Intelligent Procedures for Intra-Day Updating of Call Center Agent Schedules

For nearly all call centers, agent schedules are typically created several days or weeks prior to the time that agents report to work. After schedules are created, call center resource managers receive additional information that can affect forecasted workload and resource availability. In particular, there is significant evidence, both among practitioners and in the research literature, suggesting that actual call arrival volumes early in a scheduling period (typically an individual day or week) can provide valuable information about the call arrival pattern later in the same scheduling period. In this paper, we develop a flexible and powerful heuristic framework for managers to make intra-day resource adjustment decisions that take into account updated call forecasts, updated agent requirements, existing agent schedules, agents’ schedule flexibility, and associated incremental labor costs. We demonstrate the value of this methodology in managing the trade-off between labor costs and service levels to best meet variable rates of demand for service, using data from an actual call center

IT-enabled Process Innovation: A Literature Review

The importance of Information Technology (IT) is growing, and in a hypercompetitive market IT must be used as a strategic asset for companies to succeed. In order to gain strategic benefits from IT, companies need to be innovative when deploying IT. This can be achieved by reengineering business processes to take advantage of the possibilities IT provides. In 1993 Thomas H. Davenport presented a framework describing the role of IT in process innovation . Based on this framework, the purpose of this paper is to conduct a literature review to answer the following research question: What kind of opportunities does IT provide for process innovation? . Davenport\u27s framework is used as an analytical lens to review articles from the top 20 IS and management journals. The paper provides an overview and an in-depth analysis of the literature on IT-enabled process innovation and suggests avenues for future research as well as recommendations for practitioners. Our analyses reveal five distinct themes related to opportunities for IT-enabled process innovation, all of which offer guidance to practitioners and highlight gaps in our current knowledge about how to leverage IT for innovation purposes

An Exact and Efficient Algorithm for the Constrained Dynamic Operator Staffing Problem for Call Centers

As a result of competition, call centers face increasing pressure to reduce costs while main-taining an acceptable level of customer service, which to a large extent entails reducing the time customers spend waiting for service. Pursuant to this, call center managers often face stochastic constrained optimization problems with the objective of minimizing cost, subject to certain cus-tomer waiting time constraints. Complicating this problem is the fact that in practice, customer arrival rates to call centers are often time-varying. In order to cost-effectively satisfy their service level goals in the face of this uncertainty, call centers may employ a certain number of permanent operators who always provide service, and a certain number of temporary operators who provide service only when the call center is busy, i.e. when the number of customers in system increases beyond a threshold level. This gives the call center manager the flexibility of dynamically adjusting the number of operators providing service (and thus the resources or costs dedicated) in response to the time-varying arrival rate. The Constrained Dynamic Operator Staffing (CDOS) problem involves determining the values for the number of permanent operators, the number of temporary operators, and the threshold value(s) that minimize time-average hiring and opportunity cost, subject to service level constraints. Currently, the only exact solution method for this problem is enumeration, which is often computationally intractable. We provide an exact and efficient solution method, the Modified-Balance-Equations-Disjunctive-Constraints (MBEDC) algorithm, for this problem resulting in a Mixed Integer Program formulation. Using our algorithm, we solve diverse in-stances of the CDOS problem, generating managerial insights regarding the effects of temporary operators and service level constraints