Waiting Experience in Open-Shop Service Networks: Improvements via Flow Analytics & Automation

47 Pages Posted: 24 Mar 2022 Last revised: 28 Oct 2022

See all articles by Manlu Chen

Manlu Chen

Renmin University of China - School of Business

Opher Baron

University of Toronto - Operations Management

Avishai Mandelbaum

Technion - Israel Institute of Technology

Jianfu Wang

City University of Hong Kong

Galit Yom-Tov

Technion-Israel Institute of Technology

Nadir Arber

affiliation not provided to SSRN

Date Written: January 27, 2022

Abstract

Waiting-for-service is a central, typically detrimental, factor in service experiences, and multiple delays will most likely amplify customers' poor impressions of a service. Yet multi-delay experiences are commonly assessed via macro measurements, e.g., overall waiting, as opposed to micro measurements that account for individual delays, e.g., maximal or most-recent delay. Furthermore, the COVID-19 pandemic has exacerbated the challenges of controlling micro measures -- physical distancing has turned short queues and waits into rigid constraints. Our paper, motivated by a health screening clinic, jointly considers macro and micro measurements.

To improve customers' waiting experience, the clinic implemented two information technologies: automated customer routing system (ACRS) and SMS-based customer paging system (SCPS). However, as our empirical study of the clinic revealed, these implementations had no significant impact for three main reasons. First, ACRS reduced system flexibility and thus caused unintended idleness of resources. Second, SCPS improved non-bottleneck activities. Third, the clinic automated its sub-optimal practice, namely station-level first-come-first-served policy, leading to long delays towards the end of a customer's route.

Nevertheless, these initiatives could facilitate enhanced operations and improve waiting experience. To this end, via a stylized queueing model and a data-driven simulation model of the clinic, we analyze delays in an open-shop service network. Our models reveal that a priority-based buffer strategy, which accounts for both system- and station-level characteristics, improves both macro- and micro-level measurements. In particular, when prioritizing according to shortest expected remaining processing time priority-base buffer policy, performance improves at both micro-level by 41.9% and macro-level by 14.9%.

Keywords: Service analytics, information technology, wait time management, open shop, priority policy

Suggested Citation

Chen, Manlu and Baron, Opher and Mandelbaum, Avishai and Wang, Jianfu and Yom-Tov, Galit and Arber, Nadir, Waiting Experience in Open-Shop Service Networks: Improvements via Flow Analytics & Automation (January 27, 2022). Available at SSRN: https://ssrn.com/abstract=4018744 or http://dx.doi.org/10.2139/ssrn.4018744

Manlu Chen (Contact Author)

Renmin University of China - School of Business ( email )

Beijing
China

Opher Baron

University of Toronto - Operations Management ( email )

105 St. George st
Toronto, ON M5S 3E6
Canada

Avishai Mandelbaum

Technion - Israel Institute of Technology ( email )

Israel

Jianfu Wang

City University of Hong Kong ( email )

Kowloon
Hong Kong
Hong Kong

Galit Yom-Tov

Technion-Israel Institute of Technology ( email )

Technion City
Haifa 32000, Haifa 32000
Israel

Nadir Arber

affiliation not provided to SSRN ( email )

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