Frontiers in Operations: Waiting Experience in Open-Shop Service Networks: Improvements via Flow Analytics and Automation

Manlu Chen (), Opher Baron (), Avishai Mandelbaum (), Jianfu Wang (), Galit B. Yom-Tov () and Nadir Arber ()
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Manlu Chen: School of Business, Renmin University of China, Beijing 100080, China
Opher Baron: Rotman School of Management, University of Toronto, Toronto, Ontario M5S 3E6, Canada
Avishai Mandelbaum: Faculty of Data and Decision Sciences, Technion—Israel Institute of Technology, Haifa 3200003, Israel
Jianfu Wang: College of Business, City University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong
Galit B. Yom-Tov: Faculty of Data and Decision Sciences, Technion—Israel Institute of Technology, Haifa 3200003, Israel
Nadir Arber: Integrated Cancer Prevention Center, Tel-Aviv Souraski Medical Center, Israel Tel Aviv University, Tel Aviv 6423906, Israel; Sackler School of Medicine, Tel Aviv University, Ramat Aviv 69978, Israel

Manufacturing & Service Operations Management, 2024, vol. 26, issue 4, 1211-1228

Abstract: Problem definition : We study open-shop service networks where customers go through multiple services. We were motivated by a partnering health screening clinic, where customers are routed by a dispatcher and operational performance is measured at two levels: micro-level, via waits for individual services, and macro-level, via overall wait. Both measures reflect customer experience and could support its management. Our analysis revealed that waits were long and increased along the service process. Such long waits give rise to negative waiting experience and the increasing shape is detrimental as it is known to create perceived waits that are even longer. Our goal is hence to analyze strategies that shape and improve customers’ perceived experience. Methodology/results : Analytically, we use a stylized two-station open-shop network to show that prioritizing advanced customers, jointly with pooling (virtual) queues, can improve both macro- and micro-level performance. We validate these findings with a simulation model, calibrated with our clinic’s data. Practically, we find that an automated routing system (ARS), recently implemented in the clinic, had a negligible impact on overall wait—It simply redistributed waiting among wait-for-routing and wait-for-service. However, ARS renders applicable sophisticated priority and routing policies (that were infeasible under the manual routing practice), specifically the ones arising from the present research. Managerial implications : Our study amplifies performance benefits of accounting for individual customers’ system status in addition to station-level load information. We offer insights into the implementation of new technologies: Firms better plan for fundamental changes in their operation rather than harness new technology to their existing operation, which may be suboptimal due to past technical limitations.

Keywords: service analytics; information technology; wait time management; open shop; priority policy (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:26:y:2024:i:4:p:1211-1228

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