Hau L. Lee

Consider a series of companies in a supply chain, each of whom orders from its immediate upstream member. In this setting, inbound orders from a downstream member serve as a valuable informational input to upstream production and inventory decisions. This paper claims that the information transferred in the form of “orders” tends to be distorted and can misguide upstream members in their inventory and production decisions. In particular, the variance of orders may be larger than that of sales, and the distortion tends to increase as one moves upstream—a phenomenon termed “bullwhip effect.” This paper analyzes four sources of the bullwhip effect: demand signal processing, rationing game, order batching, and price variations. Actions that can be taken to mitigate the detrimental impact of this distortion are also discussed.

Many companies have embarked on initiatives that enable more demand information sharing between retailers and their upstream suppliers. While the literature on such initiatives in the business press is proliferating, it is not clear how one can quantify the benefits of these initiatives and how one can identify the drivers of the magnitudes of these benefits. Using analytical models, this paper aims at addressing these questions for a simple two-level supply chain with nonstationary end demands. Our analysis suggests that the value of demand information sharing can be quite high, especially when demands are significantly correlated over time.

Tremendous variability in orders along the supply chain can plague companies trying to eliminate excess inventory, forecast product demand, and simply make their supply chain more efficient. What causes the bullwhip effect that distorts information as it is transmitted up the chain? The authors identify four major causes: 1. Demand forecast updating. As each entity along the chain places an order, it replenishes stock and includes some safety stock. With long lead times, there may be weeks of safety stocks, which make the fluctuation in demand more significant. 2. Order batching. Companies may place orders in batches, often to avoid the cost of processing orders more frequently or the high transportation costs for less-than-truckload orders. Suppliers, in turn, face erratic streams of orders, and the bullwhip effect occurs. When order cycles overlap, the effect is even more pronounced. 3. Price fluctuation. Special promotions and price discounts result in customers buying in large quantities and stocking up. When prices return to normal, customers stop buying. As a result, their buying pattern does not reflect their consumption pattern. 4. Rationing and shortage gaming. If product demand exceeds supply, a manufacturer may ration its products. Customers, in turn, may exaggerate their orders to counteract the rationing. Eventually, orders will disappear and cancellations pour in, making it impossible for the manufacturer to determine the real demand for its product. The authors suggest several ways in which companies can counteract the bullwhip effect: 1. Avoid multiple demand forecast updates. Companies can make demand data from downstream available upstream. Or they can bypass the downstream site by selling directly to the consumer. Also, they can improve operational efficiency to reduce highly variable demand and long resupply lead times. 2. Break order batches. Companies can use electronic data interchange to reduce the cost of placing orders and place orders more frequently. And they can ship assortments of products in a truckload to counter high transportation costs or use third-party logistics companies to handle shipping. 3. Stabilize prices. Manufacturers can reduce the frequency and level of wholesale price discounting to prevent customers from stockpiling. They can also use activity-based costing systems so they can recognize when companies are buying in bulk. 4. Eliminate gaming in shortage situations. In shortages, suppliers can allocate product based on past sales records, rather than on orders, so customers don't exaggerate their orders. They can also eliminate their generous return policies, so retailers are less likely to cancel orders. Only by thoroughly understanding the underlying causes of the bullwhip effect, say the authors, can companies counteract and control it.

Today's marketplace is characterised by turbulence and uncertainty. Market turbulence has tended to increase for a number of reasons. Demand in almost every industrial sector seems to be more volatile than was the case in the past. Product and technology life‐cycles have shortened significantly and competitive product introductions make life‐cycle demand difficult to predict. At the same time the vulnerability of supply chains to disturbance or disruption has increased. It is not only the effect of external events such as wars, strikes or terrorist attacks, but also the impact of changes in business strategy. Many companies have experienced a change in their supply chain risk profile as a result of changes in their business models, for example the adoption of “lean” practices, the move to outsourcing and a general tendency to reduce the size of the supplier base. This paper suggests that one key element in any strategy designed to mitigate supply chain risk is improved “end‐to‐end” visibility. It is argued that supply chain “confidence” will increase in proportion to the quality of supply chain information.

Abstract In this paper, we study the effects of an imperfect production process on the optimal production cycle time. The system is assumed to deteriorate during the production process and produce some proportion of defective items. The optimal production cycle is derived, and is shown to be shorter than that of the classical Economic Manufacturing Quantity model. The analysis is extended to the case where the defective rate is a function of the set-up cost, for which the set-up cost level and the production cycle time are jointly optimized. Finally, we also consider the case where the deterioration process is dynamic in its nature, i.e., the proportion of defective items is not constant. Both linear, exponential, and multi-state deteriorating processes are studied. Numerical examples are provided to illustrate the derivation of the optimal production cycle time in these situations.

This paper reviews the literature on quantitatively-oriented approaches for determining lot sizes when production or procurement yields are random. We discuss issues related to the modeling of costs, yield uncertainty, and performance in the context of systems with random yields. We provide a review of the existing literature, concentrating on descriptions of the types of problems that have been solved and important structural results. We identify a variety of shortcomings of the literature in addressing problems encountered in practice, and suggest directions for future research.

A supply chain is a network of facilities that performs the functions of procurement of material, transformation of material to intermediate and finished products, and distribution of finished products to customers. Often, organizational barriers between these facilities exist, and information flows can be restricted such that complete centralized control of material flows in a supply chain may not be feasible or desirable. Consequently, most companies use decentralized control in managing the different facilities at a supply chain. In this paper, we describe what manufacturing managers at Hewlett-Packard Company (HP) see as the needs for model support in managing material flows in their supply chains. These needs motivate our initial development of such a model for supply chains that are not under complete centralized control. We report on our experiences of applying such a model in a new product development project of the DeskJet printer supply chain at HP. Finally, we discuss avenues to develop better models, as well as to fully exploit the power of such models in application.

Advances in information system technology have had a huge impact on the evolution of supply chain management. As a result of such technological advances, supply chain partners can now work in tight coordination to optimise the chain-wide performance, and the realised return may be shared among the partners. A basic enabler for tight coordination is information sharing, which has been greatly facilitated by the advances in information technology. This paper describes the types of information shared inventory, sales, demand forecast, order status, and production schedule. We discuss how and why this information is shared using industry examples and relating them to academic research. We also discuss three alternative system models of information sharing - the information transfer model, the third party model and the information hub model.

This paper presents a comprehensive model framework for linking decisions and performance throughout the material-production-distribution supply chain. The purpose of the model is to support analysis of alternative manufacturing material/service strategies. A series of linked, approximate submodels and an heuristic optimization procedure are introduced. A prototype software implementation is also discussed.

Expanding product variety and high customer service provision are both major challenges for manufacturers to compete in the global market. In addition to many ongoing programs, such as lead-time reduction, redesigning products and processes so as to delay the point of product differentiation is becoming an emerging means to address these challenges. Such a strategy calls for redesigning products and processes so that the stages of the production process in which a common process is used are prolonged. This product/process redesign will defer the point of differentiation (i.e., defer the stage after which the products assume their unique identities). In this paper, we develop a simple model that captures the costs and benefits associated with this redesign strategy. We apply this simple model to analyze some special cases that are motivated by real examples. These special cases enable us to formalize three different product/process redesign approaches (standardization, modular design, and process restructuring) for delaying product differentiation that some companies are beginning to pursue. Finally, we analyze some special theoretical cases that enable us to characterize the optimal point of product differentiation and derive managerial insights.

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RFID (Radio‐Frequency Identification) technology has shown itself to be a promising technology to track movements of goods in a supply chain. As such, it can give unprecedented visibility to the supply chain. Such visibility can save labor cost, improve supply chain coordination, reduce inventory and increase product availability. Industry reports and white papers are now filled with estimates and proclamations of the benefits and quantified values of RFID. Early adopters are now rallying more and more followers. However, most such claims are educated guesses at best and are not substantiated, that is, they are not based on detailed, model‐based analysis. This paper argues that there is a huge credibility gap of the value of RFID, and that a void exists in showing how the proclaimed values are arrived at, and how those values can be realized. The paper shows that this credibility gap must be filled with solid model analysis, and therefore presents a great opportunity for the Production and Operations Management (POM) research community. The paper reviews some of the ongoing research efforts that attempt to close the credibility gap, and suggests additional directions for further strengthening the POM's contribution to help industry realize the full potentials of RFID.

Consider a supply chain in which a product must pass through multiple sites located in series before it is finally delivered to outside customers. Incentive problems may arise in this system when decisions are delegated to corresponding site managers, each maximizing his/her own performance metric. From the overall system's point of view, the decentralized supply chain may not be as efficient as the centralized one. In practice, alternative performance mechanisms are often used to align the incentives of the different managers in a supply chain. This paper discusses the cost conservation, incentive compatibility, and informational decentralizability properties of these mechanisms. In particular, for a special type of supply chain, we show that a performance measurement scheme involving transfer pricing, consignment, shortage reimbursement, and an additional backlog penalty at the last downstream site satisfies all these properties.

Information integration efforts between manufacturers and retailers, in the form of information sharing, synchronized replenishment, and collaborative product design and development, have been cited as major means to improve supply chain performance. This paper develops a conceptual framework that relates information-integration initiatives to manufacturer profitability. The framework allows such initiatives to impact inventory management and revenue-enhancing measures that, in turn, increase manufacturer profit margins, or affect profit margins directly. Through an extensive survey in the food and consumer packaged goods industry, we empirically examine this framework. The analysis reveals that the various integration techniques are differentially associated with manufacturer performance. Collaborative planning on replenishment, in the form of vendor-managed inventory (VMI), is directly and positively related to manufacturer margins, while collaboration on new products and services is positively related to intermediate performance measures. Specifically, this latter form of collaboration allows the manufacturer to charge higher wholesale prices and, interestingly, is associated with lower retailer, and consequently manufacturer, stockouts. In contrast, collaboration on the handling of excess and defective retailer inventory (i.e., reverse logistics) results in higher manufacturer stockout levels, on average. Solely sharing information on either inventory levels or customer needs is associated with higher manufacturer performance measures up to a certain point; sharing this information is prevalent among manufacturers that achieve industry-average profitability relative to those that achieve below industry-average profitability. The paper explains these results in the context of the conceptual framework developed and discusses the managerial implications for effective coordination between supply chain partners.

Building a strong supply chain is essential for business success. But when it comes to improving their supply chains, few companies take the right approach. Many businesses work to make their chains faster or more cost-effective, assuming that those steps are the keys to competitive advantage. To the contrary: Supply chains that focus on speed and costs tend to deteriorate over time. The author has spent 15 years studying more than 60 companies to gain insight into this and other supply chain dilemmas. His conclusion: Only companies that build supply chains that are agile, adaptable, and aligned get ahead of their rivals. All three components are essential; without any one of them, supply chains break down. Great companies create supply chains that respond to abrupt changes in markets. Agility is critical because in most industries, both demand and supply fluctuate rapidly and widely. Supply chains typically cope by playing speed against costs, but agile ones respond both quickly and cost-efficiently. Great companies also adapt their supply networks when markets or strategies change. The best supply chains allow managers to identify structural shifts early by recording the latest data, filtering out noise, and tracking key patterns. Finally, great companies align the interests of the partners in their supply chains with their own. That's important because every firm is concerned solely with its own interests. If its goals are out of alignment with those of other partners in the supply chain, performance will suffer. When companies hear about the triple-A supply chain, they assume that building one will require increased technology and investment. But most firms already have the infrastructure in place to create one. A fresh attitude alone can go a long way toward making it happen.

