AN EXCEL ADD-IN FOR CALL CENTERS SIMULATION

Transcription

1 27 Congreso Nacional de Estadística e Investigación Operativa Lleida, 8-11 de abril de 2003 SIMUC@LL: AN EXCEL ADD-IN FOR CALL CENTERS SIMULATION Javier Faulín 1, Ángel A. Juan 2, Rafael García Martín 3. 1 Department of Statistics and Operations Research Public University of Navarra. SPAIN javier.faulin@unavarra.es 2 Economic and Business Studies Universitat Oberta de Catalunya. SPAIN ajuanp@uoc.edu 3 Department of Statistics and Econometrics University Charles III of Madrid. SPAIN rgnewman@est-econ.uc3m.es ABSTRACT Our paper uses simulation to design the appropriate parameters of the queues associated to a call centre. This simulation is performed having the help of an add-in for Microsoft Excel which has been especially built for this kind of systems of service. The use of Excel add-ins is suitable for this problem because of the easiness of their implementation and the good quality of the solutions obtained. This add-in contains the main descriptors of a queue, highlighting the constraint of maximum length of the queue, because this restriction is crucial in the management of call centres. On the other hand, the use of a spreadsheets is significant, because it permits the development of other essential calculations. After the simulation of the behaviour of these centres, some optimal decisions have been taken related to number of servers, quality of service, policy of service, priorities design and so on. Finally, the situations of several scenarios are compared in order to discuss the efficiency of the decisions designed by the simulation process. The process is repeated until the performance of the system was satisfactory. Key Words: Simulation, Spreadsheets, Add-ins, Call Centres, Queues. AMS Classification: 65C05 90B22 1

2 1. Introduction It is said that more than 70% of business transactions take place over the telephone. Therefore, the presence of a call center in the economic life of our society is indispensable for making business. Likewise, the appropriate design and management of this kind of service centers is essential. Modeling a call center is a challenging task, but it is also a very topical issue. There are plenty of companies and banks that show their corporate image by means of a call center. The virtual relationship between companies and customers is sometimes softened by the friendly voice heard in a phone. Therefore, call center management is a very specific mission which is indispensable for the appropriate development of the company that supports the center. But, what is a call center? Mehrotra (1997) gave the following definition: Any group whose principal business is talking on the telephone to customers or prospects. This group could be situated in a single place, in a multiple position or distributed with agents in individual offices. Usually, agents in a call center share a common set of computers and resources. Other definition is found in the web page designed by the Call Center News Service ( Now, the definition is outlined as follows: A call center is traditionally defined as a physical location where calls are placed, or received, in high volume for the purpose of sales, marketing, customer service, telemarketing, technical support or other specialized business activity. Nevertheless, sometimes a call center is described as a place of doing business by phone that combined a centralized database with an automatic call distribution system. But it is necessary to take into account other roles of this kind of centers, for instance: a) huge telemarketing centers, b) help desks, both internal and external, c) outsourcers (better known as service bureaus) that use their large capacity to serve lots of companies, d) reservation centers for airlines and hotels, e) fundraising and collections organizations. Then the traditional role depicted in the early call centers of the seventies has substantially changed. Nowadays, call centers are generally set up as large rooms, with workstations that include a computer, a telephone set (or headset) hooked into a large telecom switch and one or more supervisor stations. It may stand by itself, or be linked with other centers. It may also be linked to a corporate data network, including mainframes, microcomputers and LANs. Increasingly, the voice and data pathways into the center are linked through a set of new technologies called CTI, or computertelephony integration. Typical companies that have implemented a call center to administer their customer relationship management are: airline reservation centers, catalog ordering companies, consumer-oriented problem solvers, or software customer support services. Until the early 1990s, only the largest centers could afford the investment in technology that allowed them to handle huge volumes. More recently, with the development of LANbased switches, internet-based transaction processing, client/server software systems, and open phone systems, any call center can have an advanced call handling and customer management system, even down to ten agents or less. Worldwide speaking, the call center business is huge, omnipresent and growing fast. The first problem to face in call center management is its size. Managers and 2

