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We consider a retailer that sells a perishable product, making joint pricing and inventory ordering decisions over a finite time horizon of T periods with lost sales. Exploring a real-life data set from a leading supermarket chain, we identify several distinctive challenges faced by such a retailer that have not been jointly studied in the literature: the retailer does not have perfect information on (1) the demand-price relationship, (2) the demand noise distribution, (3) the inventory perishability rate, and (4) how the demand-price relationship changes over time. Furthermore, the demand noise distribution is nonparametric for some products but parametric for others. To tackle these challenges, we design two types of data-driven pricing and ordering (DDPO) policies for the cases of nonparametric and parametric noise distributions. Measuring performance by regret, that is, the profit loss caused by not knowing (1)–(4), we prove that the T-period regret of our DDPO policies are in the order of [Formula: see text] and [Formula: see text] in the cases of nonparametric and parametric noise distributions, respectively. These are the best achievable growth rates of regret in these settings (up to logarithmic terms). Implementing our policies in the context of the aforementioned real-life data set, we show that our approach significantly outperforms the historical decisions made by the supermarket chain. Moreover, we characterize parameter regimes that quantify the relative significance of the changing environment and product perishability. Finally, we extend our model to allow for age-dependent perishability and demand censoring and modify our policies to address these issues. This paper was accepted by David Simchi-Levi, Management Science Special Section on Data-Driven Prescriptive Analytics.

Problem definition: Considering a mix of prepositioning and local purchasing, common to cover humanitarian demands in the aftermath of a rapid-onset disaster, we propose policies to determine preposition stock. These formulations are developed in the presence of demand, budget, and local supply uncertainties and for single-items delivery. Academic/practical relevance: The immediate period aftermath of a disaster is the most crucial period during which humanitarian organizations must supply relief items to beneficiaries. Yet, because of many unknowns such as time, place, and magnitude of a disaster, supply management is a significant challenge, and these decisions are made intuitively. The features and complexities we examine have not been studied in the literature. Methodology: We derive properties of the optimal solution, identify exact solution methods, and determine approximate methods that are easy to implement. Results: We (i) characterize the interplay of supply, demand, and budget uncertainties, as well as the impact of product characteristics on optimal prepo stock levels; (ii) show in what conditions the prepo stock is a simple newsvendor solution; and (iii) discuss the value of emergency funds. Managerial implications: We show that budget level is a key determinant of the optimal policy. When it is above a threshold, inventory increases in disaster frequency and severity, but the reverse is true otherwise. When budget is limited, the rate of savings from improved forecasts is amplified (attenuated) for critical (noncritical) items, reflecting opposing directional effects of mismatch cost and cost of insufficient funding. Our model can also be used to estimate the value of initiatives to mitigate constraints on local spend (e.g., a line of credit underwritten by large donors that is available during the immediate relief period).

In today’s digital age, with the aid of the internet and data mining, many firms use vertically differentiated product bundling to influence demand to match up with inventory status, especially in industries with short product life cycles. Despite this practice, there is little understanding on how exactly the inventory dynamics impact the bundling strategy, and, in turn, how the bundling strategy affects the firm's inventory decisions. To fill this gap, we present a dynamic model to analyze the optimal joint replenishment, pricing, and bundling decisions over time. A key enabler of our analysis is a novel demand model that transfers the discrete bundling decision and the corresponding pricing decision into a continuous market share decision. We show that the optimal policy is dictated by a no-order set in each period. For items in this set, we do not place replenishment orders, because these items are overstocked. The rest of the policy parameters -- the order-up-to-levels for the items that we do order, the bundling and pricing decisions, and the bundle assembly quantity -- all depend on the overstock levels. We also characterize how the optimal bundling decision depends on item complementarity, cost structure, inventory levels, demand uncertainty, and supply responsiveness.

