A contingency theoretical perspective to the product‐process matrix

Abstract

Purpose

The product‐process matrix of Hayes and Wheelwright is widely known for its prescriptive managerial guidance. Yet, most empirical studies have found no support for its performance assertions or have even contradicted them. The purpose of this paper is to propose a contingency theoretical generalization and extension that accommodates both the performance implications of the original model and the best‐known departures from it.

Design/methodology/approach

The authors test their extended model with survey data from 151 manufacturing plants.

Findings

The authors' model extends the process dimension of the original model into a specificity dimension that depends not only on the layout of the process but also on the flexibilities that can be achieved with advanced manufacturing technologies. Similarly, the product dimension, which was operationalized as product variety in the original matrix, is generalized to the complexity of the production task, where product variety is only one element among others. Furthermore, the authors extend the model to accommodate also the dynamism of the task environment.

Research limitations/implications

In addition to the testing of the extended model, the study provides openings for further theoretical development. In particular, the findings demonstrate the value of the contingency theoretical concept of suboptimal equifinality to operations management research.

Practical implications

The study updates the product‐process matrix to match the modern industrial reality and thus enables the continued application of this important operations strategy prescription.

Originality/value

The study integrates insights from previous research in operations management and contingency theory into a generalization and extension of the product‐process matrix.

Citation

Helkiö, P. and Tenhiälä, A. (2013), "A contingency theoretical perspective to the product‐process matrix", International Journal of Operations & Production Management, Vol. 33 No. 2, pp. 216-244. https://doi.org/10.1108/01443571311295644