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Planning Experiments &DOE
Planning the experiment is probably the most important task in the Improve phase when using DOE. For planning to be done well, some experts estimate that 10-25% of your time spent should be devoted to planning and organizing the experiments.
The purpose of DOE is to create an observable event from which data may be extracted and decisions made about the best methods to improve the process. DOE may be used most effectively in the following situations
- Identifying factors that produce a specific response or outcome
- Selecting between alternative approaches to effect the best outcome
In DOE, a full factorial design combines levels for each factor with levels for all other factors. This basic design ensures that all combinations are used, but if factors are many, this design may take too much time or be too costly to implement. In either case, a fractional factorial design is selected as the number of runs is fewer with fewer treatments.
The project team decides the exact steps to follow in the Improve phase. Steps to include in the Improve phase may actually be identified in the Measure and Analyze phases and should be noted to expedite later planning in the Improve phase. Planning the experiment(s) to be conducted in the improve phase using DOE is
- Establish experiment objectives – Objectives differ per project, but the designs typically fall into three categories to support different objectives
- Screening – used to identify which factors are most important.
- Characterization – used to quantify the relationships and interaction between several factors.
- Optimization – used to develop a more precise understanding of just one or two variables.
- Identify factors to be considered –
- Label both input variables (x factors) and output variables (y responses) in the experiment.
- Use information collected in prior phases to assist in the identification process.
- Finalize an experiment design
- Select a design for the experiment.
- Choose a design type (full factorial, fractional factorial, or others) that meets the experiment’s objectives.
- Determine how the factors are measured.
- Consider the resources needed and determine whether a practice run or pilot experiment may be needed.
- Run the experiment
- Run the experiment and collect the data. Place initial data in the results column of a design array, a graphical representation of the experiment factors and results.
- Minimize chance for human error by carefully planning where human error could occur and allow for the possibility in the planning process.
- Randomize the runs to reduce confounding.
- Document the results as needed depending on the experiment.
- Analyze the results of the experiment
- Review the results of the experiment(s).
- Examine the relationships among input variables (factors) acting together and with regards to the output variable(s) (responses).
- Make decisions on next steps
- Based on the results, determine next steps.
- Are additional runs of the experiment needed?
- Do the levels need to be modified prior to conducting the experiment again?
- If the results point to an optimal solution, implement the factors and the levels of choice and look at the Control phase to sustain the desired improvements.
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