Problem definition: How can one adapt multiechelon inventory models to capture the cash flows generated by various payment-timing contracts? What competitive inventory policy behavior arises under these various payment-timing arrangements? Academic/practical relevance: Technology advancements are increasing the variety of payment triggers between supply chain stages. However, traditional multiechelon inventory models, which were originally developed for vertically integrated systems, do not explicitly account for payments flowing up the supply chain nor issues of payment timing between stages. Methodology: We introduce an analytical modeling methodology to incorporate financial inventory costs generated by payment-timing arrangements between stages in multiechelon inventory models. We also combine these methods with established inventory theory to study competitive inventory policies in a two-echelon system. Results: Under a class of payment-timing contracts, we show how to express payment flows at each stage in terms of standard physical inventory metrics and the demand-arrival process. We also show how to calculate the average inventory costs for each stage under given inventory policies and payment-timing contracts. In the two-echelon base-stock model, we first show that the cost of decentralization under standard wholesale price contracts is significantly driven by too much inventory at the supplier; it is not exclusively driven by too little inventory at the retailer as in the selling-to-the-newsvendor literature. We show that wholesale price contracts with a type of popular consignment payment timing still leads to too little inventory at the retailer. However, we prove there exists a wholesale price contract with partial consignment timing that can achieve the centralized inventory levels at both the supplier and the retailer. Managerial implications: Researchers can leverage our methodology to incorporate the price transfers and timing aspects of contracts in multiechelon inventory models. Our insights also help managers better understand the impact of prices and payment timing on decentralized chain behavior and performance.

We present a reproducible, objective review of research trends using text mining and citations of papers published in Manufacturing & Service Operations Management during its first 20 years whose abstracts or keywords contain capacity or inventory. The review is followed by our subjective projections on future research opportunities.

With an increasingly open global economy and advanced technologies, some third-party logistics providers (3PLs), such as Eternal Asia, have emerged as supply chain orchestrators, linking buyers with manufacturers worldwide. In addition to their traditional transportation services, these orchestrators provide procurement and financial assistance to buyers in the supply network, especially small- and medium-sized enterprises (SMEs) in developing countries. Oftentimes, the 3PLs can obtain payment delay arrangements from the financially stronger manufacturers, which in turn can be partially extended to the SME buyers, alleviating their high costs of capital. To illustrate the efficiency improvements of the aforementioned practice, we use a model to explicitly capture the cash-flow dynamics in a supply chain consisting of a manufacturer, a buyer, and a 3PL firm and explore the conditions under which this innovation benefits all parties in the supply chain so that the business model is sustainable. We characterize these conditions and show that the supply chain profit can be higher under leadership by the 3PL than by the manufacturer. The intermediary role of the 3PL is crucial, in that its benefit may vanish if the manufacturer chooses to directly grant payment delay to the buyers. We demonstrate that the benefit is more likely to occur with more buyers. We further identify the unique Nash bargaining solution for the transportation time and the payment delay grace period. The online appendix is available at https://doi.org/10.1287/msom.2017.0667 . This paper has been accepted for the Manufacturing & Service Operations Management Special Issue on Value Chain Innovations in Developing Economies.

Amid the prolonged COVID-19 pandemic, the miraculous breakthroughs of multiple effective and safe COVID-19 vaccines offer hopeful prospects. Yet, the endgame of the pandemic is not vaccines; it is vaccination. The daunting challenge of vaccinating the world offers ample investigative opportunities for management scientists who are interested in improving the efficiency and equity of vaccine supply chains. In this article, we provide a brief overview of these opportunities through three constituent parts: (1) supply, (2) demand, and (3) matching supply with demand.

We consider continuous-review assemble-to-order (ATO) systems with a general bill of materials (BOM) and general leadtimes. ATO systems have the advantage of mass customization and are widely adopted in practice. However, characterizing the optimal inventory policy is notoriously challenging and computational intractable, especially in large-scale systems. We propose effective and computational scalable heuristics through asymptotics, sample-path, linear programming and primal-dual analyses. First, we characterize the asymptotically optimal policy for the M-system. The policy consists of a periodic review priority (PRP) allocation rule and a coordinated base-stock (CBS) replenishment policy. We then construct heuristic policies using insights from the asymptotically optimal policy. In particular, we adopt the PRP allocation rule and develop a decomposition approach for inventory replenishment. This approach decomposes a general system into assembly subsystems and a linear program is constructed to compute policy parameters. However, both the CBS and the assembly decomposition approach are limited to simple systems. We then consider a second approach, which decomposes a system into distribution subsystems and each subsystem has a straightforward solution, which is similar to the newsvendor problem. We use the primal-dual analysis to show that the expected cost under the optimal independent base-stock policy in a general system could be bounded by two newsvendor systems with properly set parameters. Finally, we examine the effectiveness and scalability of these two decomposition approaches in numerical tests. We find that the assembly decomposition is very effective but computationally expensive and thus only good for small-scale systems; the distribution decomposition performs as effective as the optimal independent base-stock (IBS) policy, but is highly scalable for large-scale systems. Numerical tests also provide some interesting insights on the impact of system parameters on the value of past demand information.