Late in the 1980s, Hewlett-Packard (HP) faced inventories mounting into the billions of dollars and alarming customer dissatisfaction with its order fulfillment process. HP produces computation and measurement products whose supply chains include manufacturing integrated circuits, board assembly, final assembly, and delivery to customers. To reduce inventory and improve order fulfillment, HP called on an internal team of industrial engineers and management scientists augmented by academic collaboration. The team used an iterative process, enriched by the interaction of model development and application. HP reaped benefits well beyond its manufacturing operations, extending to diverse functions throughout the organization. Similarly, the academic partners have infused their research with real-life experience. The supply-chain methodology is now mature, and HP is transferring the technology into the product divisions.

Classical Economic Manufacturing Quantity (EMQ) models have usually ignored the possibility of process deterioration and the existence of defective items in the production lot. Moreover, the use of machine inspection for maintenance and restoration purposes has not been considered. On the other hand, past studies on maintenance models do not consider the length of a production run to be a decision variable. This paper addresses the problem of joint control of production cycles or manufacturing quantities and maintenance by inspection. A simple relationship has been developed to determine the effectiveness of maintenance by inspection. Furthermore, when maintenance by inspection is adopted, it is shown that the optimal inspection intervals are equally-spaced. The problem of simultaneous determination of EMQ and the inspection schedules is solved by using an approximation to the cost function. The resulting EMQ is found to be an adjustment to the classical EMQ. Finally, the relationships between different parameter values and the magnitudes of the cost penalty for using the classical EMQ are examined.

Advances in information system technology have had a huge impact on the evolution of supply chain management. As a result of such technological advances, supply chain partners can now work in tight coordination to optimise the chain-wide performance, and the realised return may be shared among the partners. A basic enabler for tight coordination is information sharing, which has been greatly facilitated by the advances in information technology. This paper describes the types of information shared inventory, sales, demand forecast, order status, and production schedule. We discuss how and why this information is shared using industry examples and relating them to academic research. We also discuss three alternative system models of information sharing - the information transfer model, the third party model and the information hub model.

By examining the state of operations management (OM) research from 1980 to 2015 and by considering three new industry trends, we propose new OM research directions in socially and environmentally responsible value chains that fundamentally expand existing OM research in three dimensions: (a) contexts (emerging and developing economies); (b) objectives (economic, environmental, and social responsibility); and (c) stakeholders (producers, consumers, shareholders, for-profit/nonprofit/social enterprises, governments, and nongovernmental organizations). In this paper, we describe some examples of this new research direction that are intended to stimulate more exciting OM research, to contribute to the economic and social well-being of both developing and developed economies. This paper was accepted by Teck-Hua Ho, operations management.

In multi-echelon inventory systems that cover extensive geographical regions, emergency lateral transshipment between bases is commonly practiced to provide improved service support for products. In these systems, since neighboring bases are at shorter distances than to the depot, the base may seek stock from them to satisfy its demands when it is out of stock. In this paper we develop a continuous-review multi-echelon model for repairable items when emergency lateral transshipments between identical bases are allowed. Approximations are derived and tested for the expected level of backorders and the quantity of emergency lateral transshipments in cases where fairly high service levels are required. These approximations are used to determine the optimal stocking levels in such a system. A procedure to find these stocking levels is given. Numerical examples illustrate the application of the procedure and the tradeoffs involved in emergency lateral transshipment.

We analyze the sourcing decision of a buyer choosing between two supplier types: responsible suppliers are costly but adhere to strict social and environmental responsibility standards, whereas risky suppliers are less expensive but may experience responsibility violations. A segment of the consumer population, called socially conscious, is willing to pay a higher price for a product sourced from a responsible supplier and may not purchase in the event of a responsibility violation from a risky supplier. We identify four possible sourcing strategies that a buyer might employ: low cost sourcing (sourcing from the risky supplier), dual sourcing, responsible niche sourcing (sourcing from a responsible supplier and selling only to socially conscious consumers), and responsible mass market sourcing (sourcing responsibly and selling to all consumers). We determine when each strategy is optimal and show that efforts to improve supply chain responsibility that focus on consumers (by increasing their willingness to pay for responsibility or increasing the number of consumers that are socially conscious) or increasing supply chain transparency may lead to unintended consequences, such as an increase in risky sourcing. Efforts that focus on enforcement and penalizing the buyer, however, never backfire and always lead to more responsible sourcing and less risky sourcing. This paper was accepted by Yossi Aviv, operations management.

Companies that source from emerging economies often face supplier responsibility risks, namely, financial and reputational burdens that the companies have to bear when their suppliers’ engagement in noncomplying labor and environmental practices becomes public. To mitigate such risks, companies can invest in screening mechanisms and design incentive schemes in sourcing contracts. Common mitigation instruments include supplier certification, process audits, and contingency payments. The interactions of these instruments are often not well understood. We first note that the effectiveness of any mitigation instrument depends on how it changes the economic trade-offs faced by a supplier in compliance to social and environmental standard, and hence we develop a model that explicitly captures such trade-offs. As a result, our model endogenizes the supplier’s noncompliance probability and connects it with various factors, including the supplier’s intrinsic ethical level that is unobservable to the buyer. We then study the buyer’s optimal contracting problem under different mitigation instruments. We find that although the process audit and contingency payment instruments can directly lower supplier responsibility risk, they, acting alone, are not as effective as the supplier certification instrument in screening suppliers with different ethical levels. Nevertheless, these instruments are all complementary to each other; when used jointly, they make supplier screening more effective and result in lower sourcing cost. These findings provide explanations for some of the observed practices used in industry to mitigate supplier responsibility risks. This paper was accepted by Serguei Netessine, operations management.

At Hewlett-Packard (HP) Company, design for manufacturability has recently been adopted as a principle for product design and development. Frequently overlooked is the relationship between design and the eventual customization, distribution, and delivery of the product to multiple markets. Different markets may have different requirements for the product due to differences in taste, language, geographical environment, or government regulations. We use design for localization or design for customization for design processes that take into account the operational and delivery service considerations for the multiple market segments. We developed an inventory model that the HP's Deskjet-Plus Printer Division used to evaluate alternative product and process designs for localization. Significant benefits can be obtained by properly exploring the opportunities in this design for localization concept.

One of the major challenges to operational managers is product proliferation. Product proliferation makes it difficult to forecast demands accurately, and consequently, leads to high inventory investment and poor customer service. Such proliferation is often a result of the global nature of the market place. Different markets may have different requirements for the product, due to differences in taste, language, geographical environment, or government regulations. Another reason for product proliferation is the expansion of the customer base. Different product versions are often developed for different market segments (e.g., education, personal, business, or government users may have different needs of a product). To gain control of inventory and service, significant benefits can be obtained by properly exploring the opportunities in the design of the product or the process by which the product is made. Logistic issues like inventory and service are thus important dimensions that design engineers should consider, in addition to measures like functionality, performance, and manufacturability. This paper describes how some simple inventory models can be used to support the logistic dimensions of product/process design. Actual examples are used for illustration.

(This article originally appeared in Management Science, April 1997, Volume 43, Number 4, pp. 546–558, published by The Institute of Management Sciences.) Consider a series of companies in a supply chain, each of whom orders from its immediate upstream member. In this setting, inbound orders from a downstream member serve as a valuable informational input to upstream production and inventory decisions. This paper claims that the information transferred in the form of “orders” tends to be distorted and can misguide upstream members in their inventory and production decisions. In particular, the variance of orders may be larger than that of sales, and distortion tends to increase as one moves upstream—a phenomenon termed “bullwhip effect.” This paper analyzes four sources of the bullwhip effect: demand signal processing, rationing game, order batching, and price variations. Actions that can be taken to mitigate the detrimental impact of this distortion are also discussed.

This paper investigates the impacts of a secondary market where resellers can buy and sell excess inventories. We develop a two-period model with a single manufacturer and many resellers. At the beginning of the first period resellers order and receive products from the manufacturer, but at the beginning of the second period, they can trade inventories among themselves in the secondary market. We endogenously derive the optimal decisions for the resellers, along with the equilibrium market price of the secondary market. The secondary market creates two interdependent effect—a quantity effect (sales by the manufacturer) and an allocation effect (supply chain performance). The former is indeterminate; i.e., the total sales volume for the manufacturer may increase or decrease, depending on the critical fractile. The latter is always positive; i.e., the secondary market always improves allocative efficiency. The sum of the effects is also unclear—the welfare of the supply chain may or may not increase as a result of the secondary market. Lastly, we study potential strategies for the manufacturer to increase sales in the presence of the secondary market.

e-Business has emerged as a key enabler to drive supply chain integration. Businesses can use the Internet to gain global visibility across their extended network of trading partners and help them respond quickly to changing customer demand captured over the Internet. The impact of e-business on supply chain integration can be described along the dimensions of information integration, synchronized planning, coordinated workflow, and new business models. As a result, many of the core supply chain principles and concepts can now be put into practice much more effectively using e-business. Significant value can be created by e-business enabled supply chain integration.

The value of information sharing and how it could address the bullwhip effect have been the subject of studies in the literature. Most of these studies used different forms of demand models, assuming that no order smoothing was used by the retailer and that the supplier has full knowledge of the retailer's demand model and order policy. In this paper, we contribute to the literature by starting with a most general demand model, coupled with a smoothing policy for order variability control. In addition, we do not require that the supplier has full knowledge of the retailer's demand model and order policy, but instead let the retailer share its projected future orders (and freely revise them as the retailer sees fit). Under such a setting, we first obtain a unifying formula for the magnitude of the bullwhip effect. The formula indicates that it is the forecast correlation over the exposure period as a whole that determines the magnitude of the bullwhip effect. We then quantify the value of information sharing and generalize the existing results in the literature. Finally, we explore the optimal smoothing parameters that could benefit the total supply chain. The resulting optimal policy resembles the postponement strategy. We find that information sharing together with order postponement improves the supply chain performance, even though the order variability may amplify in some cases.

Supply chain security has become a major concern to the private and public sector, after the disastrous event of September 11, 2001. Prior to September 11, 2001, supply chain security concerns were related to controlling theft and reducing contraband such as illegal drugs, illegal immigrants, and export of stolen goods. But after September 11, 2001, the threat of terrorist attacks has heightened the need to assure supply chain security. The public is of course concerned with the potential of having weapons of mass destruction embedded in the shipments through the supply chain. In addition, the private sector is concerned with the costs of assuring security, and the potential disruptions associated with real or potential terrorist acts. Governments and industry have all responded with proposals to create more confidence in supply chain security, while maintaining smooth flows of goods and services in a global supply chain. One of the most effective strategies may be to apply the lessons of successful quality improvement programs. In this paper, we describe how the principles of total quality management can actually be used to design and operate processes to assure supply chain security. The central theme of the quality movement––that higher quality can be attained at lower cost by proper management and operational design––is also applicable in supply chain security. By using the right management approach, new technology, and re-engineered operational processes, we can also achieve higher supply chain security at lower cost. We will demonstrate how this can be done with a quantitative model of a specific case example.

In this paper, we examine two information-based supply-chain efforts that are often linked to Vendor-Managed Inventory (VMI) programs. Specifically, we consider a supplier serving multiple retailers located in a close proximity. The first effort uses information on the retailers' inventory positions to coordinate shipments from the supplier to enjoy economies of scale in shipments, such as full truckloads.The second effort uses the same information for eventual unloading of the shipments to the retailers to rebalance their stocking positions. How much benefit do we gain from such initiatives? What are the relative benefits of the two initiatives? What are the drivers of such benefits? This paper seeks answers to these questions.

As RFID technology matures and organizations seek to deploy it in their business operations, a basic objective in the endeavor is that of extracting business value from the technology. This paper examines three dimensions of the value proposition of RFID and attempts to identify areas for further investigation. The first dimension consists of the generic architecture of RFID implementations and the drivers of value that can result from its components. The second consists of measurement issues associated with quantification of value. Since the complete benefits of RFID will only result when multiple independent organizations deploy the technology and coordinate the resulting information flows, the third dimension addresses incentives for achieving that diffusion. The collection of issues identified through this exercise offers an initial roadmap to view ongoing research and recognize additional problems for further investigation.

