Quantity Flexibility Contracts: Optimal Decisions with Information Updates*

We study single and multiperiod quantity flexibility contracts involving one demand forecast update in each period and a spot market. We obtain the optimal order quantity at the beginning of a period and order quantities on contract and from the spot market at the then prevailing price after the forecast revision and before the demand materialization. The amount that can be purchased on contract is bounded by a given flexibility limit. We discuss the impact of the forecast quality and the level of flexibility on the optimal decisions and managerial insights behind the results.     

Quantity Flexibility Contract in the Presence of Discount Incentive

We design a new contract, which we refer to as the QFi contract, that combines the quantity flexibility (QF) mechanism and the price‐only discount incentive. Under the QF contract, the buyer does not assume full responsibility for the forecast, yet the supplier guarantees the availability of the forecasted quantity and extra buffer inventory. In contrast, the price‐only discount contract places full inventory burden on the buyer. We show that the proposed QFi contract effectively balances the inventory risk for both the buyer and the supplier considering both the QF and discount mechanisms. We also show that the QFi contract is able to achieve supply chain coordination. More importantly, the QFi contract's coordinating price scheme does not require knowledge of demand distribution. We identify areas where the buyer and supplier may both benefit from implementing the QFi contract as opposed to the extant QF or price‐only (wholesale) discount contractual decisions in a decentralized supply chain. We also specify the conditions under which supply chain coordination can be achieved in a win‐win manner. We conclude with managerial implications and provide directions for future research.     

A Game-Theoretic Approach to Quantity Discount Problems*

The traditional quantity discount problem is analyzed from the perspective of game theory, including both noncooperative and cooperative models. For the noncooperative case, the Stackelberg equilibrium is derived. For the cooperative case, the Pareto Optimality criteria are used to find a group of optimal strategies. Both scenarios are illustrated through an example which quantifies the benefits resulting from cooperation between the buyer and the seller for game-theoretic solutions using geometric programming.     

A Newsvendor Problem with Revenue Sharing and Channel Coordination*

Increasingly manufacturers and retailers are implementing revenue‐sharing policies to coordinate distribution channels more effectively. This article considers a standard newsvendor problem in a single manufacturer–retailer channel and compares the expected profits that each party receives in a traditional ordering environment with those that can be achieved under a revenue‐sharing policy designed to completely eliminate double marginalization. It is shown that the retailer always benefits from the transition to revenue sharing by capturing a portion of the incremental channel profit generated by the complete elimination of double marginalization. A necessary demand‐distribution‐dependent condition is derived under which the transition to revenue sharing benefits the manufacturer as well. The findings of this research are illustrated in a numerical example for the uniform demand distribution.     

Discount Pricing Policies and the Coordination of Decentralized Distribution Systems*

A challenge of supply chain management is to align the objectives, and hence coordinate the activities, of independent supply chain members. In this study, we approach this problem in a simple way by extending traditional quantity discounts that are based solely on buyers' individual order size to discount policies that are based on both buyers' individual order size and their annual volume. We show that discount policies are able to achieve nearly optimal system profit and, hence, provide effective coordination, for a decentralized two‐echelon distribution system, whereby a supplier sells a product to a group of heterogeneous and independent retailers each facing a downward‐sloping demand curve of its retail price. When buyers are heterogeneous, a critical issue of coordination is to motivate different customers to increase their demand and lot size according to their potential so as to improve profits. We show that market heterogeneity presents an effective discriminating factor for the supplier to segment customers in the design of a coordination mechanism.     

Flow Coordination and Information Sharing in Supply Chains: Review,Implications, and Directions for Future Research

Advances in information technology, particularly in the e‐business arena, are enabling firms to rethink their supply chain strategies and explore new avenues for inter‐organizational cooperation. However, an incomplete understanding of the value of information sharing and physical flow coordination hinder these efforts. This research attempts to help fill these gaps by surveying prior research in the area, categorized in terms of information sharing and flow coordination. We conclude by highlighting gaps in the current body of knowledge and identifying promising areas for future research.     

Channel Coordination for a Supply Chain with a Risk‐Neutral Manufacturer and a Loss‐Averse Retailer*

This articles considers a decentralized supply chain in which a single manufacturer is selling a perishable product to a single retailer facing uncertain demand. It differs from traditional supply chain contract models in two ways. First, while traditional supply chain models are based on risk neutrality, this article takes the viewpoint of behavioral principal–agency theory and assumes the manufacturer is risk neutral and the retailer is loss averse. Second, while gain/loss (GL) sharing is common in practice, there is a lack of analysis of GL‐sharing contracts in the supply chain contract literature. This article investigates the role of a GL‐sharing provision for mitigating the loss‐aversion effect, which drives down the retailer order quantity and total supply chain profit. We analyze contracts that include GL‐sharing‐and‐buyback (GLB) credit provisions as well as the special cases of GL contracts and buyback contracts. Our analytical and numerical results lend insight into how a manufacturer can design a contract to improve total supply chain, manufacturer, and retailer performance. In particular, we show that there exists a special class of distribution‐free GLB contracts that can coordinate the supply chain and arbitrarily allocate the expected supply chain profit between the manufacturer and retailer; in contrast with other contracts, the parameter values for contracts in this class do not depend on the probability distribution of market demand. This feature is meaningful in practice because (i) the probability distribution of demand faced by a retailer is typically unknown by the manufacturer and (ii) a manufacturer can offer the same contract to multiple noncompeting retailers that differ by demand distribution and still coordinate the supply chains.     