Industry 4.0 marks a new industrial revolution centered around technologies that connect digital and physical realms, revamping how information is used to manage work. Two core enablers are the Internet of Things (IoT) and 3D printing. The former monitors the real-time status of a complex system, and the latter responds to that information with agile manufacturing. Nevertheless, how exactly this can be done remains unclear. To gain insights, we consider the context of a 3D printer supplying a critical part installed in multiple machines embedded with sensors and interconnected via IoT. While it is tempting to perceive that the marriage of 3D printing and IoT would make on-demand printing a reality, our results indicate that this is not necessarily so. The true benefit of the marriage is enabling predictive printing. In particular, it is optimal for the 3D printer to print-to-stock predictively in advance of demand, triggered by a system-lifetime-status dependent threshold. Whether it is optimal to print-on-advance-demand to achieve minimum inventory depends crucially on the printing speed. We further quantify the impact of IoT on system cost and inventory by separately assessing the impact of advance demand information from embedded sensors and that of IoT’s real-time information fusion from sensor interconnections. We demonstrate that information fusion intensifies the inventory reduction through advance demand information, and IoT’s impact hinges upon the effectiveness of embedded sensors. Our framework can be leveraged to aid IoT investment decisions and to help develop operating policies for predictive 3D printing.

This paper studies dynamic inventory and pricing decisions for a set of substitutable products over a flnite planning horizon. We present a general stochastic, price-dependent demand model that unifles many commonly used demand models in the literature. Unsatisfled demands are backlogged. There are linear purchasing, inventory-holding, and backordering costs. The objective is to maximize the total expected discounted proflt. The original formulation is not jointly concave in the decision variables and is therefore intractable. One key observation here is that the problem becomes jointly concave if we work with the inverse of the price vector { the market share vector. We characterize the optimal policy and develop algorithms to compute it. We establish conditions under which the optimal policy demonstrates certain monotonicity property, which, in turn, can greatly enhance computation. We also analyze the myopic policy and its optimality, and present a numerical study to illustrate the interplay of the pricing and inventory decisions.

We study a newsvendor problem with unknown demand distribution in a nonstationary demand environment over a multi-period time horizon. The demand in each period consists of a time-varying demand level and an additive random shock. Neither the demand level nor the random shock is separately observable. The amount of change in the demand level over the time horizon is measured by a cumulative variation metric. The problem has widespread applications, such as perishable inventory planning, staffing, and medical resource capacity planning in the wake of COVID-19. We design a nonparametric dynamic ordering policy, termed the moving window ordering policy, that tracks the shifts in the unknown demand level while accounting for the unobservable random demand shocks. To compute the order quantity in each period, this policy only needs the past demand observations, without any access to the underlying demand distribution. For a finite variation "budget," we prove that our ordering policy is first-order optimal in the sense that its regret grows at the smallest possible rate. We also extend our analysis to the case of asymptotically large variation budgets. Through case studies based on real-life data, we show that our policy can save 20-80% of overage and underage costs, relative to policies widely used for perishable inventory replenishment and nurse staffing.

In 2020, inventory shrinkage eroded $61.7 billion in the U.S. retail industry. Unfortunately, fighting inventory shrinkage to protect retailers’ already slim profits is challenging due to unknown shrinkage rates and censored sales data caused by unobservable theft behavior. To deal with such information deficiency, we introduce two new features to a Bayesian inventory model: (1) the invisibility of both inventory level and shrinkage rate, and (2) interleaving customer and theft arrivals (which contribute to actual sales and shrinkages, respectively). In contrast to the previous works, we use triple-censored sales data (invisible lost sales, thefts, and “lost thefts”) to learn inventory level and shrinkage rate simultaneously in real-time. We then use these learning formulae to develop a POMDP (Partially Observable Markov Decision Process) model for making inventory and loss prevention decisions. We analytically solve this model and propose several heuristic order policies to capture the benefit of learning. We demonstrate that our model outperforms several benchmark models. Through a numerical study, we show that our estimated shrinkage rate converges quickly and monotonically to the actual value. For products with high shrinkage rates (5% − 12%), our heuristic policy can help seize 82% − 94% of the ideal profit retailers could earn under full information. Our approach can thus enable early and easy identification of an effective loss prevention strategy, reduce shrinkage, and increase sales.

