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

Transcription

1 UNIVERSITY OF BAYREUTH Lehrstuhl für Betriebswirtschaftslehre VII Wirtschaftsinformatik Making Simulation Work for the Organizational Design of Communication Centers: Challenges and Practical Experience Michael Zapf, Katja Storch Working Paper 6/2001 Working Papers in Information Systems Editor: Prof. Dr. Armin Heinzl University of Bayreuth Department of Information Systems (BWL VII) Universitaetsstrasse 30, D Bayreuth Phone , Fax Internet:

2 Making Simulation Work for the Organizational Design of Communication Centers: Challenges and Practical Experience Michael Zapf Research and Teaching Assistant University of Bayreuth/Germany Katja Storch MBA-Student University of Bayreuth/Germany Keywords: Organizational design, process design, communication center, case studies, process evaluation Abstract 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 communication centers existed only in Europe and this figure is expected to rise up to until 2006 [Frost and Sullivan 2000]. According to Cleveland, one goal of communication center management is to 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 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 front office generalists generalists specialists standard call special call asynchronous request Figure 1. Process Model for the Back Office Case 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. 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 d 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. 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 s it to an agent with specific knowledge (specialist) in the. Asynchronous requests are posted as letters, faxes or s and are directly routed to generalists in the. In the 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. 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 ing 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 d like a standard call in model A. Exceptional calls are accepted by generalists in the front office and are ed to generalists in the 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 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].

4 Request Type Volume calls per week Handle Time After Talk Time (1, 1.95, 3) (3, 4, 5) (2, 3, 5) (13, 16, 19)./. standard frequent 3450 (2.5, 3.6, 4.5) call exceptional 300 (3.5, 4.6, 5.5) total 3750 (3, 4.3, 6) special call 20 (2, 3.7, 5) asynchronous 5913 request (7, 8.88, 10) Table 1. Simulation Data for Different Request Types in the Back Office Case Additional simulation data concerning the number of agents, the waiting tolerance of customers and the time are summarized in Table 2. Parameter Value Dimension agents generalists 10 # agents front office generalists 18 # agents specialists 1 # agents waiting wait time until minute tolerance abandonment call back % (0.5, 1, 1.5) 70 % wait time until minute call back (1, 5, 10) time exceptional requests 20 % talk time special requests 20 % talk time Table 2. Additional Simulation Data in the Back Office Case 3.3 Experimental Design Both organizational designs have been evaluated under five different environmental situations. Starting from the normal situation, which reflects the initial data situation of section 3.2, different simulation parameters have been modified: The overload situation results from an increase of 50% of the average request volume. The volume of standard calls goes up to 5625 calls, for example. The situation of staff absence results in the absence of one front office generalist and three generalists. A lower waiting tolerance of (0.1, 0.5, 1) results in the low tolerance situation. In the long classification situation the classification time for exceptional calls is doubled to 40 percentage of the talk time. The single experiments have been undertaken in the form of multiple terminating simulation runs [Kelton et al. 1998]. The planning horizon was one week with different opening hours per day. In order to obtain expressive results, we launched 100 runs for every experiment, every run representing one week of operation. 3.4 Results The performance of the alternative process designs is analyzed according to two performance measures, the average lost call rate for standard calls and the average throughput time for asynchronous requests. We will concentrate on these measures, since they reflect the main results of the study. Additional measures which have been evaluated do not give a more detailed understanding of the designs Average Lost Call Rate for Standard Calls (%) normal overload staff absence Model A low tolerance Model B long classification Figure 2. Average Lost Call Rate for the Back Office Case Figure 2 shows the average lost call rate for standard calls in different environmental situations. In most situations the ing strategy for exceptional calls (Model B) improves the performance of the design and leads to fewer lost calls. Only in the overload situation both designs have similar results. The average throughput times for asynchronous requests are presented in Table 3. The relief of the front office in Model B leads to worse performance values of the back office. Especially in the overload and staff absence situation there is a large difference between the two process designs.

