Modeling and Simulation of Call Centers (original) (raw)
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Call center simulation modeling: Methods, challenges, and opportunities
… , 2003. Proceedings of the 2003 Winter, 2003
Using stochastic models to plan call center operations, schedule call center staff efficiently, and analyze projected performance is not a new phenomenon, dating back to Erlang's work in the early twentieth century. However, several factors have recently conspired to increase demand for call center simulation analysis. Increasing complexity in call traffic, coupled with the almost ubiquitous use of Skill-Based Routing. Rapid change in operations due to increased merger and acquisition activity, business volatility, outsourcing options, and multiple customer channels (inbound phone, outbound phone, email, web, chat) to support. Cheaper, faster desktop computing, combined with specialized call center simulation applications that are now commercially available. In this tutorial, we will provide an overview of call center simulation models, highlighting typical inputs and data sources, modeling challenges, and key model outputs. In the process, we will also present an interesting "realworld" example of effective use of call center simulation. 1 INTRODUCTION: "WHY CALL CENTERS?" The trend in our economy from manufacturing towards services is well documented. One notable facet of this transition towards services has been the explosion of the call center industry. Mehrotra (1997) defines call centers as "Any group whose principal business is talking on the telephone to customers or prospects." In this paper, we will refer to the individuals who talk on the phone with customers as "agents." While the size of the industry is difficult to accurately determine, a plethora of statistics from diverse sources reflect that fact that this is a huge and growing global industry. Most stunning: Mandelbaum (2001) cites a study that an estimated 3% of the United States population works in this industry. Most recent: an explosion of outsourced call centers springing up in India, the Philippines, the Caribbean, and Latin America, serving overseas customers in the United States and Western Europe as well as growing domestic market needs.
Modelling and simulation of a telephone call center
Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693), 2003
We consider a system with two types of traffic and two types of agents. Outbound calls are served only by blend agents, whereas inbound calls can be served by either inboundonly or blend agents. Our objective is to allocate a number of agents such that some service requirement is satisfied. We have taken two approaches in analyzing this staffing problem: We developed a simulation model of the call center, which allows us to do a what-if analysis, as well as continuous-time Markov chain (CTMC) queueing models, which provide approximations of system performance measures. We describe the simulation model in this paper.
Scenario Analysis within a Call Center Using Simulation
Journal of Operations and Supply Chain Management
This paper works on and presents the results of several analyses - scenario and sensitivity - made with the help of Simulation and focused on dimensioning questions of handling capacity in a large Brazilian call center. The objective is to measure the sensitivity of the call center's performance to potential modifications of critical variables. The bibliography related to the application of such tool in call centers is reviewed, and the way by which the problem is treated nowadays is described in detail. The methodology used to achieve this article objective involved a simulation model in the Arena Contact Center software, which worked as base case upon where the scenario and sensitivity analyses could be performed. This paper comes to the conclusion that Simulation is a tool perfectly adequate to its purpose as long as it could be able to show, for the studied call center, mainly that: (i) it is possible to reduce the operator contingent; (ii) fair variations on the demand patt...
2012
The ability to profitably manage the level of resources in a service system can be considered a strategic skill in all those organizations, including no-profit ones and Public Administrations, that aim at providing an added value service to customers as well as balancing the level of service (in terms of quality) with costs. In this paper we will focus on a typical service system inside of which, in every moment, management struggles in order to reach that balance, because of the extremely dynamic behavior of the entire system: a Call Center. Our aim is to show that an efficient management of the customers abandonment and the quality of service offered to customers, can positively impact on a correct resource leveling in the system, which may otherwise be found by means of typical Operations Research or Queuing Theory methods. In particular, we want to show that this can be more easily inferred and understood by resorting to simulation. After introducing some preliminary aspects by means of Queuing Theory (Erlang's formula), we'll first study the problem of customer abandonment (balking and reneging) by simulating a Call Center simple model by means of a process-oriented discrete-time simulation tool (Arena), and then explore a more complex model taking into 2 account customer satisfaction an the quality of the service offered, approaching the modelling process of the system by means of System Dynamics. Results show that the level of resources can be further reduced, and that the customer (often thought as an entity external to the system) plays instead an important role on the performance of the whole system, both operationally and economically.
A simulation-based decision support system for workforce management in call centers
SIMULATION, 2013
Workforce management is critical in call centers, where thousands of calls are handled by hundreds of agents every day. In a call center, where the call arrival rates tend to fluctuate during the day, the agent allocation plans are required to be planned flexibly and the number of operating call center agents ought to be updated whenever needed, in order to keep the customer satisfaction level over a predefined level. Workforce plans are usually generated by the use of queuing models that are based on Erlang C calculations. However, they have assumptions that oversimplify the real system and jeopardize the validation of the model. At this point, the simulation models, which do not have such restrictive assumptions, are effective in calculating the required number of agents for each time period and measuring the performance of a given shift schedule. In this study, a simulation-based decision support system (DSS) is developed that runs on real-time data for one of the largest call centers in Turkey. The graphical user interfaces (GUIs) are designed in accordance with the man-machine interaction consideration to increase the usability, functionality and effectiveness of the DSS. It is shown that the combination of the advantages of simulation with a flexible and user-friendly DSS environment provides more effective and efficient workforce planning and performance reporting in call centers.
Telephone Call Centers: Tutorial, Review, and Research Prospects
2003
Telephone call centers are an integral part of many businesses, and their economic role is significant and growing. They are also fascinating socio-technical systems in which the behavior of customers and employees is closely intertwined with physical performance measures. In these environments traditional operational models are of great value -and at the same time fundamentally limited -in their ability to characterize system performance.
A Tutorial On Modelling Call Centres Using Discrete Event Simulation
ECMS 2013 Proceedings edited by: Webjorn Rekdalsbakken, Robin T. Bye, Houxiang Zhang, 2013
Arriving at an optimal schedule for the staff and determining their required skills in a call centre is imperative to balance the conflicting requirements of delightful customer experience, high employee satisfaction and low cost. Due to the complex nature of modern call centres, simulation modelling is increasingly being used to predict their performance. We have modelled a call centre using our in-house discrete event simulation tool called DESiDE. This paper describes how every component of call centres were modelled as simulation resources. This paper also describes the changes that had to be made to DESiDE in order to handle the special requirements of call centre modelling and also the metrics used by call centres.
Performance Indicators Analysis inside a Call Center Using a Simulation Program
International Journal of Business & Technology, 2016
This paper deals with and shows the results of different performance indicators analyses made utilizing the help of Simulation and concentrated on dimensioning problems of handling calls capacity in a call center. The goal is to measure the reactivity of the call center’s performance to potential changes of critical variables. The literature related to the employment of this kind of instrument in call centers is reviewed, and the method that this problem is treated momentarily is precisely described. The technique used to obtain this paper’s goal implicated a simulation model using Arena Contact Center software that worked as a key case at the time where the events analyses could be executed. This article comes to the conclusion that Simulation is a completely suitable instrument to accomplish its purpose since it could be adequate to demonstrate, for the call center taken into consideration principally that: (a) it is feasible to reduce the agent contingent; (b) reasonable variatio...