Forecasting in Multi-skill Call Centers: A Multi-agent Multi-service (MAMS) Approach: Research in Progress

Workforce management is critical in call center business. Human resources are the highest cost, and therefore efficiency is a key success factor. On the other side relevant peaks of incoming calls have to be served. We here consider a complex case, with a many-to-many relationship between agents and services, i.e. the same agent serves many customers and the same customer may be served by many agents. In this perspective, we propose a model to forecast calls in long- and mid-term by ARIMA (Auto-Regressive Integrated Moving Average), and to size workforce in mid-term by integrating an Erlang model. Finally, we have developed a tool to forecast calls in a multi-agent multi-service call center. Field tests are running and first results validate our model.

[1]  James W. Taylor,et al.  A Comparison of Univariate Time Series Methods for Forecasting Intraday Arrivals at a Call Center , 2008, Manag. Sci..

[2]  Yili Hong,et al.  The Relationship Between Confidence Intervals for Failure Probabilities and Life Time Quantiles , 2008, IEEE Transactions on Reliability.

[3]  Layth C. Alwan,et al.  Time-Series Modeling for Statistical Process Control , 1988 .

[4]  Haipeng Shen,et al.  Interday Forecasting and Intraday Updating of Call Center Arrivals , 2008, Manuf. Serv. Oper. Manag..

[5]  R. Choudary Hanumara,et al.  Forecasting Incoming Calls to Telemarketing Centers , 1993 .

[6]  Antonella Longo,et al.  IT Service Level Management: Practices in Large Organizations , 2011 .

[7]  Tomonobu Senjyu,et al.  Combination of artificial neural network and ARIMA time series models for short term price forecasting in deregulated market , 2009, 2009 Transmission & Distribution Conference & Exposition: Asia and Pacific.

[8]  Oliver W. W. Yang,et al.  Wireless traffic modeling and prediction using seasonal ARIMA models , 2003, IEEE International Conference on Communications, 2003. ICC '03..

[9]  Nigel Meade,et al.  Forecasting call frequency at a financial services call centre , 2002, J. Oper. Res. Soc..

[10]  H.M.A. El Hag,et al.  An adjusted ARIMA model for internet traffic , 2007, AFRICON 2007.

[11]  Ying Guo,et al.  Air Pollution PM2.5 Data Analysis in Los Angeles Long Beach with Seasonal ARIMA Model , 2009, 2009 International Conference on Energy and Environment Technology.

[12]  Zeynep Akşin,et al.  The Modern Call Center: A Multi‐Disciplinary Perspective on Operations Management Research , 2007 .

[13]  Sandjai Bhulai,et al.  A Simple Staffing Method for Multiskill Call Centers , 2008, Manuf. Serv. Oper. Manag..

[14]  Jonathan Weinberg,et al.  Bayesian Forecasting of an Inhomogeneous Poisson Process With Applications to Call Center Data , 2007 .

[15]  P. McSharry,et al.  A comparison of univariate methods for forecasting electricity demand up to a day ahead , 2006 .

[16]  Bruce H. Andrews,et al.  L. L. Bean Improves Call-Center Forecasting , 1995 .

[17]  L. M. Anderson Statistics with Confidence. Confidence Intervals and Statistical Guidelines , 1989 .

[18]  Itay Gurvich,et al.  Overflow Networks: Approximations and Implications to Call Center Outsourcing , 2012, Oper. Res..

[19]  Zeynep Akşin,et al.  Queueing Models for Multiclass Call Centers with Real-Time Anticipated Delays , 2007 .

[20]  Avishai Mandelbaum,et al.  Telephone Call Centers: Tutorial, Review, and Research Prospects , 2003, Manuf. Serv. Oper. Manag..

[21]  T. Barroero,et al.  Right Sizing Customer Care: An Approach for Sustainable Service Level Agreements , 2011, 2011 International Joint Conference on Service Sciences.

[22]  Pierre L'Ecuyer,et al.  Staffing multi-skill call centers via search methods and a performance approximation , 2009 .

[23]  Pierre L'Ecuyer Modeling and Optimization Problems in Contact Centers , 2006, Third International Conference on the Quantitative Evaluation of Systems - (QEST'06).

[24]  Refik Soyer,et al.  Modeling and Analysis of Call Center Arrival Data: A Bayesian Approach , 2008, Manag. Sci..

[25]  M G Bissell,et al.  Time series modeling for quality control in clinical chemistry. , 1988, Clinical chemistry.

[26]  J. Lagarto,et al.  Price forecasting in the day-ahead Iberian electricity market using a conjectural variations ARIMA model , 2012, 2012 9th International Conference on the European Energy Market.

[27]  Shane G. Henderson,et al.  Call Center Staffing with Simulation and Cutting Plane Methods , 2004, Ann. Oper. Res..

[28]  Avishai Mandelbaum,et al.  The impact of customers’ patience on delay and abandonment: some empirically-driven experiments with the M/M/n + G queue , 2004, OR Spectr..

[29]  Avishai Mandelbaum,et al.  Queueing Models of Call Centers: An Introduction , 2002, Ann. Oper. Res..

[30]  Daesik Hur A Comparative Evaluation of Forecast Monitoring Systems in Service Organizations , 2002 .

[31]  Layth C. Alwan,et al.  The Problem of Misplaced Control Limits , 1995 .

[32]  Konstantinos Kalpakis,et al.  Distance measures for effective clustering of ARIMA time-series , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[33]  C. Kasemset,et al.  Effect of confidence interval on bottleneck identification via simulation , 2010, 2010 IEEE International Conference on Industrial Engineering and Engineering Management.

[34]  Ward Whitt,et al.  Staffing of Time-Varying Queues to Achieve Time-Stable Performance , 2008, Manag. Sci..

[35]  Avishai Mandelbaum,et al.  Service Engineering in Action: The Palm/Erlang-A Queue, with Applications to Call Centers , 2007 .

[36]  Douglas G. Altman,et al.  Statistics with confidence: Confidence intervals and statistical guidelines . , 1990 .

[37]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[38]  Patrick T. Harker,et al.  Computing performance measures in a multi-class multi-resource processor-shared loss system , 2000, Eur. J. Oper. Res..

[39]  Johann Christoph Strelen,et al.  The accuracy of a new confidence interval method , 2004, Proceedings of the 2004 Winter Simulation Conference, 2004..

[40]  Jonathan D. Cryer,et al.  Time Series Analysis , 1986 .

[41]  Sandjai Bhulai,et al.  A queueing model for call blending in call centers , 2003, IEEE Trans. Autom. Control..

[42]  G. Koole,et al.  An Overview of Routing and Staffing Algorithms in Multi-Skill Customer Contact Centers , 2006 .

[43]  Jeffrey E. Jarrett,et al.  Improving forecasting for telemarketing centers by ARIMA modeling with intervention , 1998 .

[44]  Bernard J. Morzuch,et al.  Evaluating Time-Series Models to Forecast the Demand for Tourism in Singapore , 2005 .

[45]  Diego J. Pedregal,et al.  An unobserved component model for multi-rate forecasting of telephone call demand: the design of a forecasting support system , 2002 .

[46]  Pierre L'Ecuyer,et al.  Staffing Multiskill Call Centers via Linear Programming and Simulation , 2008, Manag. Sci..

[47]  Stefan Helber,et al.  Performance analysis of an inbound call center with skills-based routing , 2004, OR Spectr..