Determine the following

- Response(s) output(s) to be optimized (consider location and spread)
- Factors - inputs to be varied
- Factor levels - high and low settings for each factor
- Resource constraints - maximum number of runs for all sequential experiments, factor setting limits, run order, etc.

Determine Sample Size For Each Run

- Variables data: determine sample size for location and/or spread effects (see manuals for calculations)
- Attribute data: determine sample size for proportion (see manuals for calculations)

Only use replication for small full factorials. Repetition is generally more resource efficient. Variance as a response requires repetition or replication.

Run The Experiment

- Propriety of conduct is key - minimize experimental noise
- Randomize runs

Location

Find the MSFE based on the standard deviation of noise effects.

Spread

Use repetitions and compute the standard deviation of the repetitions for each run.

Code data as ln(s^{2}) and treat as a location effect.

OR

Leave uncoded. The effect is the big s^{2} divided by the small s^{2}. Compare to the critical value of F.

Location

Find the MSFE based on the standard deviation of noise effects.

Spread

Use repetitions and compute the standard deviation of the repetitions for each run.

Code data as ln(s^{2}) and treat as a location effect.

OR

Leave uncoded. The effect is the big s^{2} divided by the small s^{2}. Compare to the critical value of F.

Proportion

Code data as sin^{-1} (P^{1/2}) and treat as a location effect

OR

leave uncoded and find the MSFE for proportion.

Count

Code data as (c + 3/8)^{1/2} and treat as a location effect.

Analyze The Results

Determine the significance of effects by using the decision tree below.

- Measurements
- Full Factorials Design: Replicated
- Full Factorials Design: Unreplicated
- Fractional Design
- Attribute

Is An Additional Experiment Required?

- If no, proceed to step 7
- If yes, go back to step 1 and begin planning the next experiment

Check The Model

Perform an analysis of residuals

- Normal distribution
- Average to zero
- Constant variance
- Randomized.

Perform a pilot run, if desired.

Implement The Improvement

- Confirm the benfit
- Communicate results
- Transfer knowledge to like processes
- Transfer knowledge to other locations