Problem definition: In a supply network consisting of a buyer, a purchasing agent, and a supplier, the buyer can procure the component from the supplier directly and rely on the purchasing agent fo...

Additive manufacturing, also known as 3D printing, has the potential to shift supply chains from global networks that rely on centralized production with traditional manufacturing technologies to mainly digital networks with distributed, local printing. Particularly well positioned to drive this transition are original equipment manufacturers (OEMs) who design and produce capital goods. We consider an OEM supplying a single part to multiple buyers over an infinite horizon. We study how the OEM can digitize the spare parts supply chain by leveraging 3D printing via intellectual property (IP) licensing. We first set up a benchmark model of the traditional physical supply chain with centralized production by the OEM. We then propose the OEM to act as an IP licensor by selling spare parts designs, rather than physical parts. With the license agreement, a buyer can print parts locally through a third-party printing service provider, enjoying a shorter lead time and lower setup cost. Given a license, each buyer chooses whether to switch to the IP licensing channel or stay in the traditional channel. The OEM selects the license terms to maximize the total profit across both channels. We characterize the OEM’s optimal license and the resulting supply chain configuration. We show that 3D printing’s competency in price plays a dominant role in production decentralization. The conventional wisdom that 3D printing is suitable for slow moving parts holds only when it is competent along several dimensions. In a numerical experiment with realistic parameter settings, decentralized production occurs in a surprisingly large number of cases, and the proposed new business model can significantly increase the OEM’s profit. Our results indicate that IP licensing by OEMs can become a major enabler in the transition to digital supply networks with distributed 3D printing, benefiting all parties involved.

Problem definition: Cloud computing is recognized as a critical driver of information technology–enabled innovations. The operations management (OM) community, however, has not been exposed enough to the essential operations problems that arise from the management of cloud value chains. Academic/practical relevance: In this paper, we examine recent research on cloud value chains and explore future research opportunities from an OM perspective. In particular, we focus on major operations management challenges facing a cloud provider in three problem domains: (1) cloud computing resource management, (2) pricing in the cloud computing marketplaces, and (3) capacity planning and management of cloud supply chains. Methodology: We describe prevalent business models and management practices in the cloud value chains, discuss recent research from OM that falls into each of the three problem domains mentioned, and point out opportunities for future research. Results: We note that cloud computing operations are driven by demand that exhibits distinct characteristics, including complex workflow, demand redundancy, multifeatured characteristics, multidimensional resource requirement, and nonstationarity. On the supply side, cloud computing operations also exhibit distinct characteristics, including heterogeneous resources, packing constraints, preconfigured (“bundled”) supply, technology risks, and cost uncertainty. These characteristics of demand and supply are not all prevalent in other operations. Managerial implications: Cloud computing operations not only share many features with classic OM problems, but also bring new challenges and innovative business models. Thus, OM tools and research have the potential to provide vital insights into cloud computing operations and impact management practices in the cloud industry, which, in turn, can stimulate much innovative research from the OM perspective. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1178 .

Motivated by our experience with a large online marketplace, we study an e-commerce middle-mile transportation network design problem. A salient feature in this problem is decentralized decision-making. While the middle-mile manager decides the network configuration on a weekly or bi-weekly basis, the real-time flows of millions of packages on any given network configuration (which we call the flow response) are controlled by a fulfillment policy employed by a different decision entity. Thus, we face a fixed-cost network design problem with unknown flow response. To meet this challenge, we first develop a predictive model for the unknown response leveraging machine learning techniques and observed shipment data. Apart from the most natural network-level predictive model, we find that the more parsimonious destination-level and arc-level predictive models are more effective. We then embed the predictive model to the original network design problem and characterize this transformed problem as a c-supermodular minimization problem. We develop a linear time algorithm with an approximation guarantee that depends on c. We demonstrate that this algorithm is scalable and effective in a numerical study. Besides the online marketplace, our approach is also applicable to the increasingly popular omni-channel retailing which also involves middle-mile network design.