3 administrators want more and more enormous centers in order to reach decreasing returns to scale in the costs. Nevertheless, the bigger is the call center, the more difficult is its management. Similarly, gigantic centers need optimization more intensively than small ones. On the other hand, the number of call centers in all over the world is quickly increasing, because they are completely immersed in the global economy. The rate of growth in the number of call centers is slowing, from 4% in 1999 to an estimated 0.8% in 2003, attributed to both maturation and consolidation within the call center industry. 2. Some Statistics about Call Centers We are going to highlight the importance of call centers in the current global economy, considering some statistical data that are wisely collected in the Call Center News Service web page. At a rough estimate, the number of call centers in North America ranges from 50,000 to as high as 200,000. The reality is probably somewhere around 100,000, depending on the definition of a call center according to its size. It is thought that the number of call centers in Europe will grow from 12,750 in 1999 to 28,289 in Europe's call center market is around $9 billion. Great Britain, France, Germany and Holland together accounted for 80% of call center sales revenues within the 15- member European Union EU. During the five-year period from 1999 through 2003, sales of call center systems among the aforementioned countries will total more than 1.8 million seats, over $3.6 billion in base revenues, and over $9 billion in gross revenues. On the other hand, the average cost of handling a call in a telephony-based center ranges from US$50-74, and the average cost falls 43% in a web-based call center. 3. Describing and Managing a Call Center The experience of founding and running a call center has been accumulated for years since the early seventies when the first call centers were established. This know-how has been compiled in the Bodin and Dawson s (2002) book and in the Dawson s (2001) reference. The first descriptive feature of a call center is the flow direction of the customers calls, or who takes the initiative in the calling process. This feature classifies the call centers in inbound and outbound centers. Literally, an inbound center is one that handles calls coming in from outside, most often through toll free numbers. These calls are primarily service and support calls, and inbound sales. An outbound center is one that does mainly outgoing telemarketing. Inbound is the biggest component of call center traffic these days, though perversely, outbound represents the area of largest projected growth in the next few years. In truth, the majority of centers contain some element of both inbound and outbound. The appropriate monitoring of a call center is performed by the manager. Her/his role consists of ensuring the continued daily operation of the center, i.e. to set service standards for the center (how many calls have been answered, on average, or how many contacts per hour outbound agents are required to make). The manager is responsible for solving daily problems, arranging for having the optimum number of agents in the right number of seats, and for making sure that the center operates adequately, 3

4 technologically speaking. Other essential responsibility of the manager is the improvement of service quality for customers: good attention to the requirements of clients, establishment of durable links with clients Customer relationship management is the main task of manager. Nevertheless, she/he must be ready to face any kind of plights or predicaments in the call center. Call centers are also becoming increasingly complicated to run. This peculiarity has a significant impact on the optimization of customer management. When the first kind of call centers were established in the early seventies the call management was performed using a very simple queueing mathematical model: a standard line on a FIFO basis, because the customer demanded a standard product for a standard use in a standard price. This situation was based on the uniformity of customers demand. Notwithstanding, nowadays the scenario is much more complex: the customer, from the point of view of the company, is different depending on her/his preferences, purchase history, advertising reference and the bundle of products that she/he has obtained. Lastly, the call centers profile a close relationship with clients, being the carriers of the footprint of quality for most customers. As a result, sales and service in a call center are usually connected in a decisive way, forming a strategic advantage. As a final point, we highlight that the description of the term call center is continuously changing. Now, it is possible to find call centers that answer s, engage in live internet chat sessions with customers and sometimes even transmit live video. Therefore experts are looking for a broader definition of what a call center is. 4. Using Spreadsheets for Simulating a Call Center. The use of spreadsheets in OR/MS and, particularly, in Simulation is well known into the managerial community. They have been successfully tested in diverse scenarios. The Gass, Hirshfeld and Wasil s (2000) and Grossman s (1999) references are outstanding examples of the excellent qualities of spreadsheets in the decision-making arena. Some other authors have even developed efficient Excel/VBA based programs for simulating queueing models (Albright (2001), Gross et al (1998)). This paper follows that direction, and shows how it is possible to use the analytical and graphic capabilities of Excel to construct an VBA program, SimuC@ll, that gives detailed information about a call center behavior. In the optimization field, by means of simulation for call centers, we highlight the Brigandi, Dargon, Sheehan and Spencer s (1994) work that developed a call processing simulator for inbound call centers at AT&T. Other current application to the management of this type of centers for driving a strategic change is explained in the Saltzman and Mehrotra s (2001) paper. The article written by Aksin and Harker (2001) shows some ideas about the specific and complete organization of call centers using Queueing Theory in a more theoretical ambit. On the other hand, the van Dijk s (2000) reference develops other ideas about the use of simulation in the queueing arena that deserve a detailed analysis for the appropriate performance of call center simulation. The outlook of these articles is 4

