DESIGN OF EXPERIMENT (DOE)

Design of Experiment (DOE) is a method of simultaneously investigating the effects of multiple variables on an output variable or response. It is a key tool in the Six Sigma methodology because it effectively explores the cause and effect relationship between numerous process variables and the output. Fractional factorial designs are good alternatives to a full factorial design, especially in the initial screening stage of a project.

Design of experiments was first conceived and developed by Sir Ronald A. Fisher in the 1920s and 1930s. Today, business & industrial experimenters are confronted by significant degrees of uncontrollable variation in such areas as raw materials, human operators or environmental conditions. Such problems can be overcome by running sufficiently large experiments. Unfortunately, large experiments are often too expensive, too time consuming, or both. Fortunately, Fisher's solutions to these problems work just as well in 21st century Six Sigma as they did in 20th century agriculture. In fact, Fisher's methods of design and analysis have become international standards in business and applied science.

There are different softwares that can run the experiments and throw up the desired results in no time. There are, of course, key disciplines in design of experiments that depend on the experimenters and not the software. These include choosing appropriate responses (output variables) and factors (input variables), setting appropriate factor ranges or levels, creating documentation for everyone involved in the experiment, managing the experiment as it takes place, reporting and presenting results, deciding whether to further optimize the process or just run a confirmation experiment. In other words, a designed experiment is a DMAIC cycle-within-a-cycle.


Programme Objectives

SSA conducts Training session on Design of Experiments (DOE), assisted by Business Simulation Softwares.

This Programme provides an insight into:

	Explaining the concept of Design of Experiment
	Providing a hands-on experience to the Participants on designing & interpreting the results
	Conducting a DOE using Statistical Software

	Companies who have adopted Process Quality Improvement tools in their Business like Six Sigma, Lean, BPMS, etc.
	Companies who are into investigation of the effect of multiple variables on an output variable
	Companies involved in Projects involving significant degrees of uncontrollable variations
	Companies who are interested in re-engineering

	Understanding DOE : a. What is DOE? b. Objectives of DOE c. Building Blocks- Terminologies, Case & Examples
	Creating Factorial Design
	View design & enter data on the worksheet
	Analyzing design & interpreting the results
	Create & interpret main effects & interaction plots
	Case Study & Examples from Service & Manufacturing Industries
	Using Statistical Software for DOE

	It is used to identify Vital few sources of variation
	It defines the relationship between the inputs & outputs
	It is used to study all possible interactions between the 'Vital Few' variables
	It minimizes the number of test runs (as compared to intuitive approaches of one-factor-at-a-time etc.)

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