@article{ee8ed78c64244f96a4e634a53197cca7,
title = "Call Center Arrivals: When to Jointly Forecast Multiple Streams?",
abstract = "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.",
keywords = "arrival process, lag dependence, vector time series, workforce management",
author = "Han Ye and James Luedtke and Haipeng Shen",
note = "Funding Information: The authors thank the department editor, the senior editor, and the two referees for their thorough reviews, which substantially improved this study. The authors also gratefully thank the Service Enterprise Engineering Lab at the Technion for providing the data, and Noah Gans and Avi Mandel-baum for their valuable suggestions on the earlier versions of this study. The work of Luedtke was supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Applied Mathematics program under contract number DE-AC02-06CH11357. The work of Shen was partially supported by the US National Science Foundation grants DMS-1106912 and DMS-1407655, the Ministry of Science and Technology Major Project of China 2017YFC1310900 and 2017YFC1310903, University of Hong Kong Stanley Ho Alumni Challenge Fund, HKU University Research Committee Seed Funding Award 104004215, and the Xerox UAC Foundation. Funding Information: The authors thank the department editor, the senior editor, and the two referees for their thorough reviews, which substantially improved this study. The authors also gratefully thank the Service Enterprise Engineering Lab at the Technion for providing the data, and Noah Gans and Avi Mandelbaum for their valuable suggestions on the earlier versions of this study. The work of Luedtke was supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Applied Mathematics program under contract number DE-AC02-06CH11357. The work of Shen was partially supported by the US National Science Foundation grants DMS-1106912 and DMS-1407655, the Ministry of Science and Technology Major Project of China 2017YFC1310900 and 2017YFC1310903, University of Hong Kong Stanley Ho Alumni Challenge Fund, HKU University Research Committee Seed Funding Award 104004215, and the Xerox UAC Foundation. Publisher Copyright: {\textcopyright} 2018 Production and Operations Management Society",
year = "2019",
month = jan,
doi = "10.1111/poms.12888",
language = "English (US)",
volume = "28",
pages = "27--42",
journal = "Production and Operations Management",
issn = "1059-1478",
publisher = "Wiley-Blackwell",
number = "1",
}