5 essential for the comprehension of the appropriate development of telephone call centers. Finally, it is necessary to pinpoint the goal of using simulation in the description of call centers. And so, it is possible to begin in earnest the debate about call centers: cost control versus service improvement. We delve into the core of a service system because we undoubtedly want to optimize the performance of some of its outputs, albeit we do not know which one. Somehow, the dilemma previously depicted is classical in any service system. Nevertheless, SimuC@ll do not presuppose the goals or aims of the decision maker. It is a queuing simulator adapted to a call centers scenario whereupon the manager of the system could change her/his goals in different situations according to the wherewithal of the center. We chose simulation for designing the structure of call centers, because it was the easiest way to monitor the changing scenarios of these service centers. Otherwise, it could have been very difficult to model waiting centers using exact mathematical representations. The choice of a spreadsheet as the basis for developing the call center simulator was due to its flexibility and its spreading in the managerial community. On the other hand, the employ of add-ins in management is very popular and some add-ins could be rounded off each other for managing a better control of the call center. Simulation add-ins can be found everywhere. We can remember among Crystal Ball, Exotic Options Simulations, and Insight (Thiriez (2001)). Our purpose was to customize a simulator for the specific use in a call center. It is clear that the previous add-ins could be employed for simulating any service system, call centers included. 5. Introducing SimuC@ll We have chosen simulation for designing the structure of call centers, because of the great variability of conditions that guide its daily dynamics. It is extremely difficult to implement other mathematical models to describing the clients flow with a computer tool. Thus, we have designed an Excel add-in in VBA code for the specific simulation of queues in call centers. But, what is the reason for designing particular simulators for call centers? What does it make different a call center from other queueing structures? Phone line does. The queue dynamics is different in a call center than in a physical line, as, for instance, a set of checkout points in a superstore. The queue management in a call center is much lither than in a traditional line: it is possible to change the queue discipline, to distinguish among different kinds of customers, to control the number of customers in line, etc. without the clients in the holding line realize that something has been altered. To wit, it is different to organize the call center queue than a standard line. On the other hand, the dynamics of clients is also diverse: it is important to gauge the proportion of clients that balk the line or renege in the queue. We will pay a special attention to those situations of balking and reneging, because they represent an excellent measure of the service quality that the call center gives to the customers. The blueprint of a simulator, having the call center optimization as the main goal, is essential for the correct development of this kind of queues. 5

