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

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.,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).,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.,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.,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.

[1]  Robert W. Palmatier,et al.  Creating Effective Online Customer Experiences , 2018, Journal of Marketing.

[2]  Jiju Antony,et al.  Design of experiments in a higher education setting , 2014 .

[3]  Dhruv Grewal,et al.  Comparison of consumer reactions to price-matching guarantees in internet and bricks-and-mortar retail environments , 2007 .

[4]  Elizabeth A. Cudney,et al.  Understanding and evaluating teaching effectiveness in the UK higher education sector using experimental design , 2019, International Journal of Quality & Reliability Management.

[5]  Ander Errasti,et al.  Evaluating order picking performance trade-offs by configuring main operating strategies in a retail distributor: A Design of Experiments approach , 2013 .

[6]  Jiju Antony,et al.  Experimental design and computer‐based simulation: a case study with the Royal Navy , 1999 .

[7]  Maya Kaner,et al.  Engineering of service processes through designing simulation experiments , 2011 .

[8]  Arash Shahin,et al.  Service quality robust design – with a case study in airport services , 2012 .

[9]  Sangbok Ree,et al.  A study on education quality using the Taguchi method , 2014 .

[10]  Karen V. Fernandez Critically Reviewing Literature: A Tutorial for New Researchers , 2019, Australasian Marketing Journal.

[11]  F. L. Chen,et al.  Sales forecasting system based on Gray extreme learning machine with Taguchi method in retail industry , 2011, Expert Syst. Appl..

[12]  Thong Ngee Goh,et al.  The Role of Statistical Design of Experiments in Six Sigma: Perspectives of a Practitioner , 2002 .

[13]  Mahmoud El-Banna,et al.  Patient Discharge Time Improvement by Using the Six Sigma Approach: A Case Study , 2013 .

[14]  R. Torraco Writing Integrative Literature Reviews: Guidelines and Examples , 2005 .

[15]  Nusser A. Raajpoot,et al.  Application of Taguchi design to retail service , 2008 .

[16]  Martín Tanco,et al.  Practical applications of design of experiments in the field of engineering: a bibliographical review , 2008, Qual. Reliab. Eng. Int..

[17]  Arthur J. Swersey,et al.  Experimental design on the front lines of marketing: Testing new ideas to increase direct mail sales , 2006 .

[18]  Mahmoud A. Barghash,et al.  Six Sigma applied to reduce patients' waiting time in a cancer pharmacy , 2014 .

[19]  Jiju Antony,et al.  Statistical process control: an essential ingredient for improving service and manufacuring quality , 2000 .

[20]  Jaideep Motwani,et al.  An application of Taguchi’s robust experimental design technique to improve service performance , 1996 .

[21]  Jiju Antony,et al.  Ten useful and practical tips for making your industrial experiments successful , 1999 .

[22]  Prithbey Raj Dey,et al.  Taguchi S/N based optimization of machining parameters for surface roughness, tool wear and material removal rate in hard turning under MQL cutting condition , 2018, Measurement.

[23]  Qingjin Peng,et al.  Bottleneck detection for improvement of Emergency Department efficiency , 2015, Bus. Process. Manag. J..

[24]  Johannes Ledolter,et al.  Using a Fractional Factorial Design to Increase Direct Mail Response at Mother Jones Magazine , 2006 .

[25]  Thong Ngee Goh,et al.  Statistical techniques for quality , 1999 .

[26]  M. M. Kapadia,et al.  A methodology of enhancing profitability through the utilization of experimental design: a catering business case study , 1999 .

[27]  Pedro Paulo Balestrassi,et al.  Response surface methodology for advanced manufacturing technology optimization: theoretical fundamentals, practical guidelines, and survey literature review , 2019, The International Journal of Advanced Manufacturing Technology.

[28]  Sandy Tse,et al.  A hedonic model for effective web marketing: an empirical examination , 2005, Ind. Manag. Data Syst..

[29]  Murat Kulahci,et al.  Designing simulation experiments with controllable and uncontrollable factors for applications in healthcare , 2011 .

[30]  Pedro Paulo Balestrassi,et al.  Optimization of AISI 1045 end milling using robust parameter design , 2015 .