Knowledge that Transforms

Collusion in auctions, with different assumptions on distributions of bidders' private valuation, has been studied extensively over the years. With the recent development of on‐line markets, auctions are becoming an increasingly popular procurement method. The emergence of Internet marketplaces makes auction participation much easier and more convenient, since no physical presence of bidders is required. In addition, bidders in on‐line auctions can easily switch their identities. Thus, it may very well happen that the bidders in an auction have very little, if any, prior knowledge about the distributions of other bidders' valuations. We are proposing an efficient distribution of collusive profit for second‐price sealed bid auctions in such an environment. Unlike some known mechanism, which balance the budget only in expectation, our approach (which we call Random k) balances the budget ex‐post. While truth‐telling is not a dominant strategy for Random k, it is a minimax regret equilibrium.

The problem studied in this paper is a predigestion of the decision faced by online retailers (etailers) that advertise on publisher or comparison‐shopping websites. An etailer may sell its product not only through its online and bricks‐and‐mortar stores, but also through the websites of one or more third parties (e.g., Yahoo.com). However, the etailer has to pay a certain amount to such third parties in an action‐based payment scheme, such as a cost‐per‐click (CPC) scheme. Under the CPC scheme, payment is based solely on click‐throughs, which means that the etailer pays only when a shopper clicks through to the product page of its website. Only a fraction of such clicks lead to actual sales. The extra cost that is associated with shoppers who first click through to the third‐party websites makes them less attractive as customers than those who directly visit the etailer's online store. Moreover, the CPC rate for a prominent placement is normally set by competitive bidding, and thus varies over time. Therefore, the etailer needs to decide dynamically whether or not to list on a third‐party website. The structural properties of the optimal policy are discussed, and numerical examples are given to show the revenue impact of dynamic listing control.

Radio Frequency Identification (RFID) technology promises to transform supply chain management. Building on previous research in information systems and supply chain management, this paper proposes a theoretical framework for RFID adoption and benefits, and tests the framework using data on u. s. firms. Our analysis suggests that there is a positive association between information technology (IT) application deployment and RFID adoption. We find that RFID implementation spending and partner mandate are associated with an expectation of early return on RFID investment, and a perceived lack of industry‐wide standards is associated with an expectation of delayed return on RFID investment. These results suggest that firms with broad IT application deployment and a critical mass of RFID implementation spending are more likely to report early returns from RFID deployments. This paper extends previous research to understand the relationship between organization characteristics and adoption and expected benefits of the emerging RFID technology.

Telephone call centers and their generalizations—customer contact centers—usually handle several types of customer service requests (calls). Since customer service representatives (agents) have different call‐handling abilities and are typically cross‐trained in multiple skills, contact centers exploit skill‐based routing (SBR) to assign calls to appropriate agents, aiming to respond properly as well as promptly. Established agent‐staffing and SBR algorithms ensure that agents have the required call‐handling skills and that call routing is performed so that constraints are met for standard congestion measures, such as the percentage of calls of each type that abandon before starting service and the percentage of answered calls of each type that are delayed more than a specified number of seconds. We propose going beyond traditional congestion measures to focus on the expected value derived from having particular agents handle various calls. Expected value might represent expected revenue or the likelihood of first‐call resolution. Value might also reflect agent call‐handling preferences. We show how value‐based routing (VBR) and preference‐based routing (PBR) can be introduced in the context of an existing SBR framework, based on static‐priority routing using a highly‐structured priority matrix, so that constraints are still met for standard congestion measures. Since an existing SBR framework is used to implement VBR and PBR, it is not necessary to replace the automatic call distributor (ACD). We show how mathematical programming can be used, with established staffing requirements, to find a desirable priority matrix. We select the priority matrix to use during a specified time interval (e.g., 30‐minute period) by maximizing the total expected value over that time interval, subject to staffing constraints.

We analyze the inventory decisions of a manufacturer who has ample production capacity and also uses returned products to satisfy customer demand. All returned items go through an evaluation process, at the end of which the decision of disposal, direct reselling, or rework is made for each unit according to a predetermined procedure. We quantify the value of information/visibility on the reverse channel for the manufacturer by making comparisons among three approaches: No information‐naive; no visibility‐enlightened; and full visibility. We find the value of visibility increases with the comparative length of the reverse channel and volume, volatility, and usability of returns. Furthermore, the smarter the manufacturer, the less benefit visibility brings to the system. By this analysis, we quantify the visibility savings of radio frequency identification (RFID) in the reverse channel as a candidate enabler technology. We also provide numerical examples to show that practical approximations in inventory management may have acceptable penalties to the manufacturer with visibility.

Analyzing the proliferation of item‐level RFID, recent studies have identified the cost sharing of the technology as a gating issue. Various qualitative studies have predicted that conflict will arise, in particular in decentralized supply chains, from the fact that the benefits and the costs resulting from item‐level RFID are not symmetrically distributed among supply chain partners. To contribute to a better understanding of this situation, we consider a supply chain with one manufacturer and one retailer. Within the context of this retail supply chain, we present analytic models of the benefits of item‐level RFID to both supply chain partners. We examine both the case of a dominant manufacturer as well as the case of a dominant retailer, and we analyze the results of an introduction of item‐level RFID to such a supply chain depending on these market power characteristics. Under each scenario, we show how the cost of item‐level RFID should be allocated among supply chain partners such that supply chain profit is optimized.

We review queueing‐theory methods for setting staffing requirements in service systems where customer demand varies in a predictable pattern over the day. Analyzing these systems is not straightforward, because standard queueing theory focuses on the long‐run steady‐state behavior of stationary models. We show how to adapt stationary queueing models for use in nonstationary environments so that time‐dependent performance is captured and staffing requirements can be set. Relatively little modification of straightforward stationary analysis applies in systems where service times are short and the targeted quality of service is high. When service times are moderate and the targeted quality of service is still high, time‐lag refinements can improve traditional stationary independent period‐by‐period and peak‐hour approximations. Time‐varying infinite‐server models help develop refinements, because closed‐form expressions exist for their time‐dependent behavior. More difficult cases with very long service times and other complicated features, such as end‐of‐day effects, can often be treated by a modified‐offered‐load approximation, which is based on an associated infinite‐server model. Numerical algorithms and deterministic fluid models are useful when the system is overloaded for an extensive period of time. Our discussion focuses on telephone call centers, but applications to police patrol, banking, and hospital emergency rooms are also mentioned.

We characterize the trade‐offs among firms' compliance strategies in a market‐based program where a regulator interested in controlling emissions from a given set of sources auctions off a fixed number of emissions permits. We model a three‐stage game in which firms invest in emissions abatement, participate in a share auction for permits, and produce output. We develop a methodology for a profit‐maximizing firm to derive its marginal value function for permits and translate this value function into an optimal bidding strategy in the auction. We analyze two end‐product market scenarios independent demands and Cournot competition. In both scenarios we find that changing the number of available permits influences abatement to a lesser extent in a dirty industry than in a cleaner one. In addition, abatement levels taper off with increasing industry dirtiness levels. In the presence of competition, firms in a relatively clean industry can, in fact, benefit from a reduction in the number of available permits. Our findings are robust to changes in certain modeling assumptions.