Knowledge that Transforms

Aggregation of corporate social performance (CSP) metrics poses a major challenge to researchers and practitioners. This study provides a critical evaluation of current aggregation approaches and proposes a new methodology based on data envelopment analysis (DEA) to compute a CSP index. DEA is independent of subjective weight specifications and provides an efficiency index to benchmark the CSP of firms. Using CSP data from 2190 firms in three major industries from the Kinder, Lydenberg, and Domini, Inc. database in 2007, our study presents the first application of the DEA model for CSP and ordinal data and opens up a new path for future empirical CSP research.

We study a supply chain of a supplier selling via a wholesale price contract to a financially constrained retailer who faces stochastic demand. The retailer might need to borrow money from a bank to execute his order. The bank offers a fairly priced loan for relevant risks. Failure of loan repayment leads to a costly bankruptcy (fixed administrative costs, costs proportional to sales, and a depressed collateral value). We identify the retailer's optimal order quantity as a function of the wholesale price and his total wealth (working capital and collateral). The analysis of the supplier's optimal wholesale price problem as a Stackelberg game, with the supplier the leader and the retailer the follower, leads to unique equilibrium solutions in wholesale price and order quantity, with the equilibrium order quantity smaller than the traditional newsvendor one. Furthermore, in the presence of the retailer's bankruptcy risks, increases in the retailer's wealth lead to increased supplier's wholesale prices, but without the retailer's bankruptcy risks the supplier's wholesale prices stay the same or decrease in retailer's wealth.

ABC inventory classifications are widely used in practice, with demand value and demand volume as the most common ranking criteria. The standard approach in ABC applications is to set the same service level for all stock keeping units (SKUs) in a class. In this paper, we show (for three large real life datasets) that the application of both demand value and demand volume as ABC ranking criteria, with fixed service levels per class, leads to solutions that are far from cost optimal. An alternative criterion proposed by Zhang et al. performs much better, but is still considerably outperformed by a new criterion proposed in this paper. The new criterion is also more general in that it can take criticality of SKUs into account. Managerial insights are obtained into what class should have the highest/lowest service level, a topic that has been disputed in the literature.

We consider a supply chain in which a distributor procures from a producer a quantity of a fresh product, which has to undergo a long‐distance transportation to reach the target market. During the transportation process, the distributor has to make an appropriate effort to preserve the freshness of the product, and his success in this respect impacts on both the quality and quantity of the product delivered to the market. The distributor has to determine his order quantity, level of freshness‐keeping effort, and selling price, by taking into account the wholesale price of the producer, the cost of the freshness‐keeping effort, the likely spoilage of the product during transportation, and the possible demand for the product in the market. The producer, on the other hand, has to determine the wholesale price based on its effect on the order quantity of the distributor. We develop a model to study this problem, and characterize each party's optimal decisions in both decentralized and centralized systems. We further develop an incentive scheme to facilitate coordination between the two parties. Computational results are reported to show the effects of freshness‐keeping efforts.

We examine the use of consumer cash mail‐in rebates offered by a manufacturer in a Stackelberg game where the manufacturer is the leader and the retailer is the follower. Our analysis indicates that rebates are profitable for manufacturers if consumers are inconsistent in the sense that their rebate valuation when they make purchase decisions is independent of their redemption probabilities when they make redemption decisions. If the manufacturer keeps the wholesale price unchanged, then the rebate increases the retailer's profit by a larger amount than the increase in the manufacturer's profit. If the manufacturer jointly optimizes the wholesale price and rebate, then the increase in the manufacturer's profit is twice the increase in the retailer's profit. The retailer responds to rebates by increasing the retail price, which increases the margin paid by consumers who do not redeem the rebate. On average, consumer surplus decreases when it is optimal for manufacturers to offer rebates. We suggest incentive schemes that make it worthwhile for retailers to limit the price increase. In these incentive schemes, the manufacturer imposes a negative relationship between the rebate value and the retail price. We show that such incentives increase supply chain profits.

Advance selling through pre‐orders is a strategy to transfer inventory risk from a retailer to consumers. A newsvendor retailer can have three strategies to choose from: no advance selling allowed (NAS), moderate advance selling with a moderate discount for pre‐orders (MAS), and deep advance selling with a deep discount for pre‐orders (DAS). This research studies how a retailer could design an advance selling strategy to maximize her own profits. We find some interesting results. For example, there exist two thresholds for the selling season profit margin and two thresholds for consumer's expected valuation. For products with higher profit margin than the high threshold on profit margin, a retailer should always use DAS. For products with medium profit margin within the two thresholds, a retailer should adopt MAS if consumer's expected valuation is lower than the high threshold and use DAS otherwise. For products with lower profit margin than the low threshold, a retailer should use NAS, DAS, or MAS, respectively, if consumer's expected valuation is lower than the low threshold, higher than the high threshold, or between the two thresholds, respectively. Through sensitivity analyses, we also show the effects of multiple consumer characteristics on a retailer's optimal advance selling strategy.

This paper examines the choice of supply chain structure for a proprietary component manufacturer (PCM). The PCM, who is the sole supply source of a critical component used to assemble an end product, can either provide its component to an original equipment manufacturer (OEM) in the end‐product market (component supplier structure), develop the end product exclusively under its own brand (monopoly structure), or provide the component to the OEM as well as develop the end product under its own brand (dual distributor structure). Typically, the end products of the PCM and the OEM will be differentiated, and the OEM tends to have a capability advantage (compared with the PCM) in producing the end product. Our paper studies the impact of this degree of differentiation and capability advantage on the optimal choice of distribution structure. We then investigate how investing in component branding, enhancing the value of the end product, using alternative supply contracts, and product valuation uncertainty influence the PCM's optimal choice of distribution structure.

This paper considers two parallel supply chains with interacting demand streams. Each supply chain consists of one supplier and one retailer. The two demand streams are jointly described with a vector autoregressive time‐series process in which they interact and their respective innovation errors correlate contemporaneously. For each supply chain, we develop insights into when and how much the supplier and the retailer can improve on their forecasting accuracy if the external demand history of the other supply chain is utilized. When this external demand history is not available or made available after a time lag, we develop a partial process and a delayed process to characterize the demand structure that the retailer can recover from the available demand histories. Our results show that the external demand history of the other supply chain always helps the retailer make better forecasts when demand streams interact; however, the enhanced information alters the retailer's order process, which may produce larger forecasting errors for the supplier. Conditions are established for the supplier to benefit from the external demand history of the other supply chain.