This paper presents a model of an (s, S) inventory system in which there are two priority classes of customers. The model treats excess demands as lost sales and can accommodate an arbitrary deterministic lead time. After considering the associated Markov-chain model, an approximate, renewal-based model is derived. This approximation is used to develop a greedy heuristic which minimizes expected costs subject to a fill-rate service constraint. The paper concludes with the results of an extensive numerical test of both the accuracy of the approximation and the performance of the heuristic with respect to the true optimal solution. Results indicate good performance which deteriorates as the fill rate requirement and lead time increase.

Past research into the determinants of international trade highlighted the importance of the basic spatial gravity model augmented by additional variables representing sources of friction. Studies modeled many sources of friction using various proxies, including indices based on expert judgment in some cases. This paper focuses on logistics friction and draws on a data set recently compiled by the World Bank with specific quantitative metrics of logistics performance in terms of time, cost, and variability in time. It finds that the new variables that relate directly to logistics performance have a statistically significant relationship with the level of bilateral trade. It also finds that a single logistics index can capture virtually all of the explanatory power of multiple logistics indicators. The findings should spur public and private agencies that have direct or indirect power over logistics performance to focus attention on reducing sources of friction so as to improve their country's ability to compete in today's global economy. Moreover, since the logistics metrics are directly related to operational performance, countries can use these metrics to target actions to improve logistics and monitor their progress.

Many manufacturing processes involved in the fabrication and assembly of “high-tech” components have highly variable yields that complicate the planning and control of production. We develop a procedure to determine optimal input quantities at each stage of a serial production system in which process yields at each stage of production may be stochastic. The procedure is applied to an example in the manufacture of a light-emitting diode (LED) display using actual yield data. We also provide a brief analysis of the quantifiable savings obtained by reducing the variability of the yield at one production stage.

Abstract Abstract Quantity discount schedules have been studied, in the past, from the retailer's, and not the supplier's, point of view. These studies address the problem of determining the economic order quantities for the retailer, given a quantity discount schedule set by the supplier. In this paper, this problem is addressed from the supplier's point of view, assuming that the retailer always uses his optimal order quantity. It is shown that under certain circumstances, quantity discounts could be of benefit to the supplier (and obviously to the retailer), even when retailer's demand is insensitive to price changes. An algorithm is developed to determine the optimal pricing policy for a linear quantity discount schedule. Numerical examples are provided, and sufficient conditions when no quantity discount should be offered are derived.

The bullwhip effect, or demand information distortion, has been a subject of both theoretical and empirical studies in the operations management literature. In this paper, we develop a simple set of formulas that describe the traditional bullwhip measure as a combined outcome of several important drivers, such as finite capacity, batch-ordering, and seasonality. Our modeling framework is descriptive in nature as it features certain plausible approximations that are commonly employed in practical inventory systems. The results are nonetheless compelling and can be used to explain various conflicting observations in previous empirical studies. Building on the theoretical framework, we discuss the managerial implications of the bullwhip measurement. We show that the measurement can be completely noninformative about the underlying supply chain cost performance if it is not linked to the operational details (such as decision intervals and leadtimes). Specifically, we show that an aggregated measurement over relatively long time periods can mask the operational-level bullwhip. In addition, we show that masking also exists under product or location aggregation in some illustrative cases.

Abstract The single period plant layout problem under uncertainty is discussed. Solution procedures for dealing with this problem, mainly the robustness approach, are recommended. The effectiveness of the robustness approach to this problem is illustrated by a numerical example.

This paper studies the impact of logistics performance on global bilateral trade. Taking a supply chain perspective, logistics performance refers to cost, time, and complexity in accomplishing import and export activities. We draw on a data set compiled by the W orld B ank containing specific quantitative metrics of logistics performance in terms of time, cost, and variability in time. Numerous researchers have shown that logistics performance is statistically significantly related to the volume of bilateral trade. Our research calibrates the impact of specific improvements in logistics performance (time, cost, and reliability) on increased trade. Our findings can spur public and private agencies that have direct or indirect influence over logistics performance to focus attention on altering the most relevant aspects of logistics performance to improve their country's ability to compete in today's global economy. Moreover, as our logistics metrics are directly related to operational performance, countries can use these metrics to target actions to improve logistics and monitor their progress.

Firms are increasingly looking to eradicate social and environmental non‐compliances at their suppliers in response to increasing regulations, consumer demand, potential for supply chain disruptions, and to improve their social, environmental, and economic supply chain performance. This study develops a model of the relationship between the buyer's supplier incentives and penalties for the supplier's social and environmental compliance, and the outcomes in terms of reduction in supplier social and environmental violations as well as the buyer's own operating costs. This model is tested empirically through analysis of a dataset of opinion‐based survey responses from practitioners at 334 companies across 17 industries. The analysis finds specific penalties and incentives that are positively associated with reduced supplier violations and reduced buyer operating costs. In particular, offering suppliers incentives of increased business and training for improving social and environmental performance is strongly associated with a reduction in both violations and operating costs.

Inefficiency and inequity are two challenges that plague humanitarian operations and health delivery in resource‐limited regions. Increasing capacity in humanitarian and health delivery supply chains is one option that has the potential to improve equity while maintaining efficiency. For example, the nonprofit organization Riders for Health has worked to increase capacity by providing reliable transportation to health workers in rural parts of sub‐Saharan Africa; with more motorcycle hours at their disposal, health workers can perform more outreach to outlying communities. We develop a model using a family of fairness function to quantify the efficiency and equity of health delivery as capacity is increased via development programs. We present optimal resource allocations under utilitarian, proportionally fair, and egalitarian objectives and extend the model to include dual modes of transport and diminishing returns of subsequent outreach visits. Finally, we demonstrate how to apply our model at a regional level to provide support for humanitarian decision makers such as Riders for Health. We use data from the baseline phase of our evaluation trial of Riders for Health in Zambia to quantify efficiency and equity for one real‐world scenario.

With a dominant volume of global transportation being conducted by sea, ocean container transport greatly impacts the global economy. Since sea vessels are drastically more fuel efficient when traveling at lower speeds, slow steaming has become a widely adopted practice to reduce bunker costs. However, this leads to a longer transportation time, which together with the unpredictability of the delay has been a big challenge. We propose a model to quantify the relationship among shipping time, bunker cost and delivery reliability. Our findings lead to a simple and implementable policy with a controlled cost and guaranteed delivery reliability.

This paper considers how research in operations management can support the development of strategies for the design and management of global supply chains. It includes a model-based statement of the problem and an overview of highlights of past research that is relevant to both theory and practice. The paper identifies opportunities for research that focus on how global supply chain modeling can inform the current debate concerning the reaction of firms to changes in government policy that are relevant to global manufacturing and logistics. This includes policy changes, such as tariffs, content requirements, taxes, and investment incentives.

Abstract Recent research has documented that companies are pursuing a variety of strategies to enhance supply‐chain resilience. This paper examines how managers actually think about resilience strategies, and then analyzes the relationship between operations, supply‐chain characteristics, and the implemented strategies. We define a “Triple‐P” framework that matches resilience strategies to supply‐chain archetypes by examining P roduct, P artnership, and P rocess complexity based on interviews of senior supply‐chain executives. These interviews revealed two major influencers of resilience strategy, that is, Homogeneity of internal supply‐chain processes and Integration with other actors in their end‐to‐end supply chains . We found that the supply chains have different resilience requirements, have different ways to achieve resilience (which we conceptualize as “bespoke supply‐chain resilience”), and face different obstacles to resilience. This study aims at initiating a dialogue between supply‐chain scholars and practitioners to support more research for developing an effective supply‐chain resilience strategy.

Which e-businesses will prevail? New research on e-fulfillment may hold the key. After all, getting a customer's online order is not enough: E-businesses also must show that they can deliver products quickly and efficiently. Hau L. Lee and Seungjin Whang, professors of operations, information and technology at Stanford University's Graduate School of Business, have studied a few successful online companies and their innovative ways of applying order-fulfillment strategies. Although the principles are not new, Internet technologies enable them to be applied in new and expanded ways. The two core concepts for improving e-fulfillment efficiency are making more use of information flows instead of physical product flows and capitalizing on existing pipelines and infrastructures. Those concepts underlie five key e-fulfillment strategies: logistics postponement, dematerialization, resource exchange, leveraged shipments and clicks-and-mortar. Whether the strategy expands on time-tested models or is a breakthrough, the trick is to determine the best one for a given situation. A computer company might use logistics postponement. By capturing more-accurate information, it could assemble final goods on demand and thereby save money by postponing delivery decisions until after receiving the final word on what the customer wants. Other companies might use dematerialization, converting physical products into information flows, just as a music CD can be converted to MP3 format or Egreetings.com substitutes digital flows for paper greeting cards sent by regular mail. With resource exchange, an e-company that needs to move a load from Hong Kong to San Francisco might borrow a ship from another company that needs a cost-effective way to return its empty vessel to California. Webvan uses the leveraged-shipment strategy, making the most of existing networks. With its clicks-and-mortar model, CVS covers the last mile by having customers pick up their online orders. Some online purchasers in Japan do the same: 7dream.com utilizes the ubiquitous 7-Eleven stores to enable a group of Japanese companies to do bulk deliveries. Pointing out ways that companies are extending e-fulfillment value beyond cost containment, the authors also demonstrate how secondary opportunities are taking companies beyond the last mile.

Price protection is a commonly used practice between manufacturers and retailers in the personal computer (PC) industry, motivated by drastic declines of product values during the product life cycle. It is a form of rebate given by the manufacturer to the retailer for units unsold at the retailer when the price drops during the product life cycle. It is a controversial policy in the PC industry because it is not clear how such a policy benefits the supply chain and its participants. We show that price protection is an instrument for channel coordination. For products with long manufacturing lead times, so the retailer has a single buying opportunity, a properly chosen price protection credit coordinates the channel. For products with shorter manufacturing lead times, so the retailer has two buying opportunities, price protection alone cannot guarantee channel coordination when wholesale prices are exogenous. However, when the price protection credit is set endogenously together with the wholesale prices, channel coordination is restored. In the two-buying-opportunity setting with fixed wholesale prices, we show that price protection has two primary impacts: (1) shifting sales forward in time and (2) increasing total sales. Finally, we present a simple numerical example that suggests, given the current economics of the PC industry, that price protection under fixed wholesale prices may benefit the total chain and the retailer but hurt the manufacturer.

Products with high product variety are often made in a manufacturing process (or a supply chain) consisting of multiple stages, with products taking certain features or “personalities” at each stage. The product may start as a common single engine. As the product moves along manufacturing process, more features are added, and the product assumes more identities of the final end product. When demands of the end products are variable from period to period, the production volumes of the intermediate stages in the manufacturing process are also variable. It is widely recognized that variabilities of production volumes may add cost to the process. This paper is motivated by our observations in industry, where some companies have reengineered the manufacturing process by reversing two consecutive stages of the process. Such changes could lead to variance reduction, thereby improving the performance of the process. We develop formalized models that characterize the impact of such changes: operations reversal. These models are used to derive insights on when such reversal would be advisable.

IBM recently implemented Optimizer, a system for flexible and optimal control of service levels and spare parts inventory, in its US network for service support. It is based upon recent research in multi-echelon inventory theory to address the IBM network. The inherent complexity and very large scale of the basic problem required IBM to develop suitable algorithms and sophisticated data structures and required large-scale systems integration. Optimizer has greatly improved IBM's US service business. The implementation of Optimizer has made it possible to make strategic changes to the configuration and control of the parts distribution network. It resulted in simultaneously reducing inventory investment and operating costs and improving service levels. Most important, however, Optimizer has proven to be a highly flexible planning and operational control system.

