OPERA

Virtually all businesses are interested in providing information and assistance to existing and prospective customers. In recent years, the decreased costs of telecommunications and information technology have made it increasingly economical to consolidate such information delivery functions, which led to the emergence of groups that specialize in handling customer phone calls. For the vast majority of these groups, their primary function is to receive telephone calls that have been initiated by customers. Such operations, known as “inbound” call centers, are the topic of this proposal. Inbound call centers may be physically housed across several different locations, time zones, or countries. Given the size of the call center industry and the complexity associated with its operations, call centers have emerged as a fertile ground for academic research. Aksin et al. (2007) provide a survey of the academic literature associated with traditional call center problems areas. They point out also the lack of research on call center operations management integrating psychological and behavioral aspects, which is by far more relevant and useful in practice. According to marketing theory for example, customer satisfaction toward the interaction will depend on the capacity of firms in identifying and understanding the related attitudinal process during the call. The objective of this project is to contribute to the literature related to these key emerging phenomena. It is included under a multi-disciplinary perspective on operations management research. In this project, we will consider various problems related to the three decision levels of the operations management of call centers; long term, mid-term and operational term. The first problem, related to strategic decisions, is to develop and analyze flexible architectures of multi-skill call centers. The second problem, related to mid-term decisions, is to optimize personnel staffing. The last problem, related to operational decisions, is to develop and analyze call center models with delay information. Our approach consists on developing and analyzing queueing models that incorporate various customer and agent behavior features. This new multi-disciplinary perspective would enhance our understanding of such complex systems, and allows to gain useful guidelines for the practitioners. To the contrary to traditional works where usually a pure queueing analysis is performed, we will account here for the impact of the human element on the call center performance. For the design problems, we will focus on the impact of the human resource management of the agents on performance. We do so by accounting for the agent responsibility and motivation as a function of the design of the call center teams. As for the delay announcement problem, we will study the customer reaction to delay information. We will model how customers would react in terms of balking, abandonment, dissatisfaction because of waiting uncertainty, etc. These novel developed models will be quantitatively analyzed using mainly stochastic processes methods. Various managerial insights will be derived thereafter.