5 front office generalists generalists specialists standard call special call standard asyn. request special asyn. request Figure 3. Process Model for Aftersales Requests in the Networking Case Environmental Situation Model A Model B Difference (A-B) normal 11,95 12,38-0,43 overload 821,95 849,82-27,87 staff absence 30,75 81,23-50,48 low tolerance 11,73 12,69-0,96 long classification 11,89 12,44-0,55 Table 3. Average Throughput Time for Asynchronous Requests in Minutes for the Back Office Case In summary it may be said that the routing of exceptional requests from the front office to the leads to lower lost call rates in all environmental situations. But this advantage has to be paid with a higher load for the back office which is combined with longer throughput times for asynchronous requests, especially in overload situations and during the absence of generalists in the. 4 THE NETWORKING CASE 4.1 Description The second example is about networking communication centers of an enterprise in the energy industry. Two communication centers A and B divide the incoming customer requests into and requests, which can be posted as calls, letters, faxes or s. Both communication centers have the same internal process structure, which is shown in Figure 3 for requests. Standard and special calls are accepted either in the front office or - after a short delay of 15 seconds - in the. While standard calls are d by the same agent who has accepted the call, special calls are ed to a specialist with the required know-how. A similar procedure applies to asynchronous requests which are d completely in the. This process is also valid for requests with one exception: A standard call can be routed to an generalist in the front office if no generalists is available. Please note that there are different groups for and requests but the same specialist group for both request types. The question in the networking case is, whether it is more efficient to establish an overflow strategy which allows the routing of requests between location A and B in overload situations, or to separate both communication centers. In the tested overflow strategy every call is routed from front office A to front office B after a delay of 15 seconds and vice versa. If no free agent is found, the call is routed to back office A/B after an additional delay of 5 seconds.

6 4.2 Simulation Model and Data For analyzing the networking of two communication centers, two simulation models have been built: The first model represents the separation strategy with no call routing between location A and B. The second model implements an overflow strategy between both places as described in section 4.1. Both models have been implemented with ARENA the same way as in the case [Kelton et al. 1998]. The initial data situation for the different request types is presented in Table 4. Request Type Volume calls per week Handle Time After Talk Time (1, 2, 3) (1, 1.9, 3) (2, 3, 4) (2, 3, 4)./. calls standard 3015 (4, 4.9, 6) standard 5481 (2, 2.88, 4) special 62 (3, 4, 5) special 288 (3, 4, 5) asyn. standard 2098 request (6, 7, 8) standard 3933./. (6, 7, 8) special 110./. (9, 10, 11) special 205./. (9, 10, 11) Table 4. Simulation Data for Different Request Types in the Networking Case Additional simulation data are summarized in Table 5. If a standard call is d by an generalist the time is multiplied by 110%. The additional settling-in period for requests of a different location is mo d- eled by a time multiplier of 105%. 4.2 Experimental Design The networking strategies have been evaluated under four different environmental situations. Starting from the normal situation, which reflects the initial data situation of section 4.2, different simulation parameters have been modified: Overload: The overload situation results from an increase of 50% of the average request volume. The volume of standard calls goes up to 4523 calls, for example. Staff absence: The situation of staff absence results in the absence of one agent per team. Parameter Value Dimension agents generalists 3 # agents front office generalists 8 # agents front office generalists 5 # agents generalists 9 # agents specialists 2 # agents waiting wait time until minute tolerance abandonment call back % (2, 4.2, 6) 85 % wait time until call minute back (1, 5, 10) special calls 20 % talk time time special asyn. requests 10 % time Table 5. Additional Simulation Data in the Networking Case Long queueing: In the long queueing situation the time before routing to the next agent group is raised up to 1 minute for every queue. The single experiments have been undertaken in the form of multiple terminating simulation runs [Kelton et al. 1998]. The planning horizon was one week with different opening hours per day. In order to obtain expressive results, we launched 100 runs for every experiment, every run representing one week of operation Average Lost Call Rate for Standard Calls (% ) normal overload staff absence Presales Separated Presales Networked long queueing Aftersales Separated Aftersales Networked Figure 4. Average Lost Call Rate for the Networking Case