In this paper we compare the following procurement strategies based on their expected costs: strategic partnership, which is based on a long‐term relationship with a single supplier; online search, which is a short‐term strategy; and a combined strategy, which is some combination of the first two strategies. In addition, we determine for the online search and combined strategy the optimal number of suppliers to contact for a price quote, and analyze how it depends on the various cost and demand parameters. The main contribution of this paper is that it does not assume a single procurement strategy, but rather compares three alternatives.

In multi-echelon repairable inventory systems with high set-up cost for order and/or high demand rates, the use of batch ordering may be more cost-effective than the common (S − 1, S) ordering policy. This paper addresses the issue of determining the optimal order batch size and stocking levels at the stocking locations in such a system. A power approximation is used to estimate the total system stock and backorder levels from which the optimal batch size can be readily determined. A search routine involving “one-pass” searches are then followed to obtain the stocking levels at the depot and the local sites of the system. This procedure has been evaluated using 900 test cases and has been found to be very effective. The power approximation approach also results in a simple analytical relationship to test whether or not (S − 1, S) is an optimal ordering policy for repairable items in a multi-echelon environment.

In this commentary, we trace back how we pursued research on the bullwhip effect, which resulted in the article published in Management Science. We reflect on the evolution of this concept, the impact that our work has had on industry, the way our work has been used in the teaching of supply chain management, and the key directions of research that have taken place since then.

Abstract In this paper, the joint problem of production planning and maintenance schedule is studied under the realistic assumption that the cost of process restoration is a function of the detection delay and the existence of shortages in the system. The detection delay is defined as the elapsed time since the production process has deteriorated until it is identified by some inspection procedure and repaired. Since production planning and maintenance problems have usually been studied as separate problems, this paper is an attempt to develop a formal framework for the joint problem. We have developed sufficient conditions for the optimality of the commonly used equal-interval maintenance schedule. The conditions are found to be a function of parameters such as the cost of defective items, the mean time for system deterioration, and the form of the restoration cost function. For specific restoration cost functions such as linear and exponential, an efficient solution procedure is presented for the simultaneous determination of the number of maintenance inspections in a production run, the length of the production run and consequently the economic manufacturing quantity, and the maximum level of backorders. A numerical example illustrates the use of this procedure and the differences between the optimal cost obtained by this procedure and the cost obtained by using the classical economic manufacturing quantity model.

We consider a multi-item inventory system with dependent item demands represented by a multivariate normal distribution and filled under a First-Come-First-Served rule. Each item is managed independently through a periodic review order-up-to policy while all items have the same review cycle. We obtain the joint demand fulfillment probability within a pre-specified time window. We also study the problem of maximizing the joint demand fulfillment probability and discuss a heuristic approach in which equal safety factors (equal fractiles) are specified for all items. Finally we present numerical results and an application with actual data.

Problem definition: Manufacturing firms are undergoing restructuring defined by a collection of adjustments and decisions, which affect the source and destination of manufactured products throughout the firm’s global supply chain network. We report on a comprehensive picture of manufacturing sourcing on a global basis. Academic/practical relevance: With dynamic changes in global economic, political, and technological conditions, the design of global supply chain strategies has become critically important for executives and has great potential for operations management research. Methodology: Our work is based on a global field study conducted in 2014 and 2015 among leading manufacturers from a wide range of industries. The data set has the distinguishing feature of reflecting actual decisions that the firms made recently (during the last three years). Results: Companies are currently restructuring their global production footprints. The majority of firms engage in offshoring. Reshoring does occur but seldom for corrective reasons. China remains the most attractive site for production sourcing, followed by Eastern Europe and Southern Asia. Manufacturing continues to decline in the developed economies of Japan and Western Europe. We observe that while North America may be at the cusp of a manufacturing renaissance, such a change is not just because of reshoring by domestic firms. Labor cost no longer dominates manufacturing location decisions; rather, firms decide based on complex trade-offs among a variety of factors. Finally, firms localize production in developed economies and use developing economies as production hubs. Managerial implications: Our goal in this paper is to inform both managerial policy decisions and the academic research agenda by developing insights on managerial practices that concern production sourcing and on the factors that drive such decisions. We develop hypotheses concerning how firms make these strategy decisions, and we discuss implications for analytical and empirical research.

Big data and the related methodologies could have great impacts on operations and supply chain management. Such impacts could be realized through innovations leveraging big data. The innovations can be described as first improving existing processes in operations through better tools and methods; second expanding the value propositions through expansive usage or incorporating data not available in the past; and third allowing companies to create new processes or business models to serve customers in new ways. This study describes this framework of the innovation cycle.

In complex supply chains, the benefits and costs of technological innovations do not always accrue equitably to all parties; thus, their adoption may critically depend on sourcing relationships and incentives. In a setting with uncertain and endogenous process yield, we study the potential of two features—contract design and sourcing channel—to create mutual benefit in decentralized value chains, where suppliers bear the costs of new technologies while benefits accrue primarily to buyers. Our focus is on agricultural value chains, where parties may transact through a channel that blends farmers’ produce (“commodity-based channel”) or that allows a one-on-one interaction between farmer and processor (“direct-sourcing channel”). Our study provides insights to companies seeking to incorporate responsible sourcing strategies while also creating economic value—a concept called “creating shared value.” We identify that the technology’s “cost elasticity” drives whether switching sourcing channel, changing contract structure, or adopting an integrated change is necessary to create shared value. This highlights that value chain innovations need to be properly designed—and sometimes combined—to achieve sustainable implementation. We also find that certain simple contracts with a linear or bonus structure are optimal, while other intuitive contracts could be detrimental. Using a data set of farms in Patagonia, Argentina, we estimate that the proposed mechanism could increase average supply chain profit by 6.9% while realizing positive environmental benefits. The online appendix is available at https://doi.org/10.1287/msom.2017.0627 . This paper has been accepted for the Manufacturing & Service Operations Management Special Issue on Value Chain Innovations in Developing Economies.

Past research (along with our experience) suggests that a firm's supply chain (i.e., value chain) plays an integral role in its ability to not only reduce cost via process innovation, but also in its ability to develop new products and services. Evidence suggests the value chain is playing an ever‐more‐important role, with greater prevalence of distributed product development (spanning geographic, organizational, or firm boundaries) and open innovation (performed outside the firm). We discuss some of the trends with regard to supplier and customer involvement in the innovation process, and summarize some of the research exploring the rationale behind those trends and the research offering advice on how firms can use external resources to further improve their innovation performance. We present a number of examples that illustrate some best practices.

The Belt and Road Initiative (BRI) is a massive, ambitious, long-term project initiated by the Chinese government, with participation from many other countries, to facilitate trade and improve logistics in an effort to promote global economic development. In this paper, we identified the supply chain and logistics innovations linked to the BRI. These innovations include new routes and modes for global trade, new supply chain design, reduction of cross-border logistics frictions, and entrepreneurial development. Examples of some of these innovations are emerging, while new ones are being developed. These innovations can enable businesses to improve their operational performances and create economic value. At the same time, to realize the full potentials of BRI, new work processes and technologies, incentive alignment, collaborations among businesses, and optimized planning are needed. This provides great opportunities for researchers to explore how to overcome barriers and achieve the full values of BRI.

Problem definition: Agricultural innovation can help farmers improve their productivity; reduce their environmental impact; and address the challenges associated with ever-changing soil, weather, a...

Supply chain fairness refers to the practices that members in a supply chain treat each other. Due to imperfections of a competitive market, some members could exploit their positions or circumstances that enabled them to gain excessive advantage over others. Such unfair practices could be in the form of unfair prices, unfair trade, or unfair pay. The COVID‐19 pandemic has exposed many vulnerabilities of global supply chains, but it also unearthed unfair supply chain practices. Yet, some firms are developing new initiatives to address various supply chain fairness issues, potentially creating strategic values. These observations motivate us to examine the potential benefits, identify underlying challenges, and discuss emerging opportunities for improving fairness in supply chains. This examination has highlighted the potential of a rich research agenda, and we propose research questions for further exploration.

