Design of experiments in the service industry: a critical literature review and future research directions

Abstract

Purpose

The purpose of the article is to present the results of a critical literature review (CLR) on Design of experiments (DoE) in the service industry.

Design/methodology/approach

A critical review of existing literature review across various databases including Scopus, Web of Science, Google Scholar and Emerald Insight were searched for the identification of relevant papers. The authors searched relevant journal articles for a time period of 25 years (1994–2019).

Findings

A total of 29 industry case studies of DoE applications were identified spanning healthcare, retail, logistics, education, marketing, after sales and catering business. The industrial experimentation strategies adopted by the case study organisations were screening, factorial designs, Taguchi, response surface method and split-plot. It was apparent that there are only a handful number of papers showing the applications of DoE across the service sector and this motivates for pursuing further research into this topic by the authors.

Practical implications

The findings of the study can be very useful for middle and senior managers to understand the benefits of implementing this powerful technique for increased understanding of service processes, as well as for optimising service performance. Moreover, the paper presents some of the fundamental challenges, as well as skills needed for the successful application of DoE.

Originality/value

To the best of our knowledge, this is the first CLR on DoE and its applications in the service sector. The findings of the study can be beneficial to both academic and industrial communities to understand some of the challenges and fundamental gaps which need to be tackled in the future.

Citation

Antony, J., Viles, E., Torres, A.F., Paula, T.I.d., Fernandes, M.M. and Cudney, E.A. (2020), "Design of experiments in the service industry: a critical literature review and future research directions", The TQM Journal, Vol. 32 No. 6, pp. 1159-1175. https://doi.org/10.1108/TQM-02-2020-0026