Asymmetric Forecast Information and the Value of Demand Observation in Repeated Procurement

In many supply chain relationships that continue over multiple periods, information about the hidden properties of the supply chain partners can be revealed throughout the course of the relationship. This article examines how the availability of such information affects the contracting scheme between a supplier and his manufacturer in a relationship that persists over several selling seasons. At the beginning of the first selling season, the manufacturer observes private information about the demand distribution, whereas the supplier who is less familiar with the market is endowed only with a prior belief about the market condition. When the supplier cannot observe the demand realization during the first selling period, she offers a contract that induces the manufacturer to reveal the market condition in the first selling season. However, the opportunity for the supplier to observe demand realization can result in the supplier offering the manufacturer a simple contract that does not induce the manufacturer to reveal his private information during the first selling season. In the latter case, the supplier observes the demand realization and designs the second period contract based on this information. We show that when the supplier chooses to offer such a contract, the manufacturer becomes worse‐off, and it has an ambiguous effect on the performance of the supply chain.     

A Purchasing Framework for B2B Pricing Decisions and Risk‐sharing in Supply Chains*

This paper presents a common modelling structure for (i) the implementation of operational policies by individual purchasing managers of risk‐sharing agreements among supply‐chain partners, and (ii) the integration of brick and click purchasing policies in a B2B. The problem of price uncertainty created within these two environments is modelled as a stochastic repetitive‐sales problem, applicable to any probability distribution. The model identifies sufficient conditions for regenerative ordering cycles, which allows for the use of the renewal reward theorem. The end result is a two‐price purchasing policy, which may substantially ease implementation problems across a global corporation's purchasing managers world‐wide and across B2B markets.     

On Extending Russell and Krajewski's Algorithm for Economic Purchase Order Quantities*

Russell and Krajewski presented an optimal purchase order quantity algorithm that considered the effect of the transportation rate structure for less-than-truckload (LTL) shipments. The authors applied the Russell and Krajewski algorithm to a variety of freight classes and lengths-of-haul. Anomalous cases were found in which the freight rate schedule, when used with the suggested algorithm, resulted in incorrect order size decisions. In this comment, the authors consider the impact of these anomalies on the optimal order quantity and associated total costs. A procedure is presented to adjust the Russell and Krajewski algorithm to arrive at the optimal purchase order quantity and the lowest total annual cost.     

An Efficient Heuristic Procedure for Practical-Sized Capacitated Warehouse Design and Management*

The purpose of this paper is to refine and test a distribution system design model and evaluate its performance with respect to solution quality, model validation, and algorithm performance. The paper reports on the development of a multiproduct warehouse logistics model. The logistics model is used to predict the performance of distribution companies that typically have to deal with product costs, the cost to distribute the products from a set of warehouses to customer outlets, the level of service provided to its customers, and flexibility for each product group and market segment.     

Determining Optimal Selling Price and Lot Size When the Supplier Offers All-Unit Quantity Discounts*

This paper is concerned with finding the optimal price and lot size for a retailer purchasing a product for which the supplier offers all-unit quantity discounts. Demand for the product is assumed to be a decreasing function of price, and a procedure is developed for finding the optimal price and lot size for a class of demand functions. The procedure then is applied to two common demand functions: (1) the constant price-elasticity function, and (2) the linear demand function.     

A Note on Determining Optimal Selling Price and Lot Size Under All-Unit Quantity Discounts

A recent study conducted by Abad [1] described a method of determining the optimal price and lot size when the supplier offers all-unit quantity discounts. The author developed a procedure using centralized and decentralized approaches. According to the author's suggestion, further study is necessary to determine if the decentralized approach always provides an optimal solution. In the present study, an attempt is made to investigate the uniformity between the centralized and decentralized approaches and to verify whether the decentralized approach always yields optimal solutions.     

