Using Design of Experiments (DOE) for Decision Analysis
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CP_070828_Tang-Otto-Seering_ICED.pdf
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Author(s)
Tang, Victor
Otto, Kevin N.
Seering, Warren P.
Date Issued
August 28, 2007
Abstract
We take an engineering design approach to a problem of the artificial - corporate decision-analysis
under uncertainty. We use Design of Experiments (DOE) to understand the behaviour of systems
within which decisions are made and to estimate the consequences of alternative decisions. The
experiments are a systematically constructed class of gedanken (thought) experiments comparable to
“what if” studies, but organized to span the entire space of controllable and uncontrollable options. We
therefore develop a debiasing protocol to forecast and elicit data. We consider the composite
organization, their knowledge, data bases, formal and informal procedures as a measurement system.
We use Gage theory from Measurement System Analysis (MSA) to analyze the quality of the data, the
measurement system, and its results. We report on an in situ company experiment. Results support the
statistical validity and managerial efficacy of our method. Method-evaluation criteria also indicate the
validity of our method. Surprisingly, the experiments result in representations of near-decomposable
systems. This suggests that executives scale corporate problems for analyses and decision-making.
This work introduces DOE and MSA to the management sciences and shows how it can be effective to
executive decision making.
Subjects
decision analysis
design of experiments (DOE)
gage R&R
complex systems
business process
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