We propose a geotemporal clustering based algorithm to predict the state-level COVID-19 cases in the United States, using the state-level population and historical COVID-19 case data as input. Our algorithm has two novel features. First, we treat a (state, date) pair as one observation in the COVID-19 case data, summarize features from the data, and classify similar observations using k-means clustering. Second, we use the similarity between the observations in the same cluster to capture the similarity of future trajectory of cases. Thus, when predicting the number of cases in a state in the future, we first map the pair of this state and the current date to a corresponding cluster, then take the observable future of older observations in this cluster as potential samples. Using mean absolute percentage error (MAPE) as the performance metric, we demonstrate that our algorithm provides reliable results for prediction periods ranging from 1 to 20 days. Our algorithm achieves the highest 7-day prediction accuracy both at the state and the national levels compared to three existing models and one intuitive baseline model. Our results indicate that in the next 20 days, states may be in starkly different situations if there are no interventions. While some states are getting better, the cases in others are still trending upward.

We study inventory control of serial supply chains with continuous, Markov-modulated demand (MMD). Our goal is to simplify the computational complexity by resorting to certain approximation techniques, and, in doing so, to gain a deeper understanding of the problem. First, we perform a derivative analysis of the problem’s optimality equations and develop general, analytical solution bounds for the optimal policy. This leads to simple-to-compute near-optimal heuristic solutions, which also reveal an intuitive relationship with the primitive model parameters. Second, we establish an MMD central limit theorem under long replenishment lead time through asymptotic analysis. We show that the relative errors between our heuristic and the optimal solutions converge to zero as the lead time becomes sufficiently long, with the rate of convergence being the square root of the lead time. Third, we show that, by leveraging the Laplace transform, the computational complexity of our heuristic is superior to the existing methods. Finally, we provide the first set of numerical study for serial systems under MMD. The numerical results demonstrate that our heuristic achieves near-optimal performance even under short lead times and outperforms alternative heuristics in the literature. In addition, we observe that, in an optimally run supply chain under MMD, the internal fill rate can be high and the demand variability propagating upstream can be dampened, both different from the system behaviors under stationary demand. The online appendix is available at https://doi.org/10.1287/opre.2017.1615 .

We consider a single-item continuous-review (r, q) inventory system with a renewal demand process and independent, identically distributed stochastic lead times. Using a stationary marked-point process technique and a heavy-traffic limit, we prove a previous conjecture that inventory position and inventory on-order are asymptotically independent. We also establish closed-form expressions for the optimal policy parameters and system cost in heavy-traffic limit, the first of their kind, to our knowledge. These expressions sharpen our understanding of the key determinants of the optimal policy and their quantitative and qualitative impacts. For example, the results demonstrate that the well-known square-root relationship between the optimal order quantity and demand rate under a sequential processing environment is replaced by the cube root under a stochastic parallel processing environment. We further extend the study to periodic-review (S, T) systems with constant lead times. The electronic companion is available at https://doi.org/10.1287/opre.2017.1623 .

In some manufacturing and service processes, several stages must be performed, but there is some freedom in the ordering of stages. Operations reversal means switching the order of two stages. Several authors have studied the benefits of operations reversal, focusing on the reduction of a certain variable’s variance or a related measure. This paper focuses instead on cost. We construct a model with the standard objective of minimizing the long-run average inventory-related cost. First, by using stochastic orders, we identify conditions under which operations reversal reduces cost. We find that in some cases the variability and cost objectives agree on when operations reversal is beneficial, but in other cases they disagree. In particular, when demands are multinomially distributed, variability reduction may be accompanied by cost increase. We show that, to guarantee a lower cost, we need certain properties on the aggregated demand at the choice-level (such as demands for sweaters of the same color). Finally, we examine the effects of cost parameters and lead times on operations reversal under the cost measure.

In today's increasingly globalized environment, more and more companies recognize the mutual dependence of supply chain partners in value creation. When making business decisions, they take into consideration their partners’ bottom line profitability, especially in emerging markets. The question is, is this kind of practice sustainable? This paper makes an attempt to formalize this issue by examining a stylized two-party supply chain model in which each player maximizes its own profit while making a certain commitment to its partner. We compare five different games between the two supply-chain partners, which reflect different power positions of the players and different levels of commitment. We identify conditions under which both players are better off with mutual commitments than without, a situation we call win-win. We show that win-win can be achieved if and only if the mutual commitments are comparable. Thus, the recognition of mutual dependence of the supply chain members needs to be translated into reciprocal concerns. In addition, different players’ commitments play different roles but together they have a similar effect as a profit sharing contract. Finally, we discuss the implications of our findings in the context of socially responsible operations. In particular, our analyses show that it is possible to care about the supply chain partners’ bottom line without sacrificing one's own profitability, and our models can be used as a tool to determine the commitment levels by evaluating the predicted outcome. This article is protected by copyright. All rights reserved.

