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[S954]Statistical Design Of Experiments

by Tony Jacowski, Ton

Design of experiments, (DOE) or conjoint analysis as it is known in marketing context, is known to be the most powerful statistical method for establishing the linkage between a customer's decision-making process and the service or product being offered. After effective application of DOE, companies find it easier to gain an insight into the significant Xs affecting a customer's decision-making ability.

Marketing Problems

Eventually, the primary aim of marketing is to calculate the upcoming market share net sales, or profitability of an offering, thus, allowing a company to:

-Foretell customer buying tendency
-Boost customer retention
-Ascertain trade-off strategies during contract negotiation
-Ascertain competitive pricing
-Predict sales
-Control brand equity
-Devise product elements
-Establish price sensitivity
-Forecast and reduce customer switch rates
-Ascertain best market position for new product introductions
-Forecast and optimal response rates for advertising campaigns regarding content creation and allocation channel mix.

The Unknown X and Y

During decision-making processes, customers usually prefer accepting or rejecting a company's offering. There ought to be a way of understanding the connection between factors customers consider before coming to a final decision, as there are times customers are oblivious of the complicated psychological processes behind their decision-making processes.

A standard contingency plan would be to develop an analytical model that involves evident characteristics of the offering. It is a wrong notion among businesses to assume that customers make decisions based on the cost - a criteria they usually follow. Usually customers make decisions based on a complex interplay of psychological and sociological factors.

Divorcing critical-to-quality elements (CTQs) for the external customer (value) and internal customer (cost) allows a company to ascertain an appropriate offering for optimizing both CTQs simultaneously.

Usually a Six Sigma project concentrates on a single Y for simplifying the analysis. Although there is a complicated mapping of Xs to Ys in the mind of the customer, the primary focus is the "yes-or-no" or the single Y. Since this discrete measurement is not as beneficial as a quantitative measure, hence the data gathered is used for driving into the multiple Ys in a customer's thought process by asking them to decide from the given choices. After this is successful, it allows the derivations of an extrapolative model for the decision-making process.

Rating Factors in Decision Making

Customers are incapable of determining ratings on absolute scales, but are considerably good at forming ratings on the basis of the relative scale. A customer's resolution to reject or accept an offering is done in a similar poorly understood and complex process.

Xs and selecting Factor Levels

Studies carried for selecting Xs use the same brainstorming tools employed in DMAIC's Measure phase. A fishbone illustration could be used for charting all probably factors the marketing department wishes to evaluate. In the end, the fishbone diagram should illustrate "a buying decision" effect.

The project should be expanded for including elements of the form of the presentation or include aspects of the offering. One can limit the factors included to a dozen by spanning the project, or carrying out a short customer survey for determining the essential aspect making buying decisions.

Lastly, studies conducted should depict the advantages of inspecting DOE/conjoint analysis applications for Six Sigma projects after being used in marketing. Quantitative predictive models that capture the customer decision-making process are powerful marketing tools for foretelling customer behavior.

One of the valuable tools in the Six Sigma toolbox is Design of Experiments. Design of Experiment (DOE) is a structured technique that helps to uncover relationships often hidden inside mountains of data. Within the structure of a Six Sigma project, Design of Experiments is a structured approach to identifying the factors within a process that contribute to particular effects, then creating meaningful tests that verify possible improvement ideas or theories.

Most of us are familiar with the concept of experimentation within the fields of science and medicine. Experiments can be designed and conducted for any process in any field not just testing physics equations or new drugs or medical procedures. Design of Experiments is a formal statistical methods required to ensure that the testing or piloting of any new improvement ideas maximize the informational potential of the trial and ultimately the return to the business. The basic principles of cause and effect and interaction of factors operate everywhere, including manufacturing and service organizations. Design of Experiments is an organized method for determining the relationships between factors that affect a process and the variable outputs of that process. It also serves to verify if a cause and effect relationship really does exist and to identify the vital few causes of variation.

In short, Design of Experiments within Six Sigma is a performance improvement methodology that uses sophisticated statistical techniques to understand and control variation, thus improving predictability of business processes. Experimental methods are used to quantify previously undefined factors and interactions between factors. This is accomplished through crafting planned experiments where controlled changes of factors will determine which factors have the largest impact on quality characteristics. Though the systematic observance of the experiments and statistical measurements of the results, useful data can be assembled and analyzed to understand the relative importance of different factors to overall process variability.

The basic concepts of Design of Experiments are factors, levels, and responses. A factor is an independent variable. In a planned experiment, the factors are deliberately varied in a predetermined manner. A level is a state of the factor that is deliberately varied. Levels can be discrete (present/absent) or numeric. Experimentation is typically done at two, or occasionally three levels for every factor; each separate level constituting an experimental run. The responses, literally the results of the experimental runs, are measured at each run of each factor-level combination. The response can also be discrete or numerical values.

An efficient experimental design varies the multiple factors in an intelligent and controlled sequence. Response data can then be collected in an intelligible way.

Combining all factors and their levels can become too large and expensive of a task, so informed deductions must be made as to which factors will generate the most pertinent data that will provide enough information for confident results. The sequence of runs in the experiment must be randomized. Randomization is crucial to give all external factors an equal chance to affect every run of the experiment. A non-randomized experiment stands a great risk of external factors acting in a systematic manner, adding noise to the response. Multiple sets of experimental runs, called replication, will provide more data and greater confidence in evaluating the results. If the budget allows, conducting more replications is desirable.

Successfully designed experiments will show the relationship between the change in level of each of the factors and the change in response. Once these relationships are understood, they can be used to find "what's best" solutions to process improvement and variation reduction. Design of Experiments is a crucial part of the Six Sigma methodology. It will allow you to see into the heart of the process and what really drives it.

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Both Tony Jacowski & Peter Peterka are contributors for EditorialToday. The above articles have been edited for relevancy and timeliness. All write-ups, reviews, tips and guides published by EditorialToday.com and its partners or affiliates are for informational purposes only. They should not be used for any legal or any other type of advice. We do not endorse any author, contributor, writer or article posted by our team.

Tony Jacowski has sinced written about articles on various topics from University, Six Sigma and Information Technology. Tony Jacowski is a quality analyst for The MBA Journal. Aveta Solution's Six Sigma Online offers online and certification classes for lean six sigm. Tony Jacowski's top article generates over 90500 views. to your Favourites.

Peter Peterka has sinced written about articles on various topics from Six Sigma, Leadership and Six Sigma. . Peter Peterka's top article generates over 12100 views. to your Favourites.