Call-Routing Schemes for Call-Center Outsourcing

Companies may choose to outsource parts, but not all, of their call-center operations. In the course of studying contact centers in the telecommunications and financial services industries, we have observed the following (apparently) common scheme. A company classifies its customers as high- or low-value, serving the former with their “in house” operations and routing the latter to an outsourcer. Typically, the company imposes service-level constraints on the time each type of customer waits on hold. This paper considers four schemes for routing low-value calls between the client company and the outsourcer. These schemes vary in the complexity of their routing algorithms, as well as the sophistication of the telephone and information technology infrastructure they require of the two operations. For three of these schemes, we provide a direct characterization of system performance. For the fourth, most complex, scheme we provide performance bounds for the important special case in which the service requirements of high- and low-value callers are the same. These results allow us to systematically compare the performance of the various routing schemes. Our results suggest that, for clients with large outsourcing requirements, the simpler schemes that require little client-outsourcer coordination can perform very well

Incentives for Quality through Endogenous Routing

We study how rework routing together with wage and piece rate compensation can strengthen incentives for quality. Traditionally, rework is assigned back to the agent who generates the defect (in a self routing scheme) or to another agent dedicated to rework (in a dedicated routing scheme). In contrast, a novel cross routing scheme allocates rework to a parallel agent performing both new jobs and rework. The agent who passes quality inspection or completes rework receives the piece rate paid per job. We compare the incentives of these rework allocation schemes in a principal-agent model with embedded quality control and routing in a multi-class queueing network. We show that conventional self routing of rework can never induce first-best effort. Dedicated routing and cross routing, however, strengthen incentives for quality by imposing an implicit punishment for quality failure. In addition, cross routing leads to workload allocation externalities and a prisoner’s dilemma, thereby creating highest incentives for quality. Firm profitability depends on capacity levels, revenues, and quality costs. With ample capacity, dedicated routing and cross routing both achieve first-best profit rate, while self routing does not. With limited capacity, cross routing generates the highest profit rate when appraisal, internal failure, or external failure costs are high, while self routing performs best when gross margins are high. When the number of agents increases, the incentive power of cross routing reduces monotonically and approaches that of dedicated routing.queueing networks; routing; Nash equilibrium; quality control; piece rate; epsilon equilibrium.

© 2012 INFORMS Overflow Networks: Approximations and

Motivated by call center cosourcing problems, we consider a service network operated under an overflow mechanism. Calls are first routed to an in-house (or dedicated) service station that has a finite waiting room. If the waiting room is full, the call is overflowed to an outside provider (an overflow station) that might also be serving overflows from other stations. We establish approximations for overflow networks with many servers under a resource-pooling assumption that stipulates, in our context, that the fraction of overflowed calls is nonnegligible. Our two main results are (i) an approximation for the overflow processes via limit theorems and (ii) asymptotic independence between each of the in-house stations and the overflow station. In particular, we show that, as the system becomes large, the dependency between each in-house station and the overflow station becomes negligible. Independence between stations in overflow networks is assumed in the literature on call centers, and we provide a rigorous support for those useful heuristics. Subject classifications: overflow networks; cosourcing; heavy-traffic approximations; separation of time scales

Approximating multiple class queueing models with loss models

Multiple class queueing models arise in situations where some flexibility is sought through pooling of demands for different services. Earlier research has shown that most of the benefits of flexibility can be obtained with only a small proportion of cross-trained operators. Predicting the performance of a system with different types of demands and operator pools with different skills is very difficult. We present an approximation method that is based on equivalent loss systems. We successively develop approximations for the waiting probability, The average waiting time and the service level. Our approximations are validated using a series of simulations. Along the way we present some interesting insights into some similarities between queueing systems and equivalent loss systems that have to our knowledge never been reported in the literature.

Matching Economic Efficiency and Environmental Sustainability: The Potential of Exchanging Excess Capacity in Cloud Service Environments

Excess capacity is a major problem for service providers. While manufacturers e.g. can produce on stock to fully utilize their capacity, the service industry traditionally faces the problem of idle capacity resulting in economic and environmental inefficiencies. Recent technological developments offering dynamic information and integration capabilities may help, as they enable an on-demand exchange of excess capacity. To examine the possible benefits of corresponding excess capacity markets, we examine a capacity related optimization problem of a service provider with and without relying on excess capacity. Therefore we build a mathematical model based on queuing theory which is evaluated with a discrete-event simulation applying the situation of providers for banking transaction services. By solving the optimization problem we found reasonable benefits of excess capacity markets concerning the economic and environmental perspective. Being a first quantitative approach, the model thereby builds the basis for empirical validation and further theoretical research

IT-enabled Excess Capacity Markets for Services: Examining the Economic Potential in Cost-driven Service Supply Chains

Capacity planning is a major challenge for service providers facing volatile demand. Inefficiencies result from idle capacity or lost revenue caused by peak loads. Concerning IT-driven services, recent technological developments offering dynamic integration and information capabilities may help. They enable an on-demand exchange of excess capacity between business partners and create value through efficient capacity allocation within a supply chain. This paper aims at examining this economic potential of IT-enabled excess capacity markets. Therefore, we use the model setting of a three-stage IT-driven service supply chain and discuss different factors influencing the capacity optimization problem. With a discrete-event simulation we then evaluate a representative factor quantitatively. Thus, we provide deeper insights about the usefulness of excess capacity markets for capacity optimization in different settings and scenarios. The results serve as a guide for practitioners, build the basis for further quantitative evaluation and represent a starting point for empirical validation

Efficient searchable symmetric encryption for storing multiple source data on cloud

© 2015 IEEE. Cloud computing has greatly facilitated large-scale data outsourcing due to its cost efficiency, scalability and many other advantages. Subsequent privacy risks force data owners to encrypt sensitive data, hence making the outsourced data no longer searchable. Searchable Symmetric Encryption (SSE) is an advanced cryptographic primitive addressing the above issue, which maintains efficient keyword search over encrypted data without disclosing much information to the storage provider. Existing SSE schemes implicitly assume that original user data is centralized, so that a searchable index can be built at once. Nevertheless, especially in cloud computing applications, user-side data centralization is not reasonable, e.g. an enterprise distributes its data in several data centers. In this paper, we propose the notion of Multi-Data-Source SSE (MDS-SSE), which allows each data source to build a local index individually and enables the storage provider to merge all local indexes into a global index afterwards. We propose a novel MDS-SSE scheme, in which an adversary only learns the number of data sources, the number of entire data files, the access pattern and the search pattern, but not any other distribution information such as how data files or search results are distributed over data sources. We offer rigorous security proof of our scheme, and report experimental results to demonstrate the efficiency of our scheme

Nonbanks and risk in retail payments

This paper documents the importance of nonbanks in retail payments in the United States and in 15 European countries and analyzes the implications of the importance and multiple roles played by nonbanks on retail payment risks. This paper also reviews the main regulatory safeguards in place, and concludes that there may be a need to reconsider some of them in view of the growing role of nonbanks and of the global reach of risks in the electronic era.