Call center performance evaluation (original) (raw)

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.

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.

Pooling strategies for call center agent cross-training

IIE Transactions, 2009

We consider call center environments where agents serve distinct customer types, and investigate the efficiency benefits achievable via cross-training. We do this by first considering specialized agents grouped into N departments according to the customer type they serve. Then we examine cross-training policies that pool a set of departments K (selecting k = |K| of N) into a single larger department in which every agent serves all of the pooled call types. For the pooled department, we analyze both First-Come-First-Served (FCFS) and Non-Preemptive Priority (NPP) service disciplines. By comparing the resulting queueing models via standard queueing approximations and numerical analysis, we characterize the impact of system parameters, such as department sizes, arrival rates and service times, on the decision of what departments to pool.

Simulation-based optimization of agent scheduling in multiskill call centers

2008

We examine and compare simulation-based algorithms for solving the agent scheduling problem in a multiskill call center. This problem consists in minimizing the total costs of agents under constraints on the expected service level per call type, per period, and aggregated. We propose a solution approach that combines simulation with integer or linear programming, with cut generation. In our numerical experiments with realistic problem instances, this approach performs better than all other methods proposed previously for this problem. We also show that the two-step approach, which is the standard method for solving this problem, sometimes yield solutions that are highly suboptimal and inferior to those obtained by our proposed method.

Limited Agents Mix-Training in Operations – A Call Center Case

2017

The operations of an inbound call center are difficult to handle; there is important uncertainty in the arrival rates estimates, and the operation is usually depending on severe service level restrictions. This paper is inspired by the work with an outsourcing call center where projects involve an inbound help desk depending on a service level agreement (SLA). Services of support are very specialized and an important training investment is needed and this kind of investment cannot be transferred to other projects. In this work, it will be analyzed the possibility of cross training a group of agents in order to serve calls from two different projects. This process is called a partial merging. This paper looks for quantifying the advantages of partial merging and characterizing the circumstances under which merging is more profitable. It will be seen that low levels of cross training produce important profit.

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.

Call Center Scheduling Technology Evaluation Using Simulation

Proceedings of the 33nd conference on …, 2001

Telemarketers, direct marketing agencies, collection agencies and others whose primary means of customer contact is via the telephone invest considerable sums of money to make the calling operation efficient and produc-tive. Investments are required in human resources, infra- ...

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.

Scheduling of agents in inbound multilingual call centers

Brazilian Journal of Operations & Production Management, 2017

This paper presents an Integer Linear Programming model, which enables the scheduling of agents in call centers according to segmentation by skills, as well as the inclusion of labor law restrictions. The results from the experimental application of this method to situations involving real-time problems are presented, wherein the necessary shifts to meet forecasted demand are provided, in addition to an economically optimum combination of agents and skills, generating a solution, which is sufficient to maintain the desired level of service at the lowest cost possible. The approach proposed in this study presents an alternative worthy of consideration, since it is able to successfully incorporate the flexibility of call centers with skills-based routing in an easy-to-implement model that has proven to be effective in experiments conducted in on-the-job settings. Advances in information technology and telecommunications in recent years have increasingly heightened the presence of call...