Since the first case was identified, COVID-19 has spread to more than 200 countries. As of October 25, 2022, it had resulted in over 6 million deaths and 600 million confirmed cases. The rapid widespread of COVID-19 led to large-scale disruptions with major supply and demand shocks in supply chains, causing significant negative impacts on the global economy. The World Bank reported that the world GDP growth rate for 2020 was −3.27%, indicating the worst recession since 1961. First, the initial epidemic-control efforts blocked the flows of raw materials across the world, and limited labor movements by imposing temporary travel restrictions. As a result, many firms struggled with supply disruptions and labor shortages, setting off a chain reaction of disruption in global supply chains. Fortune (2020) reported that as of February 21, 2020, 94% of the Fortune 1000 companies had experienced supply-chain disruptions due to COVID-19.1 According to the Institute for Supply Management, almost 75% of companies reported supply-chain disruptions in some capacity due to coronavirus-related transportation restrictions.2 A survey conducted by the National Association of Manufacturers reported that 78.3% of manufacturers believed that COVID-19 had a significant negative influence on their financial performance, and 35.5% of them had experienced some type of supply chain disruptions.3 Second, the demand for essential personal-protective equipment increased dramatically because the virus was easily transmitted from person to person through air-borne droplets; accompanied by a drop in the need for other manufactured products (Nicola et al., 2020). For example, COVID-19 had caused a surge in the demand of face masks that were necessary to prevent infection among frontline workers (who were directly exposed to the virus when treating infected patients or conducting nucleic acid tests) and individuals in public places. China was the main producer of masks at the start of the crisis, accounting for approximately half of world production. In January 2020, China could produce 20 million masks per day, which was insufficient to equip even just healthcare workers in China. As a result of extensive efforts by the government and companies, Chinese production increased six-fold and reached 116 million masks per day by the end of February, 2020. But even this was insufficient to meet its own demand, and China imported a large quantity of masks. Similarly, Germany also experienced very limited availability of face masks in the spring of 2020. To increase the supply of face masks, the German government contracted with hundreds of companies and introduced a series of incentives to encourage local, non-medical manufacturers to temporarily transform to produce masks. More generally, demand patterns for supplies of all types became less predictable from the beginning of pandemic. Significant changes on the demand side included quick shifts of buying patterns (from physical stores to online stores) and rapid shifts of product mix (e.g., decline in office equipment to be used on site, but increases in appliances for home-office use). Compared with previous pandemics, COVID-19 has affected supply chains more durably and globally. As this special issue approaches publication, the COVID-19 pandemic has lasted for over 3 years. The extended duration of this external shock has been sufficient to permanently change the behavior of supply-chain partners (Poelman et al., 2021). These changes have encouraged firms to demonstrate an innovative bent, leading to structural process changes in a variety of industries. For example, demand for in-person restaurant dining decreased, whereas demand for take-away foods greatly increased, though by 2022, this pattern had begun to reverse. Under this change, firms needed to explore new ways of organizing their supply chains with respect to factors like product diversity and cooperation with more partners in the supply chains. This made it possible to define products and services to benefit quickly from pandemic-related opportunities (van der Vegt et al., 2015). The COVID-19 pandemic has also affected global supply chains as economic integration interacted with travel restrictions and worldwide lockdowns (Nikolopoulos et al., 2021) to create unprecedented challenges for enterprises to respond to changes in demand and supply, employee shortages, and lack of access to financial capital. These long lasting, structural, and global supply-chain impacts have led enterprises to increase responsiveness and resilience (2Rs) via management mechanisms and technological innovations. Based on the media reports and literature review, such innovative measures include cross-enterprise cooperation (e.g., Unilever and Terra Drone), government-enterprise cooperation (e.g., Hubei Provincial Government and JD Logistics), adjustments to manufacturing activities (e.g., BYD, Foxconn, Pernod Ricard, and Bauer shifted their production to face masks/shields), as well as the adoption of digital technologies (e.g., JD Logistics, New HAVI). This 2R emphasis represents a major departure from the usual emphasis on cost reduction (Caunhye et al., 2016). Observing that some companies have performed well in responding quickly to changes in demand and supply, and flexibly to disruptions, we seek to learn from the innovative practices and experiences of that have yielded success in dealing with this large-scale disruption. What were the key lessons learned in terms of selecting suppliers and managing supplier/customer relationships, designing global supply networks, and adopting new digital technologies and big data analytics? What were the long-term impacts of COVID-19 on the structures of global supply chains and the strategic positioning of major supply chain players? This special issue focuses on uncovering the key success factors and lessons from these innovative practices. We aimed to gain deeper understanding of how the adoption of technological innovations, business model innovations, and innovations in collaboration mechanisms and methods of operations improvement/optimization have helped companies enhance 2Rs in supply chains. To frame this special issue, we define some key concepts and briefly review some literature published in leading operations management journals focusing on supply chain responsiveness, supply chain resilience, supply chain integration, and big data analytics. To address disruptions and demand shocks, a great deal of emphasis has been placed in research on strategies to enhance the 2Rs in supply chains (Hendricks et al., 2009). Responsiveness is defined as the ability of a supply chain to respond purposefully within an appropriate timeframe to customer requests or supply changes in the marketplace (Shekarian et al., 2020). Generally speaking, responsiveness to changes in demand and supply is reflected in multiple aspects (Williams et al., 2013), including volume flexibility, variety flexibility, product/service modification flexibility, new product flexibility, and so on. According to Singh and Sharma (2014), a responsive supply chain could ensure a reduction in lead time, the right service quality, the right service quantity, and the on-time response to requirements of customers. Roh et al. (2014) asserted that the goal of a responsive supply chain design is to provide customers with the right product at the right place in the right length of time. The recent literature and practice show that effective measures to improve the responsiveness of supply chains include signing flexible contracts with suppliers, forecasting the trend of future demand and supply, conducting multi-source procurement, making centralized decision-making, and investing in digital technologies. Resilience is defined as the ability to mitigate the negative effects, rapidly accommodate, and react to a supply chain disruption (Kim et al., 2015). Wieland and Durach (2021) stated that resilience does not just relate to the ability of a system to bounce back after a disruptive event but also to the capacity to adapt and transform. Combining these perspectives, resilience generally refers to the ability to absorb or cushion against damage or loss, as well as the ability to rapidly recover from a disruption (Hora & Klassen, 2013). Thus, resilience is dynamic instead of static, which is considered as a fundamental attribute that supply chains need to adopt for maintaining stable growth in the face of external disruptions (Essuman et al., 2020) such as the COVID-19 pandemic. Supply chain resilience consists of multiple constituent elements, including stability, agility, robustness, collaboration, redundancy, centralization, visibility, and information sharing (Hosseini et al., 2019). Tukamuhabwa et al. (2015) emphasized the importance of building collaborative relationships in improving supply chain resilience. Other measures include maintaining slack resources, adopting a flexible production strategy, and building a risk-management infrastructure (Ambulkar et al., 2015; Modi & Mishra, 2011). Although 2Rs are the key attributes for enterprises to improve supply chain performance, there is limited research on how to develop responsive and resilient strategies to deal with long-lasting, structural, and global impacts. Previous studies mainly focused on normal situations, in which supply chain integration and big data analytics are the bases of building 2Rs supply chains. Supply chain integration is the ability to integrate all activities among a company's internal functions and external partners (supplier, distributor, retailer, etc.), until the finished product arrives at the end customer (Zhao et al., 2013). From the perspective of collaborative partners, supply chain integration could be divided by horizontal strategies and vertical strategies (Mesquita & Lazzarini, 2008), while the vertical integration is further divided by integration with suppliers (also known as upstream integration) and integration with customers (also known as downstream integration). In the era of Industry 4.0, supply chain integration consists of three dimensions: process and activity integration, technology and system integration, and organizational relationship linkages (Tiwari, 2020). A recent editorial in JOM (Browning, 2020) discussed related aspects of organizational and process integration. In recent years, under the influence of economic globalization, supply chains have been transformed. Since globalization has become pervasive, suppliers have pursued global markets, and most companies source extensively from global suppliers (Cohen & Lee, 2020). This has led to an increase in outsourcing activities and a corresponding decline in vertical integration of supply chains. As a result, supply chain networks have become flatter and more complex, composed of different organizations dispersed across multiple tiers and different geographies, and extended beyond a single country's boundaries (Choi & Hong, 2002). Global supply chains are characterized by focal firms that distribute across multiple countries, locate production facilities abroad, or source from offshore suppliers. Munir et al. (2020) showed that integration in the global supply chain could increase companies' resilience in making flexible deliveries and the number of products. Big data refers to data that arrive at a high volume and velocity with considerable variation, while analytics refers to the ability to gain insights from data via statistics, learning, optimization, or other techniques. The applications of big data and analytics are closely interlinked to enable firms to make better decisions. Hence, prior literature has typically discussed them together as big data analytics, which allows the use of advanced computing techniques, strategies and architectures to store, extract, and analyze multi-source, heterogeneous data to support decisions, and has been commonly used in operations management (Wamba et al., 2015). In the era of economic globalization, supply chain management has become extremely complex, with large-scale and online decision-making challenges emerging (Yang et al., 2021), for example, the joint decision-making between proactive planning and reactive operations in the forms of demand forecasting, production planning, inventory management, supply allocation, transportation, and distribution. It is no longer efficient to rely on traditional analytics methods. Many firms have been exploring how to take big data analytics to promote lean and agile activities in supply chain management (Baruffaldi et al., 2019). Existing studies have shown that applications of digital technologies can help improve supply chain performance by enhancing visibility and reducing supply chain risks (Govindan et al., 2018). The digitalization of supply chains produces large volumes of data, which is regarded as a new kind of resource and has the potential to create value and enhance competitiveness. Singh and El-Kassar (2019) proposed that digital technologies have transformed traditional supply chain management into a more data-driven approach, which requires a much higher level of big data analytics capabilities compared to traditional supply chain management. Following the call for papers, the submission of 114 manuscripts, and the review and revision process, seven articles were selected for this special issue that contribute to our understanding of the impact of COVID-19 on supply chains and its effect on addressing the 2Rs. In “Strengthening supply chain resilience during COVID-19: A case study of JD.com” (Shen & Sun, 2023), the authors used quantitative operational data obtained from JD.com4 to analyze the impact of the pandemic on supply chain resilience. They described the challenging scenarios that retailing supply chains experienced in China and the practical response of JD.com over the course of the pandemic the pandemic. JD.com was observed to respond well to the exceptional demand and severe logistical disruptions caused by COVID-19 in China based on its highly integrated supply chain structure (including both process and activity integration and technology and system integration) and comprehensive digital technologies. In particular, the existing, intelligent platforms and delivery procedures were modified slightly but promptly to deal with specific disruptions. The joint efforts of multiple firms, the government, and the entire Chinese society contributed to surmounting the challenges. The experience of JD.com contributes to understanding of the value of investing in operational flexibility and beyond-supply-chain collaboration given the possibility of large-scale supply chain disruptions such as the COVID-19 outbreak. In “Breaking out of the pandemic: How can firms match internal competence with external resources to shape operational resilience?” (Li et al., 2023), the authors explored how firms sought to effectively combine internal competence with external resources from the supply chain network to improve operational flexibility and stability during the COVID-19 pandemic. The internal flexibility refers to product diversity, the internal stability refers to operational efficiency, the external flexibility refers to structural holes, and the external stability refers to network centrality. Drawing upon matching theory, the authors provided an internal-external combinative perspective to explain operational mechanisms underlying different matchings. Based on the empirical results of 2994 unique firms and 5293 observations, they found that more heterogeneous combinations between internal (external) flexibility and external (internal) stability may result in a complementary effect that enhances operational resilience, whereas more homogeneous combinations between internal flexibility (or stability) and external flexibility (or stability) may have a substitutive effect that reduces operational resilience. With the COVID-19 pandemic having had a significant impact on supply chains, government initiatives have played a central role in managing the crisis. In “The impact of governmental COVID-19 measures on manufacturers' stock market valuations: The role of labor intensity and operational slack” (Chen et al., 2023), the authors investigated the impact of the Chinese government's Level I emergency-response policy (Ge et al., 2020) on manufacturers' stock-market values, and the role of manufacturers' operational slack on adding resilience. Specifically, through an event study of 1357 Chinese manufacturing companies listed on the Shenzhen Stock Exchange, the authors found that the government's emergency-response policy triggered a statistically significant positive reaction from the stock market for manufacturers. However, the authors also found negative impacts on stock market values for manufacturers in labor-intensive industries because of the labor immobility triggered by the Level I measures. In addition, this article identified the positive role of operational slack in the form of financial slack and excess inventory in helping to maintain operations and business continuity, mitigate risks caused by the labor mobility restrictions, and improve supply chain resilience, which identifies operational slack as a supply chain resilience strategy to mitigate pandemic-related risks. When the COVID-19 pandemic broke out, the medical-product industry faced unprecedented demand shocks for personal protective equipment, including face masks, face shields, disinfectants, and gowns. Companies from various industries responded to the urgent need for these potentially life-saving products by adopting ad hoc supply chains in an exceptionally short time. In “Realizing supply chain agility under time pressure: Ad hoc supply chains during the COVID-19 pandemic” (Müller et al., 2023), the authors explored the use by 34 German companies of ad hoc supply chains to produce personal protective equipment. From these cases, the authors developed an emergent theoretical model of ad hoc supply chains around enablers of supply chain agility such as dynamic capabilities (the ability to integrate, build, and reconfigure internal and external competences to address rapidly scenario changes), entrepreneurial orientation (proactiveness, risk-taking, innovativeness, autonomy, and competitive aggressiveness), and temporary orientation (speedy action in a limited time). To cope with the COVID-19 crisis, many firms allowed their employees to work from home (WFH). In “Working from home and firm resilience to the COVID-19 pandemic” (Ge et al., 2023), the authors examined whether a firm's WFH capacity increased its resilience. The authors put forward and tested a unique data set that combines listed firms' financial data, epidemiological data, and online job postings data from China. They found that imposing COVID-19 anti-contagion policies on firms and their suppliers or customers significantly increased their operating revenue volatility, slowed their recovery, and had repercussions on their supply chains. WFH enhanced firms' resistance capacity by reducing the effect of COVID-19 on their operating revenue volatility and disruptions to their supply chain partners; however, it also decreased their recovery capacity by extending the time taken to return to normal. Firm attributes, along with workers' occupations, education, and experience, impacted the effect of WFH on firm resilience. This article enhances our understanding of shock transmission across supply chains and identifies WFH as a source of firm resilience. In “Developing supply chain resilience through integration: An empirical study on an e-commerce platform” (Qi et al., 2023), the authors developed a framework that described the impact on supply chain resilience of process and activity integration between an e-commerce platform and suppliers. An analysis of data from a Chinese e-commerce platform found that integration between the e-commerce platform and suppliers in terms of information sharing, joint planning, and logistics cooperation had positive impact on supply chain resilience, while procurement automation had the opposite effect. Manufacturing flexibility positively moderated the impact of information sharing, joint planning, and logistics cooperation. The results contribute to understanding of the factors that encourage the development of supply chain resilience, suggesting that the relationship between integration and resilience is best examined within a contingency framework. There is ongoing debate about whether a firm should develop a concentrated supply chain, with the literature reporting both benefits and drawbacks. In “Opportunities or constraints? A network embeddedness perspective on the role of supply chain concentration during the pandemic” (Jiang et al., 2023), the authors examined this puzzle. Drawing on the interdependence perspective, the authors investigated how customer and supplier concentration affected supply chain resilience during the disruption and recovery stages of the pandemic, where customer concentration refers to the extent to which a firm's sales are dependent on a few major customers, and supplier concentration refers to the extent to which a firm's purchases are from a few major suppliers. The analysis of 26,488 firm-quarter observations from 2366 Chinese-listed manufacturing firms revealed that concentration can be both detrimental and beneficial, contingent on the exchange role and the crisis stage. To be specific, customer concentration accentuated the downside of firm productivity in the disruption stage but facilitated productivity restoration in the recovery stage, while supplier concentration had no significant impact on productivity in the disruption stage but hindered firm productivity from bouncing back in the recovery stage. The guest editorial team (Xiang Li, Xiande Zhao, Hau L. Lee, and Chris Voss) would like to thank the co-editors-in-chief of the Journal of Operations Management, Suzanne de Treville and Tyson Browning, for their support. The authors would also like to acknowledge all of the many reviewers and associate editors for their extensive reviews and expert advice on the large number of articles submitted to this special issue. This work was supported by the National Natural Science Foundation of China (Nos. 71931001, 71722007). The research of Prof. Zhao was partially suported by the Major Program of National Social Science Foundation of China (22&ZD082).