6 Let us begin explaining how works. is a single-phase multi-server queueing simulator developed by the authors in order to find appropriate values for the parameters of a call center; in other words, SimuC@ll is a parameter tuner for queues in a call center. SimuC@ll has been coded in Visual Basic keeping in mind its implementation in Excel 2000 or 2002 as an add-in. Once the code is working in Excel, we can see nine worksheets, which we can comment by order of appearance as follows: 1) Introduction sheet with the presentation of the potential probability distributions that will model each line: gamma, normal, lognormal, Weibull and exponential distributions, 2) Inputs sheet (see Figure 1), in which the user should provide a suitable value for each parameter in the line to model, 3) Simulation sheet, which contains the simulation outcomes, 4) Final report sheet, showing the main upshots of the simulation with several estimations of the line descriptors (that help to build new scenarios for the call center), 5) Individual times sheet, which depicts a list of the times consumed by each customer in the queue, in the system and being served, 6) NumInQ sheet, which portrays a bar chart for comparing the time percentages of occupancy of the queue, 7) Waiting time sheet, which shows a bar chart of the waiting time percentages depicted by quartiles, 8) Server utilization sheet, which illustrates a pie chart for contrasting the time that a specific server is busy or idle, and 9) Average numbers sheet giving a bar chart of the balance between arrivals, rejections, served customers, etc. It is easy to understand that the following task -after the introduction of several values in the inputs cells- is to adjust the parameters in order to reach a good performance of the simulator. This is the most artistic undertaking for the call center modeler. Now, we are going to describe the general procedure to run the SimuC@ll simulator. As mentioned above, the Inputs worksheet allows the submission of the ruling parameters for the call center queues. First of all, some initial parameters are needed: time units and the inter-arrival times distribution. For the inter-arrival times, SimuC@ll assumes that they can be split up into four intervals whose range can be modeled by the user. The range and the parameters can be different for each interval. Nevertheless the type of probability distribution must be the same for those intervals. In other cells, the service time distribution, the abandonment time distribution, the number of servers and the maximum number of clients in queue must be provided. The cells which need these inputs are colored in green. In some cells, it is necessary to click in them in order to choose between several options. In others, a click shows a short explanation about the parameter to submit. The simulation can be controlled by the user, bounding the simulation time and the iterations number to run in the last cells of the Input worksheet. Once all the parameters and distributions have been specified, we will begin the simulation after clicking the starting button. The Simulation Outputs worksheet writes, iteration after iteration, the complete values for the simulation process: arrivals, abandonment estimation, clients rejected, clients served, etc., adding up total quantities for each defined variable. Similarly, the average and maximum values are also computed. The Final report worksheet shows the main estimations about the line descriptions, as it has been depicted in Figure 2. The remaining five worksheets are devoted to drawing -in convenient graphssummarized data. This will facilitate the comprehension of the simulation upshots to the end user (generally speaking, the call center manager). 6

7 Figure 1: General Outlook of the Inputs Worksheet Figure 2: Final Report worksheet for 7

8 6. Use and Necessity of We are going to justify the necessity s development and use as an interesting alternative to the use of some well-known simulators for Excel, such Crystal Ball, or Insight. It is clear that a specific simulator can describe a queueing system better than a general purpose one. But this explanation it is not the raison d être for the construction of this new add-in. We can summarize the motives for the necessity of a simulator focused on call centers, in the following points: a) Nowadays, call centers are increasing in number and in popularity according to the data showed in the second and third sections. Their adequate description is essential in the communication system of many companies. b) The queues constituted in call centers are specific of this kind of services. A general simulator could not represent the call center dynamics in an appropriate way. A quick description of the call center customers could be made using a tool like SimuC@ll. c) A specific simulator would make easier the report writing in order to take hard decisions or to contrast information inside the call center. The add-in structure inside Excel, involves the broad use of SimuC@ll in the managerial world, because of the current popularity of spreadsheets. On the other hand, the add-in could be transformed in an adequate DSS for some call centers. 7. Describing the UOC Call Center The Universitat Oberta de Catalunya (UOC ( is a completely virtual university having its head offices in Barcelona (Spain) and teaching over Internet in Spain and Latin-America. It is one of the pioneering universities in the e-learning world. It was born in 1995 with 200 students and it has registered more than 20,000 students in The UOC has integrated the information and communication technologies tools in the syllabus for each university subject. Taking into account the distance teaching of this university based on the Internet network, it is evident that students, users and people in general need a direct contact to the UOC. This contact takes the form of several inbound call centers. For this paper, we have selected one of the several call centers that the UOC offers to its students. From now on, we will call the selected call center as UOC Call Center or simply UOC CC. This particular inbound call center has been working in a proper way for several semesters. It receives phone calls in a schedule ranging from Monday to Friday between 8:45 am and 7:45 pm (11 hours per day or 55 hours per week). According to the general description of a call center, the UOC CC is an inbound communication system having a standard line on a FIFO discipline. The purpose of the people who call to the UOC CC is to obtain information about a wide range of issues related to real practice in learning and teaching over the Internet at UOC. Commercially speaking, the UOC CC is the welcoming voice that helps to solve problems or that explains the inner operations of this on-line university. Therefore, the appropriate working of the call center is essential for the corporate image of the UOC. 8

