Problem Resolution Optimization
[article]
Summary:
No matter how well we plan and execute software development, defects are generated and can escape to the customers. Failure to quickly resolve software problems leads to negative consequences for our customers and increases internal business costs. A quick deterministic
method to prioritize problems and implement their solution helps to reduce cycle time and costs. Achieving this goal requires several steps. The first is to determine a model that links problem resolution performance to institutional variables and problem characteristics. Statistical Design of
Experiments (DOE) is a tool that provides data requirements for estimating the impacts of these variables on problem resolution. Once data has been gathered, the results of statistical analysis can be input into a mathematical optimization model to guide the organization.
This paper describes such an analysis.
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About the author
Don Porter graduated from Northern Illinois University, where he received the Master of Arts Degree in Economics in 1978 and the Master of Science in Statistics in 1980. His undergraduate work focused on Economics and Mathematics. Don taught courses in Economics, Mathematics, and Statistics until 1985, when he entered the business world as a statistical consultant. He has worked for several Fortune 500 companies, including Continental Telephone, Ameritech, Abbott Labs, BP-Amoco and Motorola, his current employer. His clientele also included several smaller companies in the Chicago area. His experience includes statistical modeling, experimental design, sampling design, statistical quality control and process improvement. In his free time Don is a diligent student of the Bible as well as Ancient Near Eastern religions, history and culture. He likes to talk to people about these topics. He also keeps his pocket billiard skills honed on his basement pool table.
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