A Comparative Analysis of Waiting Time Routing Rule for Queue Reduction in Call Center

Satisfying customers need has become an inevitable phenomenon for businesses to survive in this high competitive era. The persistent complaints of waiting queue by customers have continuously posed a threat to call centres globally. Call centres are an important function of most companies’ day to day business activities. They are often the link between a company and its customers and hugely impact the customer’s perspective or point of view of a company. The queue experienced by customers at call centres is increasingly becoming alarming as many customers are irritated by the long time spent on the queue before their calls are been answered. It is imperative that an investigation be carried out into what criteria are been used to route calls to any particular call centre agent at any given time and how average handling time influences waiting queues at call centres. It is important to conduct a comparative analysis to evaluate existing routing rules for waiting time rules to determine the optimal among the waiting time rule. This study used a collection of java programs to simulate existing rule for waiting time routing rules. The JAVA program evaluator evaluates each of the waiting time routing rules using the call canter’s raw data, collected from the data logging system of Global  Communications, one of the biggest telecommunication company in Nigeria.  The study simulated four rules, Java program were developed for each of the four routing rules since their procedure varies from one another. The result from our simulation was able to determine the optimal rule among the existing waiting time routing rules. Keywords: Call center, Queuing system, Routing rules, Average Speed of Answe

A Comparative Analysis Of Conventional Software Development Approaches Vs. Formal Methods In Call Distribution Systems

When we think about formal method; the first thing which comes in our mind is mathematical approach. The process of formalization is an approach based on mathematics and used to elaborate the properties of systems (hardware and software). The mathematical modeling or formal methods provide us a framework for large and complex systems. Thus these systems can be specified, analyzed, designed, and verified in a systematic way rather than the approaches which are used conventionally. Formal verification and the methods are applied using theoretical computer science fundamentals to solve the complex and difficult problems in large and complex software and hardware systems to ensure the systems will not fail with run-time errors. Conventional approaches of software verification in call distribution systems rely on quality assurance to verify the system behavior and robustness. The process of software testing cannot show the absence of errors it can only show the presence of errors in software systems. [1] In contrast, the mathematically-based techniques of verification are based on formal methods to prove certain software attributes, for example proving that software does or does not contain the occurrence of errors at run-time such as overflows, divide-by-zero, and access violation, invalid memory access and stack/heap corruption. [1] In this paper later we will have comparative analysis of formal methods vs. conventional software development approaches in call distribution systems. Using this comparison we‘ll try to identify the methodologies and approaches which would be better in SDLC for call distribution systems.

Staffing to Maximize Profit for Call Centers with Impatient and Repeat-Calling Customers

Motivated by call center practice, we study the optimal staffing of many-server queues with impatient and repeat-calling customers. A call center is modeled as an M/M/s+M queue, which is developed to a behavioral queuing model in which customers come and go based on their satisfaction with waiting time. We explicitly take into account customer repeat behavior, which implies that satisfied customers might return and have an impact on the arrival rate. Optimality is defined as the number of agents that maximize revenues net of staffing costs, and we account for the characteristic that revenues are a direct function of staffing. Finally, we use numerical experiments to make certain comparisons with traditional models that do not consider customer repeat behavior. Furthermore, we indicate how managers might allocate staffing optimally with various customer behavior mechanisms

Routing to manage resolution and waiting time in call centers with heterogeneous servers

In many call centers, agents are trained to handle all arriving calls but exhibit very different performance for the same call type, where we define performance by both the average call handling time and the call resolution probability. In this paper, we explore strategies for determining which calls should be handled by which agents, where these assignments are dynamically determined based on the specific attributes of the agents and/or the current state of the system. We test several routing strategies using data obtained from a medium-sized financial service firm's customer service call centers and present empirical performance results. These results allow us to characterize overall performance in terms of customer waiting time and overall resolution rate, identifying an efficient frontier of routing rules for this contact center. © 2012 INFORMS.link_to_subscribed_fulltex

Threshold Routing to Trade-off Waiting and Call Resolution in Call Centers

In a call center, agents may handle calls at different speeds, and also may be more or less successful at resolving customers’ inquiries, even when only considering customers calling with similar requests. One common measure of successful call resolution is whether or not the call results in the customer calling back. This presents a natural trade-off between speed and quality, where speed is defined as the average time before an incoming call is answered (the average waiting time) and quality is defined as the percentage of all arriving calls that do not result in callbacks (the call resolution). The relevant control is the routing; that is, the decision concerning which agent should handle an arriving call when more than one agent is available. In an inverted-V model setting, we formulate an optimization problem with the dual performance objective of minimizing average customer waiting time and maximizing the call resolution. We solve this optimization problem asymptotically in the Halfin-Whitt many-server limit regime, interpret its solution as a routing control for the discrete-event system, and show via simulation that the interpreted routing control is on the efficient frontier. In particular, any routing control that has a lower average waiting time (higher call resolution) must also have a lower call resolution (higher average waiting time)

Staffing, Routing, and Payment to Trade off Speed and Quality in Large Service Systems

Most common queueing models used for service-system design assume that the servers work at fixed (possibly heterogeneous) rates. However, real-life service systems are staffed by people, and people may change their service speed in response to incentives. The delicacy is that the resulting service speed is jointly affected by staffing, routing, and payment decisions. Our objective in this paper is to find a joint staffing, routing, and payment policy that induces optimal service-system performance. We do this under the assumption that there is a trade-off between service speed and quality and that employees are paid based on both. The employees selfishly choose their own service speed to maximize their own expected utility (which depends on the staffing through their busy time). The endogenous service-rate assumption leads to a centralized control problem in which the system manager jointly optimizes over the staffing, routing, and service rate. By solving the centralized control problem under fluid scaling, we find four different economically optimal operating regimes: critically loaded, efficiency driven, quality driven, and intentional idling (in which there is simultaneous customer abandonment and server idling). Then we show that a simple piece-rate payment scheme can be used to solve the associated decentralized control problem under fluid scaling