research@hec - Issue #21 - (Page IV)

Call Centers research Christian van Delft A Robust Method for Workforce Optimization BIOGRAPHY Call centers have become an integral part of many high-quality service. Christian van Delft and his co-authors have developed a method for staffing call workload. They show that giving personnel in these centers secondary tasks to centers that, for the first time, takes into account the uncertainty in overall daily perform between calls makes it possible to reduce operating costs while ensuring Christian van Delft holds a Bachelor’s of Engineering in Applied Mathematics from the Université catholique de Louvain (Belgium) and a doctorate in Operations Management from HEC Genève (Switzerland). He taught at HEC Genève, HEC Lausanne, the Ecole Polytechnique Fédérale de Lausanne, and the Ecole Centrale de Paris, before joining HEC in 1992. His work focuses on stochastic optimization, stock planning and management. companies, and the quality of their customer service is a potential competitive advantage and selling point. Indeed, van Delft and his coauthors explain, as early as 2002, call centers handled more than 70 percent of all business/customer interactions in the United States. While their emergence on a large scale has led to the investigation of numerous problems in operations management, none of the research thus far has taken into account one of the key factors of call center sizing and shift scheduling: the high degree of uncertainty concerning the number and duration of calls from day to day. This overlooked variable complicates workload evaluations and thus leads to staffing problems. Meanwhile, as van Delft points out, “ salaries make up 70 to 75 percent of a call center’s operating costs,” so inefficiencies in this area constitute a major concern. hec INTEGRATING AN INHERENT ASPECT OF CALL CENTERS: UNCERTAINTY “In theory, the method is supposed to be simple: managers use commercial software packages, act as though they could predict the future, and plan out days based on data that are assumed to be reliable. They then readjust to fluctuations in real time as best they can.” This method can be efficient — if there are few forecasting errors or if workload varies relatively little from one day to the next. But these conditions do not always hold true. “For example, in areas close to the sea, we would expect that when the weather gets nice, people will go to the beach, thus changing their consumption habits by putting off telephone purchases until later in the evening. On the other hand, when the weather is less pleasant, calls will tend to be spread evenly throughout the day instead,” explains the researcher, who sees in this issue a potential application for supply chain optimization in the context of random phenomena. “As we see it, you have to integrate forecasting errors directly into the methodology— because they are definitely going to happen—and handle adaptation costs from the outset.” A PROBLEM OF SIZE As explained above, the traditional approach, which presumes a relatively accurate prediction of consumer calling habits, cannot solve the planning problems associated with factors such as weather conditions. “It’s a little like if somebody told you to go to the moon or to Mars and find the deepest crater,” he continues. “There is no efficient strategy!” To deal with this problem, van Delft and his coauthors used robust optimization, a useful concept that was first presented several years ago. “The idea is to try and find a solution with the best possible performance, looking at the worst-case scenario given a set of uncertainties that are set in advance. The huge advantage of this approach IV research@hec • June-July 2011 http://www.hec.edu/Faculty-and-Research/Faculty/VAN-DELFT

Table of Contents for the Digital Edition of research@hec - Issue #21

Cover & Contents
Rethinking Business Models To Fight Poverty
Call Centers: A Robust Method for Workforce Optimization
Corporate Venture Capital: The Factors Behind Successful Investments
HEC PARIS Actuality

research@hec - Issue #21