Making Simulation Work for the Organizational Design of Communication Centers: Challenges and Practical Experience

In this paper some important challenges for the design of communication centers are discussed. The practical value of discrete event simulation for this domain is shown, and a method for the systematic evaluation of organizational designs is presented. This approach has been tested in practice and the results of two cases will be documented in detail. 1. CHALLENGES AND PRACTICAL VALUE OF SIMULATION Many organizations deploy communication centers in order to establish effective and satisfactory interactions with actual and potential clients. In 1999 12.750 communication centers existed only in Europe and this figure is expected to rise up to 28.000 until 2006 [Frost and Sullivan 2000]. According to Cleveland, one goal of communication center management is to handle the workload of incoming customer requests with the desired service level on a highquality standard [Cleveland and Mayben 1997]. Qualified employees (agents) and a well-designed organizational structure are prerequisites to reach this goal. Some challenges for the design of communication centers arise from the dynamic environment, especially the unpredictable behavior of customers. This often manifests in varying volumes of incoming requests, varying uses of communication channels, varying processing times and varying abort rates. Therefore many stochastic effects have to be taken into consideration and stochastic discrete event simulation seems to be an obvious tool [Law and Kelton 1991]. Simulation can be used to evaluate the performance of specific organizational designs under given environmental factors and allows the selection of the best design for a given purpose [Zapf and Heinzl 2000]. Nevertheless there are critical success factors for putting simulation into practice. We identified the following two: • Getting appropriate input data and • meeting the given time restrictions. Especially for the design of new communication centers it is difficult to get “good” data because of the lack of historical data. Apart from that the modeling and simulation process have to be finished after a few days but the results must build a sound base also for the future organization. 2. MAKING SIMULATION WORK In order to handle the challenges for the organizational design of communication centers we suggest two strategies: • Evaluate many environmental situations in order to anticipate the effects of changing environments. • Automate the evaluation process with the help of a simulation control system to meet the time restrictions. The systematic evaluation of many environmental constellations can be performed in the following steps [Heinzl and Zapf 2000]: 1. Identify potential organizational designs, relevant performance measures, relevant influence factors and accompanying mean values and critical values. 2. Simulate a) all designs under normal conditions (mean values) and b) worst case scenarios for all designs (critical values). 3. Condense and visualize the results. For automating this process, the simulation control system SimControl has been built [Zapf 2001]. All relevant parameters and results can be stored in a simulation database and multiple simulation experiments can be automatically performed. The system has been implemented as Microsoft Access application and utilizes the ARENA program as simulation kernel [Kelton et al. 1998]. The presented evaluation procedure has been applied to different design problems in practice [Zapf 2001, Storch 2000]. In the next sections we will present the results of two case studies in detail. 3 THE BACK OFFICE CASE 3.1 Description The first case deals with different routing strategies for an enterprise of the water supply industry. The corresponding communication center consists of two parts, a front office and a back office. The organizational processes have been modeled in form of Petri-nets [Van der Aalst 2000] which are presented in Figure 1. Most of the incoming requests are standard calls which deal with questions according to invoices and reminders, modifications of customer data and requests for general information. Standard calls are normally handled by agents with general knowledge (generalists) in the front office. If customers have to wait more than 15 seconds for an employee in the front office, the call is automatically routed to the back office. The same procedure is also valid for the first routing part of special calls which deal with technical problems or difficult data modifications. The generalist who receives such a special call forwards it to an agent with specific knowledge (specialist) in the back office. Asynchronous requests are posted as letters, faxes or e-mails and are directly routed to generalists in the back office. In the back office case it has been examined if it is makes sense to route exceptional standard requests with long processing times from an agent in the front office to a generalist in the back office. This routing strategy obviously has two conflicting effects: 1. The talk times in the front office can be reduced and therefore more agents are available to accept calls. 2. There arises an additional effort from classifying and forwarding of exceptional standard requests. 3.2 Simulation Model and Data The basic structure of the simulation model A without routing of exceptional requests can be directly derived from the process model of Figure 1. Model B results from partitioning of standard calls into frequent and exceptional calls. Frequent calls are handled like a standard call in model A. Exceptional calls are accepted by generalists in the front office and are forwarded to generalists in the back office after a short classification time. Both models have been implemented with the ARENA simulation tool-set, which provides a graphical user interface for the generation of SIMAN programming code [Kelton et al. 1998]. The initial data situation for the different request types is presented in Table 1. The triangular distribution is used for modeling talk and after talk times since the exact distribution forms are not known, but estimates for the minimum, maximum and most likely values are available [Kelton et al. 1998, 512]. handle generalists standard call generalists specialists front office back off ice i f de lay >= 15 seconds