The semiconductor firm Xilinx uses two different postponement strategies: product postponement and process postponement. In product postponement, the products are designed so that the product's specific functionality is not set until after the customer receives it. Xilinx designed its products to be programmable, allowing customers to fully configure the function of the integrated circuit using software. In process postponement, a generic part is created in the initial stages of the manufacturing process. In the later stages, this generic part is customized to create the finished product. Xilinx manufactures a small number of generic parts and holds them in inventory. The use of these generic parts allows Xilinx to hold less inventory in those finished products that it builds to stock. And for some finished products, Xilinx can perform the customization steps quickly enough to allow it to build to order.

Abstract We describe our experience of developing models in which the principles of design for supply chain management (DFCM) have been implemented for new product development at Hewlett-Packard Company (HP). This experience arises from the development of a new product that is scheduled to be released in 1995. A key design decision faced by the product development team was whether to use a universal module or regionally dedicated modules to satisfy global market requirements. We describe a wide range of factors—including manufacturing and logistics costs—that could be used to support the design decision; these factors associated with product and process design contribute to tolal supply chain costs. We review the analytical model used to evaluate the cost and service implications of the two design alternatives. Finally, we discuss qualitative considerations that might influence the eventual decisions as well as the lessons learned from this real world experience. Keywords: supply chain managementproduct designuniversalitystandardizationinventory management

Since Clark and Scarf's pioneering work, most advances in multiechelon inventory systems have been based on demand processes that are time independent. This paper revisits the serial multiechelon inventory system of Clark and Scarf and develops three key results. First, we provide a simple lower-bound approximation to the optimal echelon inventory levels and an upper bound to the total system cost for the basic model of Clark and Scarf. Second, we show that the structure of the optimal stocking policy of Clark and Scarf holds under time-correlated demand processes using a Martingale model of forecast evolution. Third, we extend the approximation to the time-correlated demand process and study, in particular for an autoregressive demand model, the impact of lead times and autocorrelation on the performance of the serial inventory system.

This paper deals with the lot sizing problem in which the key features of imperfections in a production process are explicitly modelled. These features include: process shifting to out-of-control states, detection of the out-of-control shifts, corrective actions following the detections, and the fixed setup and variable processing times of reworks. The problem is motivated by the wafer probe operation in semiconductor manufacturing. The key objective that drives the lot sizing decision is to reduce the total processing time on a critical resource. Such an objective is aimed at reducing the congestion level at this resource.

Abstract In this paper we present a model for multi-echelon repairable systems with batch ordering policy at the bases. Such an ordering policy is desirable when demand rates and/or the set up cost for ordering are relatively high. Operating characteristics of such a system are analyzed. A two-parameter approximation scheme for the distribution of the number of orders outstanding and consequently backorders is developed, and its performance evaluated. It is found that, under a variety of scenarios including both finite and infinite servers at the repair facility, the approximation scheme is very effective in providing a relatively simple means to determine the stocking levels at both the depot and the bases to minimize the cost of inventory holding and backorders. For the special case when the batch size is one, we confirm the effectiveness of the use of the negative binomial distribution as an approximation for the distribution of orders outstanding, as suggested by recent studies, under a much more general setting.

Supply chain management is all about software and systems, right? Put in the best technology, sit back, and watch as your processes run smoothly and the savings roll in? Apparently not. When HBR convened a panel of leading thinkers in the field of supply chain management, technology was not top of mind. People and relationships were the dominant issues of the day. The opportunities and problems created by globalization, for example, are requiring companies to establish relationships with new types of suppliers. The ever-present pressure for speed and cost containment is making it even more important to break down stubbornly high internal barriers and establish more effective cross-functional relationships. The costs of failure have never been higher. The leading supply chain performers are applying new technology, new innovations, and process thinking to far greater advantage than the laggards, reaping tremendous gains in all the variables that affect shareholder value: cost, customer service, asset productivity, and revenue generation. And the gap between the leaders and the losers is growing in almost every industry. This roundtable gathered many of the leading thinkers and doers in the field of supply chain management, including practitioners Scott Beth of Intuit, Sandra Morris of Intel, and Chris Gopal of Unisys. David Burt of the University of San Diego and Stanford's Hau Lee bring the latest research from academia. Accenture's William Copacino and the Warren Company's Robert Porter Lynch offer the consultant's perspectives. Together, they take a wide-ranging view of such topics as developing talent, the role of the chief executive, and the latest technologies, exploring both the tactical and the strategic in the current state of supply chain management.

Naval Research Logistics (NRL)Volume 34, Issue 3 p. 365-380 Article Operating characteristics of a two-echelon inventory system for repairable and consumable items under batch ordering and shipment policy Hau L. Lee, Hau L. Lee Department of Industrial Engineering and Engineering Management, Stanford University, Stanford, California 94305Search for more papers by this authorKamran Moinzadeh, Kamran Moinzadeh Department of Management Science, University of Washington, Seattle, Washington 98195Search for more papers by this author Hau L. Lee, Hau L. Lee Department of Industrial Engineering and Engineering Management, Stanford University, Stanford, California 94305Search for more papers by this authorKamran Moinzadeh, Kamran Moinzadeh Department of Management Science, University of Washington, Seattle, Washington 98195Search for more papers by this author First published: June 1987 https://doi.org/10.1002/1520-6750(198706)34:3<365::AID-NAV3220340305>3.0.CO;2-PCitations: 59AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract In this article we have generalized previous models on multiechelon recoverable inventory systems to cover the cases of batch ordering and shipment policy, and when items can either be repaired or condemned. The batch ordering and shipment policy is appropriate when the setup cost for shipment and order and/or the demand rates of the items are relatively high. The operating characteristics of such a system have been studied. Specifically, the probability distribution of backorder levels at the bases are analyzed for different repair-time distributions. An approximation scheme is proposed for this distribution, and is evaluated using extensive simulation results. The results indicate that the scheme is very effective in providing near-optimal stocking levels in such a system. Citing Literature Volume34, Issue3June 1987Pages 365-380 RelatedInformation

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.

Abstract Continuous or periodic inspection policies are often used to monitor the maintenance of a production process. In this paper, a comparative study of continuous and periodic inspection policies in deteriorating production systems is provided. The overall cost function is comprised of: set-up, inspection, inventory holding, defective and restoration costs. Different models, constant, linear and exponential, of restoration costs are considered. Tradeoffs between the continuous and the periodic inspection policies are analyzed, and ranges of values of system or cost parameters for which the continuous inspection policy is preferred are established.

This article presents a series of models for explaining hospital utilization. The models belong to the multinomial logit class, which relate the probability of hospital selection to factors that include travel time between patients and hospitals, hospital attractiveness factors, physician characteristics, and patient characteristics. Separate models for different population groups (sex, age, socioeconomic classes) and medical services (general medicine, obstetrics, pediatrics, psychiatry, and general surgery) are estimated and compared with the general hospital model. The results, which are based on an extensive data base and new estimation procedures, are superior to those of past studies. A number of differences between utilization patterns for the various populations are also observed.

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Cloud computing has been a rising trend in the business world. In this study, we consider two most important pricing schemes offered to sustained customers by major service providers in the cloud industry: the reservation‐based scheme (the R‐scheme) by Amazon or Microsoft, and the utilization‐based scheme (the U‐scheme) by Google. We consider a duopoly model with heterogeneous customers characterized by the mean and the coefficient of variation of their usage. We show that under either pricing scheme, the effective price is essentially an increasing function of the coefficient of variation of usage, and thus both schemes aim for rewarding stability in usage. However, when the providers adopt different schemes, we show that customers with lower demand volatility would prefer the R‐scheme, while those with higher demand volatility would prefer the U‐scheme. Furthermore, we study the impact of evolving market characteristics, including the distributions of market preference, demand size, and demand volatility, as well as the impact of the providers’ service levels on their choices of schemes and decisions on the pricing parameters. We find that if the market has a stronger preference for a particular provider or that provider has a higher service level than its competitor, the provider is more likely to adopt the R‐scheme, while its competitor's adoption of a scheme depends on the extent of the price competition. Specifically, when the diversity of customer preference becomes higher (lower), the price competition becomes softened (intensified), and the competitor is more likely to adopt the R‐scheme (U‐scheme, respectively).

Given the promise of three‐dimensional (3D) printing, also known as additive manufacturing, some innovative consumer goods companies have started to experiment with such a technology for on‐demand production. In this study, we consider two adoption cases of 3D printing in a dual‐channel (i.e., online and in‐store) retail setting, and evaluate its impact on a firm’s product offering, pricing, and inventory decisions. Our analysis uncovers the following effects of 3D printing. First, 3D printing at the factory has the substitution effect of technological innovation for online demands, as 3D printing replaces the traditional mode of production. Such technology substitution not only leads to increased product variety offered online, which allows the firm to charge a price premium for online customers, but also induces the firm to offer a smaller product variety and a reduced price in‐store. There is an additional environmental benefit when more customers are steered from the in‐store channel to the online channel. Second, when 3D printing is used in‐store as well, in addition to the substitution effect, the firm also achieves a structural effect due to the fundamental change in the supply chain structure. Since the in‐store demand is served in a build‐to‐order fashion, the firm achieves postponement benefits in inventory management. The environmental benefit is the most significant in this case. Moreover, using 3D printing in‐store will require a new supplier–retailer relationship. We find that cost‐sharing contracts can coordinate the supply chains where 3D printing is used in‐store and the supplier controls the raw material inventory.

Successful vendor-vendee relationship is viewed as an important ingredient for maintaining competitiveness in the current marketplace. This calls for a careful and comprehensive approach in selecting vendors. The cost of quality (or better phrased as the cost of “unquality”) resulted from imperfections of a vendor's incoming input materials is one component of the total costs in the evaluation of vendors. The purpose of this paper is to show that looking at only one dimension of the quality cost is not sufficient and to highlight the importance of having a high quality internal process. We explore the relationship between the vendor's quality cost, the vendor's input quality, and the imperfections of the manufacturing process. We analyze the properties of the resulting quality cost model, and draw managerial implications in the selection of vendors.