9 The UOC CC fulfills a set of characteristics which make it interesting for its evaluation using SimuC@ll: we have a complete database of observations about its daily working (number of inbound calls, number of answered calls, number of reneged calls, ); the number of arrival calls in this service center is quite stable; servers are well trained and experienced in appropriate manners for dealing with students; and finally, it is a small call center, whereupon its management is not extraordinarily difficult. 8. Applying SimuC@ll in the UOC Call Center: Defining the Model Our final intention is to analyze the selected call center system using the discrete event simulation algorithms integrated in SimuC@ll. Nevertheless, before performing the analysis, we need to model the service system in a proper way. To begin with, we should pinpoint the main goals targeted at this service center. These goals could be described as follows, according to their level of relevance: a) to offer an outstanding service to the student or potential student, b) to improve UOC corporate image, and c) to optimize service costs. Therefore, taking into account these goals, we are not specially interested in estimating total service costs (we will leave that problem to the managers): as far as we concern, we will consider worthy to use one or two extra agents if this policy results in a significant improvement on service quality (this idea is also enforced by the fact that when an agent is not attending a call, he or she can either still answering student s or helping with the Frequent Asked Questions database). Efficiency, quality and promptness in service are the fundamental tenets in this call center management. Once we know both the call center properties and our goals, we can start to build up a mathematical model for this system. Our starting point is the system database for the trimester January-March 2002 (Faulin and Juan (2002)). This database contains information about the call center dynamics (arrival time for each call, service times, abandonment times, etc.) Since it is not easy to obtain all these data for every day in the former trimester (basically due to technical reasons), we have randomly selected a sample of ten working days for each of the three months. The sampled data have been stored in an Excel file for further manipulation. We can describe the following phases for the modeling process: Phase 1: Stable Arrival Behavior After we have gathered the arrival times from the database, a natural idea is to check the hypothesis that the average number of calls arriving to the system is approximately constant for the three months (i.e.: we want to know if it has sense to assume that the number of arrivals remains stable enough from January to March). In order to check that hypothesis, we have used the statistical package Minitab to carry out the associated ANOVA test, as it can be seen in Figure 3. The ANOVA test points out a value of 0.63 for the F statistic, with an associated p- value of Therefore, it seems reasonable to assume a stable behavior in the 9

10 monthly average number of calls arriving at the center. Before accepting this conclusion as a valid one, we have check also for the assumptions of ANOVA: 1) Normality assumption: An Anderson-Darling normality test (Stephens (1974)) on the residuals has result in a p-value of (which validates our assumption). 2) Constant variance assumption: this assumption has been check using a scatterplot of residuals vs. fitted values. 3) Independence assumption: we have used a run chart to verify this assumption on the independence of the observations. Figure 3: ANOVA test for mean number of arrivals Phase 2: Daily Arrival Rate We are also interested in finding out if the arrival rate keeps constant all day long or if it takes significantly different values depending on the hour of the day. Since we know arrival times for each day, we can make use of Excel to calculate the daily number of arrivals per fifteen minutes period from 8:45 a.m. to 19:45 p.m. After that, we can calculate averages of these numbers (by period) and create the histogram of the Figure 4. The geometrical structure of the arrival rate in Figure 4 suggests that this rate varies upon the hour of the day. Fortunately, SimuC@ll dreams up the possibility of modeling the call inputs in different time intervals. As suggested in the histogram, it is possible to split the arrival range up into four intervals whose borders can be profiled by the modeler. According to the number of calls depicted in Figure 4, we can define the following time intervals or time strips for the arrivals to the call center: a) Strip 1: 08:45-10:00, b) Strip 2: 10:00-14:00, c) Strip 3: 14:00-16:15, and d) Strip 4: 16:15-19:45. Therefore, we seize on the SimuC@ll structure to find out the most appropriate probability distribution for each time strip. 10

11 Figure 4: Arrival Average Distribution Phase 3: Fitting Inter-Arrival Times Knowing the times for customer calls, the inter-arrival times can easily be calculated in an Excel worksheet as it appears in Figure 5 (each inter-arrival time can be calculated as the difference between two consecutive arrival times). Afterwards, we will have to fit inter-arrival times by a theoretical probability distribution. Again, we have used Minitab to fit the data to an appropriate probability distribution in several scenarios. We have chosen the Anderson-Darling test (Stephens (1974)) for verifying the origin population of the data. We have tried to fit the data with all of the following theoretical distributions: gamma, Weibull, exponential, normal and lognormal. First, we have considered the data as a whole for each day; afterwards, we have split the days up into the already explained four time strips. After an intensive mathematical analysis, we conclude that the best distribution for fitting the inter-arrival data is a Weibull distribution having different parameters in each time strip. This information can be summarized in Table 1. Exponential distribution could be also used for fitting these data because the Anderson-Darling test reaps good outcomes for that distribution; notwithstanding the Weibull distribution is the best option according to the same test. 11

