|Abstract or Summary
- Product development efforts are extremely important to a company's success in
today's global competitive business environment. Yet, these highly consequential
efforts are terribly nebulous to a point that past experiences are inherently underutilized.
This thesis demonstrates a methodology to quantify past product development efforts in
an attempt to better utilize past experiences.
The methodology is centered around conducting an observational study, using
regression analysis to expose relationships between various aspects of past product
development efforts. In the study, products developed in the past serve as observational
units, various cost and time variables serve as dependent variables, and a variety of
variables characterizing product development efforts serve as independent variables.
The nominal group technique is employed, along with formal personal interviewing, to
identify the many different variables targeted for data collection.
Regression analysis is used to test and identify relationships between the multitude of combinations of dependent and independent variables. Three simple model forms are used to 'capture' any potential relationship: a straight line model, an exponential model, and a natural-logarithmic model. Dependent-independent variable combinations that have met a given statistical criteria, in one of these three model forms, are labeled statistically notable, and later classified as practically relevant.
The applicability of the methodology is demonstrated by presenting 'generic' results obtained by making use of information and historical data from a well established electronics company who wishes to be referred to as Company X. These results show that cost and/or time increase with the increase in: the number of parts in a product, the number of assembly processes, quality/utility of the product, or a product performance enabling specification. Furthermore, product shape is found to be associated with cost and time. Interestingly though, only a relatively few variables were found to be associated with time as compared to cost. The statistical models that were identified can serve as a quantitative historical record and perhaps a prediction tool for Company X, giving them a competitive advantage in their future product development efforts.