Production and Operations ManagementVolume 6, Issue 3 p. 191-192 INTRODUCTION TO THE SPECIAL ISSUE ON GLOBAL SUPPLY CHAIN MANAGEMENT Hau L. Lee, Hau L. Lee Department of Industrial Engineering and Engineering Management, Stanford University, Stanford, California 94305, USASearch for more papers by this authorShu Ming Ng, Shu Ming Ng Department of Information and Systems Management, Hong Kong Universio of Science and Technology, Clear Water Bay, Kowloon, Hong KongSearch for more papers by this author Hau L. Lee, Hau L. Lee Department of Industrial Engineering and Engineering Management, Stanford University, Stanford, California 94305, USASearch for more papers by this authorShu Ming Ng, Shu Ming Ng Department of Information and Systems Management, Hong Kong Universio of Science and Technology, Clear Water Bay, Kowloon, Hong KongSearch for more papers by this author First published: 05 January 2009 https://doi.org/10.1111/j.1937-5956.1997.tb00425.xCitations: 56AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume6, Issue3September 1997Pages 191-192 RelatedInformation

This chapter discusses the analytical models for evaluating the postponement alternatives. The chapter introduces the three key postponement enablers: process standardization, process resequencing, and component standardization. The industry applications utilizing these enablers are described. Various other techniques for managing product variety, such as modularity and downward substitution are reviewed and the additional benefits of postponement in pricing and information processing are examined. Postponement can be enabled through changes in the manufacturing distribution process or the product architecture. "Process standardization" refers to standardizing the initial steps in the process across the product line so that products are not differentiated at these steps and distinct personalities of the products are added at a later stage. All the products in the product line are processed through several standard steps. A complementary approach to process standardization is process resequencing.

SUBTITLE: SUPPLY CHAIN MANAGERS TODAY FACE A DILEMMA: HOW DO YOU IMPROVE SECURITY WITHOUT JEOPARDIZING SUPPLY CHAIN EFFECTIVENESS? THE ANSWER MAY LIE IN THE PRINCIPLES OF THE MOVEMENT. TOTAL MANAGEMENT TAUGHT US THAT WE CAN DECREASE DEFECTS WITHOUT INCREASING COSTS -- OR ACHIEVE QUALITY WITHOUT TEARS. BY APPLYING THESE LESSONS, WE MAY BE ABLE TO CREATE STRATEGIES THAT BOTH PREVENT AND MITIGATE SECURITY BREACHES WHILE ALSO STRENGTHENING PRODUCTIVITY.

Two operational formats namely mass customization and mass production can be implemented to satisfy customer preference-based demand. The mass customization system consists of two stages: the initial build-to-stock phase and the final customize-to-order phase. The mass production system has a single stage: building products with pre-determined specifications to stock. In each case, the company makes decisions on the number of initial product variants, product specifications, production quantities and product pricing. Under a uniform customer preference distribution, the optimal number of base-product variants resembles the well known economic order quantity solution, and the optimal product specifications are equally spaced. We characterize three possible benefits of mass customization: (i) the gained surplus from offering each customer her ideal product; (ii) extra revenue from price discrimination; and (iii) reduced costs due to risk pooling under stochastic demand.

We study a supply chain involving a supplier–retailer relationship. When production lead-time is long and the selling season is short, the retailer has to place an order ahead of the season, which resembles the classical Newsvendor model. However, we consider the situation when the supplier agrees to deliver the order in multiple shipments in the season, and then the retailer needs to determine the quantity and/or timing of each shipment. Under a centralized setting, we derive the optimal quantity and/or timing decisions of the retailer. Under a decentralized setting, incentive misalignment arises from ineffective allocation of inventory costs between the parties, in addition to the well- known double marginalization effect. Hence, we devise an incentive contract, which involves a risk-sharing mechanism at the end of the season and an inventory subsidizing scheme for the entire season; in practice, the inventory subsidizing scheme can be implemented in different ways, such as a direct subsidizing scheme or a delayed-payment scheme. The proposed contract can achieve channel coordination and Pareto optimality. Furthermore, we can show that the inventory subsidizing scheme plays a key role in channel coordination because without the inventory subsidizing scheme, the loss of supply chain efficiency is almost always significant.

Problem definition: The paper focuses on an innovative bank-intermediated trade finance contract, which we call dynamic trade finance (DTF, under which banks dynamically adjust loan interest rates as an order passes through different steps in the trade process). We examine the value of DTF, the impact of process uncertainties, and the associated information frictions on this value and the strategic interaction between DTF and FinTech. Academic/practical relevance: As more than 30% of global trade involves bank-intermediated trade finance, examining contract innovation in trade finance (DTF) and its strategic interaction with FinTech is of practical importance. Also, analyzing trade finance in the presence of process dynamics and information frictions complements the existing academic literature. Methodology: We construct a parsimonious model of a supply chain process consisting of two steps. The duration of each step is uncertain, and the process may fail at either step. Information delay may also occur when verifying the process passing a step. The seller borrows from a bank to finance this two-step process either through uniform financing (the interest rate remains constant throughout the process) or DTF (the interest rates are adjusted according to a precommitted schedule as the process passes each step). When lending, the bank faces a regulatory capital requirement (the bank is required to hold capital reserve when issuing risky loans) or information asymmetry (the seller/borrower possesses more accurate information about the trade process than the bank). Results: The value of DTF lies in its ability to reduce transactional deadweight loss (under the regulatory capital requirement) and screening (separate high-quality borrowers from the low-quality ones under information asymmetry). This value is greater for more reliable or lengthier trade processes, yet DTF’s ability to screen is stronger when the process is less reliable. The severity of information delay hurts the value of DTF convexly. FinTech that expedites information transmission and verification and enables automatic execution complements DTF, and those that segment customers more efficiently could substitute DTF. Managerial implications: Our results shed light on how the underlying trade process dynamics and the type of information frictions involved affect the optimal deployment of contract innovations (DTF) and FinTech in trade finance.

Companies' financial strength and market position depend on successful new product introductions, which, in turn, depend on successful product rollovers. Given the low success rate of product rollovers, companies need a formal process to plan and coordinate product rollovers and to reduce risk. This article presents a framework to help companies manage product rollovers, choose the best rollover strategy, and improve product rollovers. Companies need to plan their rollovers early, when they are planning the new product's introduction. First, they choose a primary rollover strategy, based in part on assessment of the uncertainties associated with the product's manufacturing, delivery, and market potential. Then they monitor product and market conditions. Finally, as product and market conditions change, they adopt a contingency strategy if necessary. Companies can consider two primary strategies for product rollovers. Solo-product roll, a high-risk, high-return strategy, aims to have all the old products sold out worldwide at the planned new product introduction date. The less risky dual-product roll plans to sell both old and new products simultaneously for a period of time and can be implemented in a variety of ways. If changed product and market conditions increase the product's risk, companies can choose from among four contingency strategies: making significant price markdowns, postponing the new product's introduction, introducing the new product earlier than planned, or combining two or more dual-product-roll strategies. Finally, while contingency strategies enable companies to modify their primary strategies if appropriate, companies can improve their product rollovers significantly by exploiting opportunities to reduce the product and market risks of each new product in the first place.

Naval Research Logistics (NRL)Volume 39, Issue 4 p. 561-577 Article Multi-item service constrained (s, S) policies for spare parts logistics systems Morris A. Cohen, Morris A. Cohen The Wharton School, University of PennsylvaniaSearch for more papers by this authorPaul R. Kleindorfer, Paul R. Kleindorfer The Wharton School, University of PennsylvaniaSearch for more papers by this authorHau L. Lee, Hau L. Lee Industrial Engineering, Stanford UniversitySearch for more papers by this authorDavid F. Pyke, David F. Pyke The Amos Tuck School, Dartmouth CollegeSearch for more papers by this author Morris A. Cohen, Morris A. Cohen The Wharton School, University of PennsylvaniaSearch for more papers by this authorPaul R. Kleindorfer, Paul R. Kleindorfer The Wharton School, University of PennsylvaniaSearch for more papers by this authorHau L. Lee, Hau L. Lee Industrial Engineering, Stanford UniversitySearch for more papers by this authorDavid F. Pyke, David F. Pyke The Amos Tuck School, Dartmouth CollegeSearch for more papers by this author First published: June 1992 https://doi.org/10.1002/1520-6750(199206)39:4<561::AID-NAV3220390409>3.0.CO;2-5Citations: 38AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Many organizations providing service support for products or families of products must allocate inventory investment among the parts (or, identically, items) that make up those products or families. The allocation decision is crucial in today's competitive environment in which rapid response and low levels of inventory are both required for providing competitive levels of customer service in marketing a firm's products. This is particularly important in high-tech industries, such as computers, military equipment, and consumer appliances. Such rapid response typically implies regional and local distribution points for final products and for spare parts for repairs. In this article we fix attention on a given product or product family at a single location. This single-location problem is the basic building block of multi-echelon inventory systems based on level-by-level decomposition, and our modeling approach is developed with this application in mind. The product consists of field-replaceable units (i.e., parts), which are to be stocked as spares for field service repair. We assume that each part will be stocked at each location according to an (s, S) stocking policy. Moreover, we distinguish two classes of demand at each location: customer (or emergency) demand and normal replenishment demand from lower levels in the multiechelon system. The basic problem of interest is to determine the appropriate policies (si Si) for each part i in the product under consideration. We formulate an approximate cost function and service level constraint, and we present a greedy heuristic algorithm for solving the resulting approximate constrained optimization problem. We present experimental results showing that the heuristics developed have good cost performance relative to optimal. We also discuss extensions to the multiproduct component commonality problem. Citing Literature Volume39, Issue4June 1992Pages 561-577 RelatedInformation

This chapter focuses on the mathematical models for controlling the inventory of a single product. Models with constant demand rates, models with deterministic time-varying demand, periodic review stochastic demand models; and continuous review stochastic demand models have been discussed. Various applications of inventory theory in industry have been presented. In models with constant demand rates, a class of models that is based on the simplest demand assumption—that is, demand is deterministic and stationary—has been discussed. The chapter presents an overview of the major developments and results for single product inventory management. The majority of research in inventory has been focused on stochastic demand models. Because of their importance, one will consider separately discrete time and continuous time cases. When the inventory system is monitored on a continuous time basis, the analysis of such system requires the specification of the demand process to the system.