12 Figure 5: Computing Inter-Arrival Times Weibull distributions Inter-arrival times as a whole First Strip (08:45-10:00) Second Strip (10:00-14:00) Third Strip (14:00-16:15) Fourth Strip (16:15-19:45) Shape parameter (α) Scale parameter (θ) Mean Table 1: Parameter Estimation for the Inter-Arrival Times of customers in the UOC CC Phase 4: Fitting Service Times and Abandonment Times This task is the other side of the same coin in the simulation process. We need a probabilistic representation for the service tasks and another for the abandoning process (in a call center system, we can consider a balking customer as a reneging one with a time of abandonment close to zero). In this case, the UOC CC managers gave us a sample of 230 observations for service times in the period January-March 2002, and another sample of 115 observations for abandonment times in the same period. Using these two samples and the Minitab package we have been able to infer the following assertion: in this case, service times can be modeled by an exponential distribution while abandonment times need a Weibull distribution. This conclusion has been reached according to the values of the Anderson-Darling test. In both cases, neither the distribution nor the parameters 12

13 depend on the time strips which have been defined for the arrival times. The convenient values for the parameters of the service times distribution and the abandonment times distribution are depicted in Table 2. Weibull distributions Service Time Abandonment Time (Exponential) Shape parameter (α) Scale parameter (θ) Mean Table 2: Parameter Estimation for the Service Times and Abandonment Times This parameter tuning has been revealed as essential to the description of the dynamics of the call center. Once we have defined all the inputs parameters of this system, we can start the discrete event simulation. 9. Applying SimuC@ll in the UOC Call Center: Taking Decisions The model for the UOC CC has already tuned in SimuC@ll. The main concerns of the system managers are associated to find the optimum workforce size. This is a typical analysis in queueing optimization. Therefore, we have defined several scenarios to analyze. These scenarios varied in the number of servers hired by the UOC CC. The call center managers estimated that if some of these servers are idle (they are not serving clients), they could carry out other similar tasks like answering s, paperwork management, helping with the FAQ s database, etc. These extra tasks would help to compensate an oversized workforce for just answer the phone. At this point, it is convenient to keep in mind that the treatment to clients in the call center must be exquisite, according to the UOC policy, meaning that the number of lost calls (balking and reneging clients) should not be greater than 5% of the total calls. As introduced above, the potential scenarios to consider in the UOC CC will be related to the number of servers to assign. Based on management criteria, it is clear that a minimum number of servers would be two agents. Only one server could not attend properly the clients. Let us consider scenarios varying the number of servers in the range with bounds 2 and 5. More servers would not reach much more efficiency in the call center management and the associate cost would be excessive. At this manner, if we perform the simulation in the previous scenarios, we will obtain the upshots depicted in Table 3. At this manner, we have performed -using SimuC@ll- the simulation in the previous scenarios. For each scenario, we have obtained numeric and graphic reports. Figure 2 shows one of this numeric reports (the scenario associated with n = 5 servers). Taking into account these simulation outcomes, and the policy of less than 5% lost calls, we recommended a workforce size of five servers for the UOC CC. Our advice was based on the following ideas: a) the lost client percentage is very high in the scenarios 1 and 2, b) the idle time percentage would be too big in the scenarios having 13

14 more than five servers, and c) the maximum time in queue is below four minutes. Therefore, these reasons involve an optimal decision with five servers. Figure 6 shows two of the relevant variables for each scenario. Scenario 1 Scenario 2 Scenario 3 Scenario 4 Number of servers Maximum time in queue for any customer (minutes) Average time in queue for any customer (seconds) Average number of customers served Average number of lost clients 38.3% 19.4% 9.0% 2.9% Idle time per server 40.5% 48.3% 55.7% 62.7% Table 3: Upshots per scenario in the UOC CC simulation. When we discussed this resolution with the UOC CC managers and workers, they agree in that our simulation output was quite coherent with their own experience (which helped to validate our model). Furthermore, they realized that the policy we proposed was very similar to the one currently implemented in the call center. Figure 6: Comparing scenarios 14

