Accounting for intra-industry technological heterogeneity in the measurement of operations efficiency

Abstract

In operations management literature, efficiency is usually measured using parallel shifts of production functions. This practice is based on the assumption that operations technologies are similar across decision-making units. Relaxing this assumption is essential as firms endowed with heterogeneous operational technologies develop distinctive operational resources and capabilities that vary systematically within an industry, resulting in non-parallel shifts of production functions. By relaxing the assumption of parallel production functions, we focus on technological differences as a measure of inefficiency in production and use non-parametric local linear estimates. Our approach based on Bayesian methods and stochastic dominance is novel in that it models for non-parallel production curve slopes that account for unknown frontier technology which is not observed but can be estimated using intra-industry variation in individual firm operational technologies. The proposed approach makes an important contribution to operations management research by relaxing a non-trivial assumption of parallel shifts of production functions in efficiency analysis.