Naval Research Logistics (NRL)Volume 34, Issue 4 p. 457-467 Article A continuous-review inventory model with constant resupply time and defective items Kamran Moinzadeh, Kamran Moinzadeh Department of Management Science, University of Washington, Seattle, Washington 98195Search for more papers by this authorHau L. Lee, Hau L. Lee Department of Industrial Engineering and Engineering Management, Stanford University, Stanford, California 94305Search for more papers by this author Kamran Moinzadeh, Kamran Moinzadeh Department of Management Science, University of Washington, Seattle, Washington 98195Search for more papers by this authorHau L. Lee, Hau L. Lee Department of Industrial Engineering and Engineering Management, Stanford University, Stanford, California 94305Search for more papers by this author First published: August 1987 https://doi.org/10.1002/1520-6750(198708)34:4<457::AID-NAV3220340402>3.0.CO;2-OCitations: 27AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract As a result of imperfect production and inspection by suppliers, pilferage, and/or damage in transit, it is common that procurement orders may contain defective items. This article deals with a continuous-review inventory system with Poisson demand arrivals and constant resupply time. Items in resupply lots may not be of perfect quality. The operating characteristics of such a system are analyzed. For purposes of computational savings, an approximation scheme for the operating characteristics is presented. As a result of the approximation, the determination of the optimal ordering policy becomes much simpler. Extensive numerical tests suggest that the approximation scheme is very effective in giving the optimal or near-optimal ordering policies for such a system. Citing Literature Volume34, Issue4August 1987Pages 457-467 RelatedInformation

Journal of Supply Chain ManagementVolume 46, Issue 2 p. 5-29 A PROCESS ANALYSIS OF GLOBAL TRADE MANAGEMENT: AN INDUCTIVE APPROACH† WARREN H. HAUSMAN, WARREN H. HAUSMAN Stanford UniversitySearch for more papers by this authorHAU L. LEE, HAU L. LEE Stanford UniversitySearch for more papers by this authorGRAHAM R. F. NAPIER, GRAHAM R. F. NAPIER TradeBeam Inc.Search for more papers by this authorALEX THOMPSON, ALEX THOMPSON TradeBeam Inc.Search for more papers by this authorYANCHONG ZHENG, YANCHONG ZHENG Stanford UniversitySearch for more papers by this author WARREN H. HAUSMAN, WARREN H. HAUSMAN Stanford UniversitySearch for more papers by this authorHAU L. LEE, HAU L. LEE Stanford UniversitySearch for more papers by this authorGRAHAM R. F. NAPIER, GRAHAM R. F. NAPIER TradeBeam Inc.Search for more papers by this authorALEX THOMPSON, ALEX THOMPSON TradeBeam Inc.Search for more papers by this authorYANCHONG ZHENG, YANCHONG ZHENG Stanford UniversitySearch for more papers by this author First published: 26 March 2010 https://doi.org/10.1111/j.1745-493X.2010.03187.xCitations: 30 † Acknowledgments: We gratefully acknowledge generous research support from TradeBeam, Inc. We also acknowledge valuable research assistance provided by Yan (Tracy) Liu of TradeBeam. We also thank David Wortman, formerly at Neiman Marcus Corporation, Ed Feitzinger, formerly at Golden Gate Logistics, Richard Gervais and team from Axxess International Forwarders, Ross Stores, Redcats USA, Pottery Barn, Simon Oxley, Integrated Distribution Services, K.L. Lee of Esquel, Tom Haugen of Li & Fung Limited, Frank Reynolds of International Projects Inc., Wachovia Global Trade Services and the many individuals from TradeBeam in the United States and China who provided much of the data input for our models. We also acknowledge very beneficial comments from several referees and the associate editor and the co-editor-in-chief on an earlier version of the paper. ‡ *Like all invited papers and invited notes, the original version of this manuscript underwent a double-blind review process. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract This paper describes a new, detailed process model for Global Trade Management (GTM) that contains sufficient detail on cross-border trade processes to estimate the benefits of Information Technology-Enabled Global Trade Management (IT-GTM). Our methodology combines a grounded theory approach with data analysis and analytical modeling. GTM describes the processes required to support cross-border transactions between importers, exporters, their trading partners and governments. IT-GTM is the set of information technologies and software solutions that can be used by companies to carry out their global trading processes in a streamlined manner. We collect data on time reductions for individual trade process steps using IT-GTM and use Critical Path Analysis to calculate the resulting improvements in key metrics such as the Manufacture to Invoice Cycle and Days Sales Outstanding for exporters, and the Order to Receipt Cycle for importers. Under reasonably conservative scenarios the gross savings from IT-GTM amount to 1.7 percent and 0.6 percent of annual sales for exporters and importers, respectively. Citing Literature Volume46, Issue2April 2010Pages 5-29 RelatedInformation

Naval Research Logistics (NRL)Volume 42, Issue 1 p. 57-80 Article Expressions for item fill rates in periodic inventory systems M. Eric Johnson, M. Eric Johnson Owen Graduate School of Management, Vanderbilt University, Nashville, Tennessee 37203Search for more papers by this authorHau L. Lee, Hau L. Lee Department of Industrial Engineering and Engineering Management, Stanford University, Stanford, California 94305Search for more papers by this authorTom Davis, Tom Davis Strategic Planning and Modeling, Hewlett-Packard Company, Palo Alto, California 94304Search for more papers by this authorRobert Hall, Robert Hall Strategic Planning and Modeling, Hewlett-Packard Company, Palo Alto, California 94304Search for more papers by this author M. Eric Johnson, M. Eric Johnson Owen Graduate School of Management, Vanderbilt University, Nashville, Tennessee 37203Search for more papers by this authorHau L. Lee, Hau L. Lee Department of Industrial Engineering and Engineering Management, Stanford University, Stanford, California 94305Search for more papers by this authorTom Davis, Tom Davis Strategic Planning and Modeling, Hewlett-Packard Company, Palo Alto, California 94304Search for more papers by this authorRobert Hall, Robert Hall Strategic Planning and Modeling, Hewlett-Packard Company, Palo Alto, California 94304Search for more papers by this author First published: February 1995 https://doi.org/10.1002/1520-6750(199502)42:1<57::AID-NAV3220420107>3.0.CO;2-2Citations: 35AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract We examine the problem of estimating the item fill rate in a periodic inventory system. We show that the traditional expressions for line item fill rate, found in many operations management textbooks, perform well for high fill rates (above 90%), but they consistently underestimate the true fill rate. The problem of underestimation becomes significant as the fill rate falls below 90% and is greatly amplified in cases with very low fill rates (below 50%). We review other more accurate expressions for fill rate, discussing their relative merits. We then develop an exact fill rate expression that is robust for both high and low fill rates. We compare the new expression to others found in the literature via an extensive set of simulation experiments using data that reflect actual inventory systems found at Hewlett-Packard. We also examine the robustness of the expressions to violations in the underlying assumptions. Finally, we develop an alternative fill rate expression that is robust for cases of high demand variability where product returns are allowed. © 1995 John Wiley & Sons, Inc. Citing Literature Volume42, Issue1February 1995Pages 57-80 RelatedInformation

Abstract Optimal inspection and ordering policies for products with imperfect qualities are considered. Two inspection policies and their effects on the economic ordering quantities are analyzed. The two inspection policies are the Blind Purchase (BP) policy and the Selective Purchase (SP) policy. Under the BP policy, products are purchased without inspection, but fraction of each shipment is inspected before they are sold to the customers. Under the SP policy, it is assumed that all items received have been inspected prior to purchase and thus are of good quality (but cost per item is usually higher under SP compared to BP). Several properties and areas of dominancy of the two inspection policies are presented.

The use of multiple control limits and multiple corresponding levels of response for processes is an effective way for statistical process control when different assignable causes exist which lead to different out-of-control states of the processes, and different restoration procedures. This paper examines the optimal economic design of such process control charts. The exact mathematical model is developed and the expected cost per time unit function is derived. The costs associated with the statistical process control are minimized by means of an optimization procedure involving a quasi-Newton method and a Fibonacci lattice search. Sensitivity analysis performed on a large number of numerical examples reveals key relationships between model parameters. A comparison between the proposed control chart and an approximate matched single-cause chart shows that the former can be a significant improvement over the latter. Several model extensions and managerial implications are also discussed.

Journal of Supply Chain ManagementVolume 46, Issue 1 p. 7-7 TAMING THE BULLWHIP† HAU L. LEE, HAU L. LEE Stanford UniversitySearch for more papers by this author HAU L. LEE, HAU L. LEE Stanford UniversitySearch for more papers by this author First published: 07 December 2009 https://doi.org/10.1111/j.1745-493X.2009.03180.xCitations: 28 † *Invited Comment Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article.Citing Literature Volume46, Issue1January 2010Pages 7-7 RelatedInformation

In this paper, a simplified model describing the stochastic process underlying the etiology of contagious and noncontagious diseases with mass screening is developed. Typical examples might include screening of tuberculosis in urban ghetto areas, venereal diseases in the sexually active, or AIDS in high risk population groups. The model is addressed to diseases which have zero or negligible latent periods. In the model, it is assumed that the reliabilities of the screening tests are constant, and independent of how long the population unit has the disease. Both tests with perfect and imperfect reliabilities are considered. It is shown that most of the results of a 1978 study by W.P. Pierskalla and J.A. Voelker for noncontagious diseases can be generalized for contagious diseases. A mathematical program for computing the optimal test choice and screening periods is presented. It is shown that the optimal screening schedule is equally spaced for tests with perfect reliability. Other properties relating to the managerial problems of screening frequencies, test selection, and resource allocation are also presented.

SUMMARY SUMMARY In this study, models for obtaining economic order quantities (EOQ) are considered which take into account the effects of advertising, price elasticity and economies of scale, and the possibility of some ordered items being defective. An earlier model by Subramanyam and Kumaraswamy (1981) is shown to be restrictive in its treatment of the effects of defective items. To correctly model there effects, three different scenarios for dealing with defective items are considered and the resulting EOQ models presented. Special cases are also considered. It is shown that the earlier model can lead to incorrect economic order quantities in these scenarios.

A behaviorally oriented correspondence course was completed by 517 subjects. All subjects received the behavioral course but were sorted into four groups: (a) behavioral instruction only, (b) recommend soup for lunch, (c) recommend soup for any meal or snack, and (d) recommend yogurt for meals or snacks. Subjects lost an average of 3.8 kg. in 10 weeks. There was a marked association between rate of eating and total caloric consumption for the meal. Eating soup as part of lunch or dinner led to both decreased consumption of kilo-calories and a slower rate of eating.

Global supply chains have delivered many benefits for consumers and firms. At the same time, they have become so integral to business that disruptions due to natural or man-made disasters can lead to major shortages in components and final products. Concurrently, social, environmental and ethical problems have arisen and continue to persist, despite government, private sector and nongovernmental efforts to address them. What are effective methods for global firms, or buyers, to gain an understanding of social and environmental problems in supply chains and to address them? We examine strategies using a sense and response framework, and find various approaches within this framework to be associated with improved performance. In particular, research suggests that using a collaborative, proactive approach with suppliers and providing meaningful incentives can be effective. There exists an opportunity to cascade these efforts to second tier suppliers and beyond, where some of the greatest impacts and opportunities to affect change lie. As discussed in Chap. 1 by Bouchery et al. (Sustainable supply chains: introduction. In: Bouchery Y, Corbett CJ, Fransoo JC (eds) Sustainable supply chains: a research-based textbook on operations and strategy. Springer, Cham, 2023), improving supply chain responsibility is not only seen as a way to mitigate a variety of risks and meet regulations, but also as a means to increase profits, either by saving costs, growing revenues via positive brand image, or doing both. In this chapter, we discuss supply chain practices associated with improved social, environmental, and in several cases, economic performance. We discuss how leading firms are complementing compliance programs, which remain important, with an increased emphasis on building shared value for various actors along the value chain.

In this paper, we consider an inventory system where orders may arrive in two shipments, that is, the first shipment of the order may contain only part of the items ordered, while the rest of the items arrive in a second shipment. However, the amount of the first shipment is random. Such situations arise when the supplier partially fills an order if it does not have sufficient stock to satisfy the amount demanded in an order, or when an arriving order contains defective items that can be returned to and replaced by the supplier at some later date. We present the operating characteristics and an approximate cost function for such a system. The properties of the approximate cost function are exploited to bound the search for the order quantity and reorder point that minimize it. Finally, the effectiveness of the approximate cost function in providing near-optimal solutions is evaluated by means of numerical examples.

The Internet has emerged as the most cost effective means of driving supply chain integration. We define e-Business as the marriage between the Internet and supply chain integration. We divide various forms of e-Business applications into three categories-e-Commerce, e-Procurement, and e-Collaboration. e-Commerce helps a network of supply chain partners to identify and respond quickly to changing customer demand captured over the Internet. e-Procurement allows companies to use the Internet for procuring direct or indirect materials, as well as handling value-added services like transportation, warehousing, customs clearing, payment, quality validation, and documentation. e-Collaboration facilitates coordination of various decisions and activities beyond transactions among the supply chainpartners over the Internet. This article studies various e-Business applications and discusses the potential of e-Business for building intelligence and optimization.