15 Nevertheless, the complete three month analysis of the UOC CC helped the managers to assign specific tasks to the servers when they are idle. The use of our recommendation involved a one server increment in the number of servers in the call center, along with new ideas about the way of training servers for the call center. 10. Final Considerations and Conclusions We chose simulation for designing the structure of call centers because of the intrinsic dynamics of this kind of centers. In this case, SimuC@ll helped to take the right decisions in the workforce design arena. Perhaps, the main virtue of this add-in is its adaptability to any new situation in queueing problems in call centers. Concerning the UOC CC, we have been able to model the waiting line of this system, supplying valuable advises about the workforce size. Other kind of advises could be supplied, as for example: maximum length of queue permitted, design of workforce size per time strip, specialization of servers according to the necessities of the customers, In fact, the UOC CC managers have asked to us for a longer and deeper analysis of its system, so it can consider other workforce details (as the possibility of hire some half-time servers) and a 6 months long study. Following this line, we are currently working in a new release of SimuC@ll that will surely improve many aspects which have been depicted in the current version. 11. Acknowledgements The authors are grateful for the help supplied for the managers of the UOC CC in applying the first release of the SimuC@ll add-in. Similarly, we would not have written this paper without the overwhelming number of phone calls of the students and potential students of the Universitat Oberta de Catalunya. We appreciate this indirect help. We are also thankful to the UOC CC managers for recognizing the potential of the students to influence operations improvement in organizations. References Aksin, Z. and Harker, P., Modeling a Phone Center: Analysis of a Multichannel, Multiresource Processor Shared Loss System. Management Science, Vol. 47, No. 2, February 2001, 2001, pp Albright, S.C., VBA for Modelers. Developing Decision Support Systems with Microsoft Excel NY. Duxbury. Bodin, M. and Dawson, K. (2002): The Call Center Dictionary. CMP Books. Third Edition. Gilroy. CA. Brigandi, A.J., Dargon, D.R., Sheehan, M.J. and Spencer, T. (1994): AT&T Call Processing Simulator (CAPS) Operational Design for Inbound Call Centers. Interfaces, Vol. 24, No. 1, January-February 1994, pp Dawson, K. (2001): The Call Center Handbook. The Complete Guide to Starting, Running, and Improving your Call Center. CMP Books. Third Edition. Gilroy. CA. 15

16 Faulin, J. and Juan, A. (2002): Designing a Simulation Add-in for Describing the Strategic Change in a Call Center. Proceedings of the International Conference on Modeling and Simulation in Technical and Social Sciences. Girona (Spain), June Available in Gass, S.I., Hirshfeld, D.S. and Wasil, E.A. (2000): Model World: The Spreadsheeting of OR/MS. Interfaces, Vol. 30, No. 5, September-October 2000, pp Gross, D. and Harris, C. (1998): Fundamentals of Queueing Theory. John Wiley & Sons. Pacific Grove. CA. Grossman, T. A. (1999): Teacher s Forum: Spreadsheet Modeling and Simulation Improves Understanding of Queues. Interfaces, Vol. 29, No. 3, May-June 1999, pp Mehrotra, V. (1997): Ringing up Big Business. OR/MS Today, Vol. 24, No. 4. August 1997, pp Plane, D. (1997): How to Build Spreadsheet Models for Production and Operations Management. OR/MS Today, Vol. 24, No 1. February 1997, pp Saltzman, R. M. and Mehrotra, V. (2001): A Call Center Uses Simulation to Drive Strategic Change Interfaces, Vol. 31, No. 3, Part 1 of 2, May-June 2001, pp Savage, S. (1997): Weighing the Pros and Cons of Decision Technology in Spreadsheets. OR/MS Today, Vol. 24, No 1. February 1997, pp Thiriez, H. (2001): Improved OR Education through the Use of Spreadsheet Models, European Journal of Operational Research, 35, pp Van Dijk, N. (2000): On Hybrid Combination of Queueing and Simulation in Proceedings of the 2000 Winter and Simulation Conference. Orlando (Florida). (eds) Joines, J.A., Barton, R.R., Kang, K. and Fishwick, P.A., pp