Staffing and routing in a two-tier call centre (original) (raw)

Staffing and Routing in a Two-Tier Call Center

SSRN Electronic Journal, 2000

This paper studies service systems with gatekeepers who diagnose a customer problem and then either refer the customer to an expert or attempt treatment. We determine the staffing levels and referral rates that minimise the sum of staffing, customer waiting, and mistreatment costs. We also compare the optimal gatekeeper system (a two-tier system) with a system staffed with only experts (a direct-access system). When waiting costs are high, a direct-access system is preferred unless the gatekeepers have a high skill level. We also show that an easily computed referral rate from a deterministic system closely approximates the optimal referral rate.

Queuing for Expert Services

Management Science, 2008

We consider a monopolist expert offering a service with a 'credence' characteristic. A credence service is one where the customer cannot verify, even after purchase, whether the amount of prescribed service was appropriate or not; examples include legal, medical or consultancy services and car repair. This creates an incentive for the expert to 'induce service', that is, to provide unnecessary services that add no value to the customer, but that allow the expert to increase his revenues. We focus on the impact of an operations phenomenon on service inducement -workload dynamics due to the stochasticity of interarrival and service times. To this end, we model the expert's service operation as a single-server queue.

Routing and staffing in large call centers with specialized and fully flexible servers

2004

We use models of loss systems and a queueing simulation to optimize routing and staffing decisions in large call centers with any number of customer types and a mixture of dedicated and completely flexible servers. Given that flexible servers are no faster than specialists for a particular customer type, we show that in the loss system it is always optimal to send customers to specialists first. If all appropriate specialists are busy, we show that it is always optimal to send the customer to a flexible server, as long as the service time distribution on the flexible server is independent of the customer type. To specify the optimal mixture of dedicated and flexible staff, we find that a simple 80/20 rule works well for a remarkably wide range of parameters. In particular, for call centers that provide excellent service by setting a tight constraint on the customer loss rate or average waiting time, we find that 20% of the staffing budget should be spent on flexible servers while 80% should be spent on dedicated servers. We provide some intuition as to why a single proportional solution is so robust and discuss extensions to other types of systems.

Optimizing the Staffing and Routing of Small-Size Hierarchical Call Centers

Production and Operations Management, 2008

M ultiple-skill call centers propagate rapidly with the development of telecommunications. An abundance of literature has already been published on call centers. Here, we want to focus on centers that would typically occur in business-to-business environments; these are call centers that handle many types of calls but where the arrival rate for each type is low. To find an optimal configuration, the integrality of the decision variables is a much more important issue than for larger call centers. The present paper proposes an approach that uses elements of combinatorial optimization to find optimal configurations. We develop an approximation method for the evaluation of the service performance. Next, we search for the minimum-cost configuration subject to service-level constraints using a branch-and-bound algorithm. What is at stake is to find the right balance between gains resulting from the economies of scale of pooling and the higher cost or cross-trained agents. The article shows that in most cases this method significantly decreases the staffing cost compared with configurations with only cross-trained or dedicated operators.

Gatekeepers and Referrals in Services

Management Science, 2003

T his paper examines services in which customers encounter a gatekeeper who makes an initial diagnosis of the customer's problem and then may refer the customer to a specialist. The gatekeeper may also attempt to solve the problem, but the probability of treatment success decreases as the problem's complexity increases. Given the costs of treatment by the gatekeeper and the specialist, we find the firm's optimal referral rate from a particular gatekeeper to the specialists. We then consider the principal-agent problem that arises when the gatekeeper, but not the firm, observes the gatekeeper's treatment ability as well as the complexity of each customer's problem. We examine the relative benefits of compensation systems designed to overcome the effects of this information asymmetry and show that bonuses based solely on referral rates do not always ensure first-best system performance and that an appropriate bonus based on customer volume may be necessary as well. We also consider the value of such output-based contracts when gatekeepers are heterogeneous in ability, so that two gatekeeper types face different probabilities of treatment success when given the same problem. We show that the firm may achieve first-best performance by either offering two contracts that separate the gatekeeper types or by offering a single contract that coordinates the treatment decisions of both gatekeepers. (Stochastic Model Applications; Personnel; Principal-Agent Problems; Routing/Triage)

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.

Service Center Staffing with Cross‐Trained Agents and Heterogeneous Customers

Production and Operations Management, 2018

We model a real-world service center with cross-trained agents serving customer requests that are heterogeneous with respect to complexity and priority levels: High priority requests preempt low priority requests and low skilled agents can only serve less complex requests, while high skilled agents can serve all requests. Our main aim is to dynamically assign requests to agents considering the priority and complexity levels of requests. We model this system as a Markov chain that is infinite in multiple dimensions and thus is not amenable to exact analysis. We therefore apply approximation and bounding techniques to develop a tractable, novel algorithm using the Matrix Analytic Method. Our algorithm closely approximates the operations of the real-world service system under a simple but effective threshold-based request-assignment policy. Extensive computational results demonstrate the usefulness of our algorithm to minimize costs given an existing staffing configuration, as well as in helping to make long-term staffing decisions. In addition, our algorithm also has at least two orders of magnitude shorter computation times than each replication of simulation. Hence, it is both fast and accurate.