Production Cost Estimation by Linear Regression Analysis: a Case Study of Model Selection in Manufacturing

Accurate cost prediction during a new product development process is an important factor influencing the ability of manufacturing firms to survive in today’s competitive markets. Various methods have been proposed to predict costs during new product development processes. Although most of these methods produce point estimates, in practice, it is more realistic and useful for a method to provide interval predictions. In this paper, the application of Ordinary Least Squares (OLS) regression is considered in order to model available past data of production cost, and thus to compute interval predictions as well as point estimates for similar new products. With reference to a real case study in manufacturing, the issue of model selection is considered and a comparison of some commonly used criteria (functions of model fit penalized for model complexity) for selecting the best regression equation is analyzed. The objective of this study is to discuss the effect that the criterion used for selecting the best regression equation could have on the precision and accuracy of the prediction. Labor time data concerned the development of new sofa models were exploited as reference test case. The OLS regression is applied to several sets of labor time data and validated with respect to its fitting and predictive accuracy. The results of this study give insight into the efficacy of OLS regression for production cost estimation, and evidence on which criterion appears suitable to be used for model selection.