Call Center Arrivals: When to Jointly Forecast Multiple Streams?

• 成果类型: Article
• 署名作者: Ye, Han; Luedtke, James; Shen, Haipeng
• 署名单位: University of Illinois System; University of Illinois Urbana-Champaign; University of Wisconsin System; University of Wisconsin Madison; University of Hong Kong
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.12888
• 发表日期: 2019
• 页码: 27-42
• 关键词: time-series; models

摘要

We consider call centers that have multiple (potentially inter-dependent) demand arrival streams. Workforce management of such labor intensive service systems starts with forecasting future arrival demand. We investigate the question of whether and when to jointly forecast future arrivals of the multiple streams. We first develop a general statistical model to simultaneously forecast multi-stream arrival rates. The model takes into account three types of inter-stream dependence. We then show with analytical and simulation studies how the forecasting benefits of the multi-stream forecasting model vary by the type, direction, and strength of inter-stream dependence. In particular, we find that it is beneficial to simultaneously forecast multi-stream arrivals (instead of separately forecasting each stream), when there exists inter-stream lag dependence among daily arrival rates. Empirical studies, using two real call center datasets further demonstrate our findings, and provide operational insights into how one chooses forecasting models for multi-stream arrivals.