Interday forecasting and intraday updating of call center arrivals

• 成果类型: Article
• 署名作者: Shen, Haipeng; Huang, Jianhua Z.
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; Texas A&M University System; Texas A&M University College Station
• 刊物名称: M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1523-4614
• DOI: 10.1287/msom.1070.0179
• 发表日期: 2008
• 页码: 391-410
• 关键词: dimension reduction; dynamic forecast updating; Principal Component Analysis; penalized least squares; Singular value decomposition; vector time series

摘要

Accurate forecasting of call arrivals is critical for staffing and scheduling of a telephone call center. We develop methods for interday and dynamic intraday forecasting of incoming call volumes. Our approach is to treat the intraday call volume profiles as a high-dimensional vector time series. We propose first to reduce the dimensionality by singular value decomposition of the matrix of historical intraday profiles and then to apply time series and regression techniques. Our approach takes into account both interday ( or day-to-day) dynamics and intraday ( or within-day) patterns of call arrivals. Distributional forecasts are also developed. The proposed methods are data driven, appear to be robust against model assumptions in our simulation studies, and are shown to be very competitive in out-of-sample forecast comparisons using two real data sets. Our methods are computationally fast; it is therefore feasible to use them for real-time dynamic forecasting.