We address the simultaneous determination of pricing, production, and capacity investment decisions by a monopolistic firm in a multi‐period setting under demand uncertainty. We analyze the optimal decision with particular emphasis on the relationship between price and capacity. We consider models that allow for either bi‐directional price changes or models with markdowns only, and in the latter case we prove that capacity and price are strategic substitutes.
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 study zero‐inventory production‐distribution systems under pool‐point delivery. The zero‐inventory production and distribution paradigm is supported in a variety of industries in which a product cannot be inventoried because of its short shelf life. The advantages of pool‐point (or hub‐and‐spoke) distribution, explored extensively in the literature, include the efficient use of transportation resources and effective day‐to‐day management of operations. The setting of our analysis is as follows: A production facility (plant) with a finite production rate distributes its single product, which cannot be inventoried, to several pool points. Each pool point may require multiple truckloads to satisfy its customers' demand. A third‐party logistics provider then transports the product to individual customers surrounding each pool point. The production rate can be increased up to a certain limit by incurring additional cost. The delivery of the product is done by identical trucks, each having limited capacity and non‐negligible traveling time between the plant and the pool points. Our objective is to coordinate the production and transportation operations so that the total cost of production and distribution is minimized, while respecting the product lifetime and the delivery capacity constraints. This study attempts to develop intuition into zero‐inventory production‐distribution systems under pool‐point delivery by considering several variants of the above setting. These include multiple trucks, a modifiable production rate, and alternative objectives. Using a combination of theoretical analysis and computational experiments, we gain insights into optimizing the total cost of a production‐delivery plan by understanding the trade‐off between production and transportation.
In this paper, we analyze the impact of two forms of commonly used threshold‐based incentive schemes on the observed sales variability. The first form of the incentive comprises an additional marginal payment on crossing a specified sales threshold and the second form of the incentive scheme comprises a lumpsum bonus payment on crossing the predetermined sales threshold. We model the effect of such incentives under two specific scenarios: an exclusive dealership selling a single product and a non‐exclusive dealer selling two competing products. For an exclusive dealer, we show that a bonus contract not only increases the expected sales, but, more importantly, decreases the sales (order) variance. Consequently, the bonus‐based scheme allows the manufacturer to regulate sales variance better. With a non‐exclusive dealer, the sales variance increases substantially with an additional marginal payment contract. However, our analysis suggests that the bonus contract continues to perform better in this case, too, if the threshold level is set appropriately using the underlying demand distribution.
Transshipment, the sharing of inventory among parties at the same echelon level of a supply chain, can be used to reduce costs. The effectiveness of transshipment is in part determined by the configuration of the transshipment network. We introduce chain configurations in transshipment settings, where every party is linked in one connected loop. Under simplifying assumptions we show analytically that the chain configuration is superior to configurations suggested in the literature. In addition, we demonstrate the efficiency and robustness of chain configurations for more general scenarios and provide managerial insights regarding preferred configurations for different problem parameters.
An abundance of flawed software has been identified as the main cause of the poor security of computer networks because major viruses and worms exploit the vulnerabilities of such software. As an incentive mechanism for software security quality improvement, software liability has been intensely discussed among both academics and practitioners for a long time. An alternative approach to managing software security is patch release, which has been widely adopted in practice. In this paper, we examine these two different ways of mitigating customer risk in the software market: liability and patch release. We study the impact of both mechanisms on a monopolistic software vendor's decision on security quality. We find the conditions under which each mechanism is effective in terms of improving security quality and increasing social surplus. The heterogeneous nature of loss is identified to be a key factor for the effectiveness of the liability mechanism. On the other hand, patch release can be effective and welfare‐enhancing regardless of the nature of loss as long as customers incur low patching cost, and/or the vendor incurs low patch development cost. We also examine the impact of customer misperception of the outcome from vulnerable software on the effectiveness of liability.
Information‐intensive services (IIS), such as financial services, business services, health care, and education, form a large and growing part of the service sector in the US economy. In this paper we present a classification of IIS based on their operational characteristics. We also propose empirically grounded conceptual analysis and prescriptive frameworks useful for the improvement of certain types of IIS. By conducting statistical analyses of a large sample of claims data from one of the largest property and casualty companies in the United States, we isolate key drivers of service performance and identify preemptive actions that can favorably impact performance metrics. Those results demonstrate the direct operationalization of the proposed frameworks with primary data. Our conceptual analysis, empirical findings, and the prescriptive framework that follow, provide an action plan that can lead to a systemic improvement in the performance of information and customer contact intensive services.
In their recent paper, Nagarajan and Sošić study an assembly supply chain in which n suppliers sell complementary components to a downstream assembler, who faces a price‐sensitive deterministic demand. Suppliers may form alliances, and each alliance then sells a kit of components to the assembler and determines the price for that kit. The assembler buys the components (kits) from the alliances and sets the selling price of the product. Nagarajan and Sošić consider three modes of competition—supplier Stackelberg, vertical Nash (VN), and assembler Stackelberg models—which correspond to different power structures in the market, and study stable supplier alliances when the assembler faces linear and isoelastic demand. In this paper, we study the impact that demand uncertainty has on stability results obtained in Nagarajan and Sošić. We first analyze models in which all decisions are made before the uncertainty is resolved, and show that the alliance of all suppliers remains stable when demand is isoelastic, or under Stackelberg models when demand is linear. However, demand uncertainty may change stability results when both parties make decisions simultaneously (VN model) and demand is linear. We then extend our results by considering scenarios in which some decisions may be postponed and made after the actual demand is known. When the ordering quantity can be determined after observing the true demand, we show that stable outcomes correspond to those obtained in the deterministic case and uncertainty has no impact on coalition stability; if only the assembler's pricing decision is postponed, we need additional conditions for stability results to carry over in the additive demand model.
Trade regulations are an important driver of supply chain strategy in many industries. For example, the textile, paper, chemical, and steel industries grapple with significant levels of non‐tariff barriers (NTBs) such as safeguard controls and countervailing duties. We explore three often observed supply chain strategies in industries subject to NTBs; direct procurement, split procurement, and outward processing arrangements (OPAs). We characterize the optimal procurement quantities for each of these three strategies, and examine how industry and country characteristics influence the firm's strategy preference. For example, we establish that the direct and split strategy profits increase in the NTB price variance but decrease in the mean price. These effects are sufficiently large that NTB price characteristics can dictate which supply chain strategy is preferred. Both the cost disadvantage and lead‐time advantage of domestic production are also significant influencers of the preferred strategy, as is the domestic‐country mandated production constraint associated with the OPA strategy.
In the delivery of health care services, variability in the patient arrival and service processes can cause excessive patient waiting times and poor utilization of facility resources. Based on data collected at a large primary care facility, this paper investigates how several sources of variability affect facility performance. These sources include ancillary tasks performed by the physician, patient punctuality, unscheduled visits to the facility's laboratory or X‐ray services, momentary interruptions of a patient's examination, and examination time variation by patient class. Our results indicate that unscheduled visits to the facility's laboratory or X‐ray services have the largest impact on a physician's idle time. The average patient wait is most affected by how the physician prioritizes completing ancillary tasks, such as telephone calls, relative to examining patients. We also investigate the improvement in system performance offered by using increasing levels of patient information when creating the appointment schedule. We find that the use of policies that sequence patients based on their classification improves system performance by up to 25.5%.