Bora Kolfal

Call-Center Labor Cross-Training: It's a Small World After All

It is well known that flexibility can be created in manufacturing and service operations by using multipurpose production sources such as cross-trained labor, flexible machines, or flexible factories. We focus on flexible service centers, such as inbound call centers with cross-trained agents, and model them as parallel queueing systems with flexible servers. We propose a new approach to analyzing flexibility arising from the multifunctionality of sources of production. We create a work sharing (WS) network model for which its average shortest path length (APL) metric can predict the more effective of two alternative cross-training structures in terms of customer waiting times. We show that the APL metric of small world network (SWN) theory is one simple deterministic solution approach to the complex stochastic problem of designing effective workforce cross-training structures in call centers.

Maximizing Throughput in Finite-Source Parallel Queue Systems

Motivated by the dispatching of trucks to shovels in surface mines, we study optimal routing in a Markovian finite-source, multi-server queueing system with heterogeneous servers, each with a separate queue. We formulate the problem of routing customers to servers to maximize the system throughput as a Markov Decision Process. When the servers are homogeneous, we demonstrate that the Shortest Queue policy is optimal, and when the servers are heterogeneous, we partially characterize the optimal policy and present a near-optimal and simple-to-implement policy. We use the model to illustrate the substantial benefits of pooling, by comparing it to the permanent assignment of customers to servers.

Capability flexibility: a decision support methodology for parallel service and manufacturing systems with flexible servers

To obtain improved performance, many firms pursue operational flexibility by endowing their production operations with multiple capabilities (e.g., multi-skilled workers, flexible machines and/or flexible plants). This article focuses on the problem of ranking (according to average wait in queue) alternative system designs that vary by capacity and the structure of capabilities for open, parallel queueing networks with partially flexible servers. Prior literature introduced the Structural Flexibility (SF) concept and because the SF method was intended for a strategic context with very little information, it did not incorporate mean service times by demand type, server speeds, or wide ranges in demand arrival rates. This article develops the Capability Flexibility (CF) index methodology to extend the range of operational environments and designs that can be ranked. By showing the effectiveness of a deterministic, second-order approximation of a capability-designs relative flexibility/performance—the CF index—it proved possible to establish the insight that the proposed simple deterministic approximation of these complex stochastic is able to capture the dominant drivers of congestion of one design relative to another.

Market Impact on IT Security Spending

Traditionally, IT security investment decisions are made in isolation. However, as firms that compete for customers in an industry are closely interlinked, a macro perspective is needed in analyzing the IT security spending decisions and this is a key contribution of the paper. We introduce the notions of direct- and cross-risk elasticity to describe the customer response to adverse IT security events in the firm and competitor, respectively, thus allowing us to analyze optimal security investment decisions. Both symmetric and asymmetric cases are examined for a duopoly in a continuous-time Markov chain (CTMC) setting. We demonstrate that optimal IT security spending, expected firm profits and willingness of firms to cooperate with competitors to improve security are highly dependent on the nature of customer response to adverse events, especially whether customer response to adverse security events in the competitor increases or decreases firm demand.

Modeling Load and Overwork Effects in Queueing Systems with Adaptive Service Rates

Servers in many real queueing systems do not work at a constant speed. They adapt to the system state by speeding up when the system is highly loaded or slowing down when load has been high for an extended time period. Their speed can also be constrained by other factors, such as geography or a downstream blockage. We develop a state-dependent queueing model in which the service rate depends on the system “load” and “overwork.” Overwork refers to a situation where the system has been under a heavy load for an extended time period. We quantify load as the number of users in the system, and we operationalize overwork with a state variable that is incremented with each service completion in a high-load period and decremented at a rate that is proportional to the number of idle servers during low-load periods. Our model is a quasi-birth-and-death process with a special structure that we exploit to develop efficient and easy-to-implement algorithms to compute system performance measures. We use the analytical ...

Load Effect on Service Times

In this paper, we develop a general framework to analyze the influence of system load on service times in queueing systems. Our framework unifies previous results and ties them to possible future studies to help empirical and analytical researchers to investigate and model the ways in which load impacts service times. We identify three load characteristics: changeover, instantaneous load, and extended load. The load characteristics induce behaviors, or mechanisms, in at least one of the system components: the server, the network, and the customer. A mechanism influences the service-time determinants: the work content or the service speed. We identify and define mechanisms that cause service times to change with load and use the framework to categorize them. We argue that an understanding of the relationship between load and service times can come about only by understanding the underlying mechanisms.

Business-to-Consumer Platform Strategy: How Vendor Certification Changes Platform and Seller Incentives

We build an economic model to study the problem of offering a new, high-certainty channel on an existing business-to-consumer platform such as Taobao and eBay. On this new channel, the platform owner exerts effort to reduce the uncertainty of service quality. Sellers can either sell through the existing low-certainty channel or go through additional screening to sell on this new channel. We model the problem as a Bertrand competition game where sellers compete on price and exert effort to provide better service to consumers. In this game, we consider a reputation spillover effect that refers to the impact of the high-certainty channel on the perceived service quality in the low-certainty channel. Counter-intuitively, we find that low-certainty channel demand will decrease as the reputation spillover effect increases, in the case of low inter-channel competition. Also, low-certainty channel demand increases as the quality uncertainty increases, in the case of intense inter-channel competition. Furthermore, the platform owner should offer a new high-certainty channel when (i) the perceived quality for this channel is sufficiently high, (ii) sellers in this channel are able to efficiently provide quality service, (iii) consumers in this channel are not so sensitive to the quality uncertainty, or (iv) the reputation spillover effect is high. In the one-channel case, the incentives of the platform owner and sellers are aligned for all model parameters. However, this is not the case for the two-channel solution, and our model reveals where tensions will arise between parties.