An invitation control policy for proactive service systems: Balancing efficiency, value, and service level

Galit B. Yom-Tov, Liron Yedidsion, Yueming Xie

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Problem definition: We study the problem of designing a dynamic invitation policy for proactive service systems with finite customer patience under scarce capacity. In such systems, prior knowledge regarding customer value or importance is used to decide whether the company should offer service or not. Academic/practical relevance: Proactive service systems are becoming more popular, as data availability and machine-learning techniques are developed to forecast customer needs. However, very little is known about the efficient use of such tools to promote and manage service systems. Methodology: We use fluid approximation and the Filippov convex method to analyze system dynamics and develop approximations for important performance measures. Results: We show that whereas prioritizing customers in descending order of their r-μ ranking (as long as there are idle servers in the system) is optimal on the fluid level, refinements are necessary in the presence of abandonment on the stochastic level. We propose an r-μ-N policy to account for customer patience. Managerial implications: Our policy can be used to promote service effectivenes and allow decision makers the means to trade off service level against costs in such systems explicitly. Using a case study of a transportation service provider, we show that such a policy can triple revenue compared with random arrivals (nonproactive policy).

Original languageEnglish
Pages (from-to)1077
Number of pages1
JournalManufacturing and Service Operations Management
Volume23
Issue number5
DOIs
StatePublished - 1 Sep 2021
Externally publishedYes

Keywords

  • Admission control
  • Contact center management
  • Proactive service
  • Queueing theory
  • Service operations
  • Stochastic methods

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research

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