Manufacturing performance: evaluation and determinants

Abstract

Purpose

Many studies examine manufacturing performance along individual benchmarking dimensions. This study aims to develop a performance metric based on quality and output volume among other variables to assess a firm's manufacturing competitiveness in relation to its major rivals.

Design/methodology/approach

The relative manufacturing performance is measured by data envelopment analysis (DEA). Several key manufacturing practices are examined for their impact on performance. They are research and development (R&D) commitment, time compression during production, and degree of outsourcing. The results are based on a sample from the world automobile industry.

Findings

The empirical results suggest that a strong R&D commitment and ability to compress production time explain 37 percent of the manufacturing performance differences among major volume automobile producers. A nonlinear convex relationship is also found between outsourcing rate and manufacturing performance. The results show how the resulting performance ratings can then be utilized to assess the effects of a selected group of practices on manufacturing performance.

Research limitations/implications

Since, there is a common basis for the sources of competitiveness among industries, the findings derived from this study are probably transportable to other industries. Also, the proposed metric and analytic approaches are generic and thus can be broadly applied to other industries. The results also suggest the need for further analysis of where improvements can be made within a given company, according to its firm‐specific characteristics.

Originality/value

Examines manufacturing performance along individual benchmarking dimensions and develops a performance metric based on quality and output volume to assess a firm's manufacturing competitiveness in relation to its major rivals.

Citation

Leachman, C., Pegels, C.C. and Kyoon Shin, S. (2005), "Manufacturing performance: evaluation and determinants", International Journal of Operations & Production Management, Vol. 25 No. 9, pp. 851-874. https://doi.org/10.1108/01443570510613938