Applying a call center multiagent simulation model to study how routing policies can reduce call costs in toll-free call centers (original) (raw)
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Dynamic Call Center Routing Policies Using Call Waiting and Agent Idle Times
Manufacturing & Service Operations Management, 2014
We study call routing policies for call centers with multiple call types and multiple agent groups. We introduce new weight-based routing policies where each pair (call type, agent group) is given a matching priority defined as an affine combination of the longest waiting time for that call type and the longest idle time or the number of idle agents in that agent group. The coefficients in this combination are parameters to be optimized. This type of policy is more flexible than traditional ones found in practice, and it performs better in many situations. We consider objective functions that account for the service levels, the abandonment ratios and the fairness of occupancy across agent groups. We select the parameters of all considered policies via simulation-based optimization heuristics. This only requires the availability of a simulation model of the call center, which can be much more detailed and realistic than the models used elsewhere in the literature to study the optimality of certain types of routing rules. We offer a first numerical study of realistic routing rules that takes into account the complexity of real-life call centers.
Simulation of Optimal Routing in Call Centers
Egyptian Computer Science Journal , 2018
Call centers can provide service to customers, handle queries, offer product support, carry out telemarketing, or market research. The challenge of queue at call center is a function of both waittimes resulting from lack of available severs and ineffective call resolution which is has to do with the agent's skill and experience to handle the problem. This paper studies the concept of call center and its challenges, discuses related literatures and adopted the results obtained from [14,16], indicating optimal as SSTF and SQR while [15] further hybridized both optimal rules to obtain Hybrid Heterogeneous Call Routing Rule (HHCRR). The methodology deployed was discrete event-driven simulation. In the displayed simulation result between SSTF, SQR and HHCRR, findings from the result proves that HHCRR performs better than both the optimal rule for wait-time (SSTF) and call resolution (SQR) routing rules
Analyzing Skill-Based Routing Call Centers Using Discrete-Event Simulation and Design Experiment
Proceedings of the 2004 Winter Simulation Conference, 2004., 2004
Call center customer service representatives (CSRs) or agents tend to have different skills. Some CSRs can handle one type of call, while other CSRs can handle other types of calls. Advances in automatic call distributors (ACDs) have made it possible to have skill-based routing (SBR) which is the protocol for online routing of incoming calls to the appropriate CSRs. At present, very little is known about SBR. We develop a discrete-event simulation model to analyze the performance of a M n /M n /C/K SBR environment in which incoming calls are handled in priority order and in a non-preemptive manner. We use the design of experiment framework to conduct our analysis. We show empirically that the scenario in which agents have 2 skills is almost as efficient as the scenario where agents have all skills (resource pooling). Also, we discover that no interaction exists between call rate factors when resource pooling exists.
Modeling and Simulation of Call Centers
Proceedings of the Winter Simulation Conference, 2005., 2005
In this review, we introduce key notions and describe the decision problems commonly encountered in call center management. Main themes are the central role of uncertainty throughout the decision hierarchy and the many operational complexities and relationships between decisions. We make connections to analytical models in the literature, emphasizing insights gained and model limitations. The high operational complexity and the prevalent uncertainty suggest that simulation modeling and simulation-based decision-making could have a central role in the management of call centers. We formulate some common decision problems and point to recently developed simulation-based solution techniques. We review recent work that supports modeling the primitive inputs to a call center and highlight call center modeling difficulties.
Improving Call Center Operations Using Performance-Based Routing Strategies
2007
The paper presents a simulation study of performance-based call routing strategies using a variety of routing rules based on historic data such as average handling time and first call resolution rate. We demonstrate the relative benefits of various performance-based call routing strategies using actual data from a financial call center. In particular, our modeling results indicate that call routing based on adjusted average handle time (AAHT) and z-scores of AAHT are two strategies that can substantially improve overall call center performance and customer satisfaction.
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.
2006
In modern society, Service Systems are considered one of the most profitable assets of both Business Companies and Public Organizations, since they can offer a highly added value for many different kind of products, both tangible and intangible. Among these Service Systems, Call Centers are considered to represent one of the most difficult cases to well understand and hence to manage. This difficulty is mainly due both to their intrinsic dynamics and to the unpredictable behaviour of input data. Furthermore, modern Call Centers tend to focus on the management of various and different types of calls, which most of the times require the government of different situations together with different agent (operator) skill management. So, in this type of scenarios, the main objective is focused on finding an efficient way to organize and manage a multi task Call Center.
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.
Staffing Multiskill Call Centers via Linear Programming and Simulation
Management Science, 2008
We study an iterative cutting-plane algorithm on an integer program, for minimizing the staffing costs of a multiskill call center subject to service-level requirements which are estimated by simulation. We solve a sample average version of the problem, where the service-levels are expressed as functions of the staffing for a fixed sequence of random numbers driving the simulation.