7 4.3 Results The process performance is analyzed as in the back office case according to the average lost call rate and the average throughput time for asynchronous requests. In the normal situation the lost call rates for and calls are equal (see Figure 4). However in the overload and staff absence situation, the lost call rate for requests is much higher than for requests while in the long queueing situation there are more lost calls than lost calls. This effects show the importance of analyzing more than one environmental situation in order to identify the strengths and weaknesses of different designs. Furthermore it can be clearly seen that for calls the networking leads to less lost calls than the separation strategy. Compared with this for calls the separation of communication centers produces the best results. Therefore a trade-off between and calls exists. Environmental Situation normal overload staff absence long queueing 8,68 19,30 11,80 8,76 separated 12,01 26,74 18,26 10,98 networked difference -3,33-7,44-6,46-2,22 8,42 26,69 14,30 7,81 separated 7,38 21,19 8,28 7,32 networked difference 1,04 5,50 6,02 0,49 Table 6. Average Throughput Time for Asynchronous Requests in Minutes for the Networking Case The advantage of the separation strategy for requests can also be identified for asynchronous requests (see Table 6). In all analyzed situations the average throughput time for requests is lower when the communication centers are completely separated. The opposite observation can be made for requests. Here the networking strategy leads to shorter throughput times. 5 CONCLUSION In this paper we presented some ideas to make simulation work for the design of communication centers. Specific challenges of this domain have been explained and the practical experience of two case studies has been documented in detail. ACKNOWLEDGEMENTS We acknowledge the extensive support of Marcus Grasemann from Prisma GmbH, Neu-Isenburg/Germany, during the realization of the presented case studies. REFERENCES [Frost and Sullivan 2000] Frost and Sullivan The European Web Based Call Center Market. Report Frost and Sullivan, Frankfurt. [Cleveland and Mayben 1997] Cleveland, B.; J. Mayben Call Center Management On Fast Forward. Annapolis, Maryland. [Heinzl and Zapf 2000] Heinzl, A.; Zapf, M Zur Reagibilität von Call Center-Prozessen. in: Das reagible Unternehmen, W. Dangelmaier; W. Felser, eds., Paderborn, [Kelton et al. 1998] Kelton, W.D.; R.P. Sadowski; D.A. Sadowski Simulation with ARENA. Boston et al. [Law and Kelton 1991] Law, A.M.; W.D. Kelton Simulation Modeling & Analysis. New York et al. [Storch 2000] Storch, K Simulationsgestützte Evaluation von Organisationsszenarien im Call Center. Diploma thesis, University of Bayreuth. [Van der Aalst 1998] Van der Aalst, W The Application of Petri Nets to Workflow Management. Journal of Circuits, Systems and Computers 8, No. 1, [Zapf 2001] Zapf, M Gestaltung flexibler Kundeninteraktionsprozesse im Communication Center: Theoretische Grundlagen und experimentelle Analyse. PhD dissertation, University of Bayreuth. [Zapf and Heinzl 2000] Zapf, M.; Heinzl, A Evaluation of Generic Process Design Patterns: An Experimental Study. in: Business Process Management, W. van der Aalst; J. Desel; A. Oberweis, eds., LNCS 1806, Springer, Berlin, BIOGRAPHY Michael Zapf is research and teaching assistant at the University of Bayreuth. He works in the field of business process design and has conducted many simulation studies in cooperation with different German companies. Communication centers build one focus of his work as researcher and consultant. Katja Storch is MBA-student and has collected extensive practical experience in the communication center field as employee of the Hewlett Packard Call Center in Amsterdam. She wrote her diploma thesis about the evaluation of communication centers in cooperation with Prisma GmbH, Neu-Isenburg, a German consulting company for the design of communication centers.