Special Research Focus on Supply Chain Linkages: Challenges for Design and Management in the 21st Century*

Supply chain management is a central and important area for academic research due to its impact on firms competing in today's global economy. Managing the flow of materials from supply sources to the ultimate customer represents a major challenge for today's managers. To assist managers, the concept of supply chain management has been adopted by many business leaders as an important way to assist in designing, planning, and controlling the network of facilities and tasks that comprise the many stages of the supply chain. In turn, the flow of academic research in the area has increased to provide a better set of guidelines for effective implementation and execution. This article sets the stage for recently completed research concentrating on supply chain management issues. First, a definition of supply chain management is provided and compared to recent usage in this area and logistics management. Also, a framework is provided that structures this dynamic and complex management task. Second, a review of past research is presented to illustrate the many paths supply chain management has traveled, and important contributions to supply management understanding and decision making. Third, recently completed research articles are introduced that have been selected to be part of this special issue of Decision Sciences. And fourth, future research directions for supply chain management that need to be pursued by interested investigators are discussed.     

A Profit‐Maximizing Approach to Disposition Decisions for Product Returns*

Motivated by the asset recovery process at IBM, we analyze the optimal disposition decision for product returns in electronic products industries. Returns may be either remanufactured for reselling or dismantled for spare parts. Reselling a remanufactured unit typically yields higher unit margins. However, demand is uncertain. A common policy in many firms is to rank disposition alternatives by unit margins. We propose a profit‐maximization approach that considers demand uncertainty. We develop single period and multiperiod stochastic optimization models for the disposition problem. Analyzing these models, we show that the optimal allocation balances expected marginal profits across the disposition alternatives. A detailed numerical study reveals that our approach to the disposition problem outperforms the current practice of focusing exclusively on high‐margin options, and we identify conditions under which this improvement is the highest. In addition, we show that a simple myopic heuristic in the multiperiod problem performs well.     

A Framework for Examining the Interface between Operations and Information Systems: Implications for Research in the New Millennium*

Even though much research has been published in operations and information systems, both functional areas find their roots in other disciplines. While operations management evolved from operations research in the 1960s, the field of information systems is of more recent vintage and traces its original roots to computer science. Both disciplines now naturally have come closer together as information and process-technology-based changes force manufacturing firms to become more efficient and customer focused. Market and technology-driven e-commerce initiatives that are likely to dominate business strategies in the future cannot be successfully achieved without a successful integration of operations and information systems. In this paper, we present a unifying framework that can be used to better understand the management of the functional interface between operations and information systems. We also categorize and highlight the contributions of the articles that appear in this special research focus issue. Finally, research directions that emerge from our understanding of this interface are outlined in an effort to stimulate further thinking and research that can advance our knowledge of this interface area.     

The Effect of Lead Time Uncertainty on Safety Stocks

The pressure to reduce inventory investments in supply chains has increased as competition expands and product variety grows. Managers are looking for areas they can improve to reduce inventories without hurting the level of service provided. Two areas that managers focus on are the reduction of the replenishment lead time from suppliers and the variability of this lead time. The normal approximation of lead time demand distribution indicates that both actions reduce inventories for cycle service levels above 50%. The normal approximation also indicates that reducing lead time variability tends to have a greater impact than reducing lead times, especially when lead time variability is large. We build on the work of Eppen and Martin (1988) to show that the conclusions from the normal approximation are flawed, especially in the range of service levels where most companies operate. We show the existence of a service‐level threshold greater than 50% below which reorder points increase with a decrease in lead time variability. Thus, for a firm operating just below this threshold, reducing lead times decreases reorder points, whereas reducing lead time variability increases reorder points. For firms operating at these service levels, decreasing lead time is the right lever if they want to cut inventories, not reducing lead time variability.     

e‐Business and Supply Chain Management

In recent years, the area of Supply Chain Management has generated a substantial amount of interest both by managers and researchers. This interest has also been fueled by the growth in the development and application of e‐business technologies. These technologies enable the supply chain manager to make coordinated decisions by integrating the diverse and sometimes conflicting objectives of the various trading partners in a chain. The purpose of this paper is to: (a) highlight strategic and tactical issues for analyzing supply chains in an e‐business setting based on papers published in this special issue; and (b) describe future research opportunities in this emerging interdisciplinary area.     

Simulation Analysis of a Manufacturing Supply Chain*

A supply chain is a series of manufacturing plants that transform raw material into finished product. A pipeline within a supply chain refers to the stream of information, material, components, and assemblies that are associated with a particular product. It is typical for manufacturing plants to put considerable effort to optimize the performance of a horizontal slice of a supply chain (such as coordination among parts that share a common resource). The need to optimize the performance of the vertical slice (the supply chain connecting raw material to finished product) by controlling the transmission of schedule instability and the resulting inventory fluctuation is often overlooked. A schedule is stable if actual production requirements for a given period do not change from the forecast production requirements. Stable production schedules are important when managing supply chains as they help control inventory fluctuation and inventory accumulation. Failure to control schedule instability results in high average inventory levels in the system. In this paper a simulation analysis of supply chain instability and inventory is conducted, and it is shown how supply chains can be analyzed for continuous improvement opportunities using simulation. The focus is on a stamping pipeline in an automobile supply chain based on operating data from General Motors (GM). It is shown that the techniques used in this paper are a useful tool for supply chain analysis.