wear?

Size 11 Shoes

Why aren’t size 11’s all the same size?

gc chdm test method example
GC CHDM Test Method Example

Why don’t we get the same %Water value?

cause effect matrix a tool to identify and quantify sources of variation
Cause & Effect Matrix – A Tool to Identify and Quantify Sources of Variation

Cause & Effects Matrix

cause and effects matrix
Cause and Effects Matrix
  • Relates the key inputs to the key outputs (customer requirements) using the process map as the primary information source
  • Key outputs are scored as to importance to the customer
  • Key inputs are scored as to relationship to key outputs
  • Pareto of key inputs to evaluate in the FMEA and control plans
  • Input into the initial evaluation of the Process Control Plan
cause effect matrix steps
Cause & Effect Matrix Steps
  • Identify key customer requirements (outputs) from process map or other sources
  • Rank order and assign priority factor to each Output (usually on a 1 to 10 scale)
  • Identify all process steps and materials (inputs) from the Process Map
  • Evaluate correlation of each input to each output
    • low score: changes in the input variable (amount, quality, etc.) have small effect on output variable
    • high score: changes in the input variable can greatly affect the output variable
  • Cross multiply correlation values with priority factors and sum for each input
examples
Examples

Note: Only a partial list

bottle production example
Bottle Production Example

Note: Only a partial Map

cause effect matrix form
Cause & Effect Matrix Form

1. List Key Outputs

Excel Template C&E Matrix.XLS

bottle production example1
Bottle Production Example

1. List Key Outputs

The Outputs are defined in Step 1 of Process Mapping

cause effect matrix form1
Cause & Effect Matrix Form

2. Rank Outputs as to Customer importance

bottle production example2
Bottle Production Example

2. Rank Outputs as to Customer importance

cause effect matrix form2
Cause & Effect Matrix Form

3. List Key Inputs by Process Step

Note: Information obtained from process map

bottle production example3
Bottle Production Example

3. List Key Inputs by Process Step

This step uses the Process Map inputs directly. Notice the Process Inputs follow the Process map step-by-step.

Note: Only a partial list of inputs

cause effect matrix form3
Cause & Effect Matrix Form

4. Relate Inputs to Outputs

relating inputs to customer requirements
Relating Inputs to Customer Requirements
  • You are ready to relate the customer requirements to the process input variables
  • Correlational scores: No more than 4 levels
    • 0, 1, 3 and 9
  • Assignment of the scoring takes the most time
  • To avoid this, spell out the criteria for each score:

0= No correlation

1= The process input only remotely affects the customer requirement

3 = The process input has a moderate effect on the customer requirement

9 = The process input has a direct and strong effect on the customer requirement

bottle production example4
Bottle Production Example

4. Relate Inputs to Outputs

Note: Only a partial list of inputs

This is a subjective estimate of how influential the key inputs are on the key outputs

cause effect matrix form4
Cause & Effect Matrix Form

5. Cross-multiply and prioritize

Sum of (Rating x Correlation Score) values for all Requirements

bottle production example5
Bottle Production Example

5. Cross-multiply and prioritize

Note: Only a partial list of inputs

We now start getting a feel for which variables are most important to explaining variation in the outputs

bottle production example6
Bottle Production Example

We have sorted on the cross-multiplied numbers and find that the input variables in the box above are the most important

We can now evaluate the control plans for these input variables

Note: Only a partial list of inputs

general approach
General Approach
  • Place the outputs across the top of the matrix and rank
    • Place inputs down the side of the matrix starting with the first process step and moving to the last
    • This approach is okay for small process with relatively few steps
  • Should only be used for processes with a relatively small number of steps and inputs
focused approach
Focused Approach
  • Phase I
    • Place the outputs across the top of the matrix and rank
    • Place the process steps down the side of the matrix
    • Correlate process step to outputs
    • Pareto the process steps
  • Phase II
    • Start a new C&E Matrix with the inputs from the top three or four process steps
    • Recommended when first starting a project
      • Focuses the efforts and gives the team a feeling that they’re working on the important process steps first
      • Gives you a running start at the FMEA and preliminary Control Plan Analysis
control plan evaluation
Control Plan Evaluation
  • Standard Operating Procedures
    • Do they exist?
    • Are they understood?
    • Are they being followed?
    • Are they current?
    • Is operator certification performed?
    • Is there a process audit schedule?
control plan evaluation1
Control Plan Evaluation
  • Controllable Input Variables:
    • How are they monitored?
    • How often are they verified?
    • Are optimum target values and specifications known?
    • How much variation is there around the target value?
    • How consistent are they?
  • Other Questions:
    • How often is the input variable out of control?
    • Which input variables should have control charts?