Additive manufacturing (AM), also known as 3D printing, has the potential to shift supply chains from global networks that rely on centralized production with traditional manufacturing technologies to largely digital networks with decentralized, local 3D printing, i.e., digital inventory. One type of firm that is particularly well positioned to drive this transition are original equipment manufacturers (OEMs), who design and produce capital goods. In this paper we propose that the OEM acts as an intellectual property (IP) licensor by selling spare parts designs, rather than physical spare parts. With these designs a buyer can print spare parts locally at much shorter lead times and at lower setup costs. We consider both an OEM who serves multiple identical buyers, and an OEM who serves two non-identical buyers. For both cases we characterize the optimal IP license contract. This determines which customers opt for the IP license channel and which remain in the traditional centralized sales channel, thus creating insights into the degree to which a supply chain decentralizes. We numerically show this to occur in a surprisingly large number of cases and we observe significant profit increases for OEMs who adopt this new business model. Our results thus show that IP licensing by OEMs can become a major enabler in the transition to digital networks with decentralized 3D printing.

Stockpiling inventory is an essential strategy for building supply chain resilience. It enables firms to continue operating while finding a solution to an unexpected event that causes a supply disruption or demand surge. While extremely valuable when actually deployed, stockpiles incur large holding costs and usually provide no benefits until such a time. To help to reduce this cost, this study presents a new approach for managing stockpiles. We show that if leveraged intelligently, stockpiles can also help an organization better meet its own regular demand by enabling a type of virtual pooling we call virtual stockpile pooling (VSP). The idea of VSP is to first integrate the stockpile into several locations’ regular inventory buffers and then dynamically reallocate the stockpile among these locations in reaction to the demand realizations to achieve a kind of virtual transshipment. To study how to execute VSP and determine when it can provide the most value, we formulate a stylized multi-location stochastic inventory model and solve for the optimal stockpile allocation and inventory order policies. We show that VSP can provide significant cost savings: in some cases nearly the full holding cost of the stockpile (i.e., VSP effectively maintains the stockpile for free), in other cases nearly the savings of traditional physical inventory pooling. Last, our results prescribe implementing VSP with many locations for large stockpiles, but only a few locations for small stockpiles. Accepted version

We consider two specially structured assemble-to-order (ATO) systems—the N- and W-systems—under continuous review, stochastic demand, and nonidentical component replenishment leadtimes. Using a hybrid approach that combines sample-path analysis, linear programming, and the tower property of conditional expectation, we characterize the optimal component replenishment policy and common-component allocation rule, present comparative statics of the optimal policy parameters, and show that some commonly used heuristic policies can lead to significant optimality loss. The optimality results require certain symmetry in the cost parameters. In the absence of this symmetry, we show that, for systems with high demand volume, the asymptotically optimal policy has essentially the same structure; otherwise, the optimal policies have no clear structure. For these latter systems, we develop heuristic policies and show their effectiveness. © 2016 Wiley Periodicals, Inc. Naval Research Logistics, 2016

Problem definition: Considering a mix of pre-positioning and local purchasing, common to cover humanitarian demands in the aftermath of a rapid-onset disaster, we propose policies to determine pre-position stock. These formulations are developed in the presence of demand, budget and local supply uncertainties, and for single items delivery. Academic/Practical Relevance: The immediate period aftermath of a disaster is the most crucial period during which humanitarian organizations must supply relief items to beneficiaries. Yet, due to many unknowns such as time, place, and magnitude of a disaster, supply management is a significant challenge, and these decisions are made intuitively. The features and complexities we examine have not been studied in the literature. Methodology: We derive properties of the optimal solution, identify exact solution methods, and determine approximate methods that are easy to implement. Results: We (i) characterize the interplay of supply, demand, and budget uncertainties, as well as the impact of product characteristics on optimal prepo stock levels, (ii) show in what conditions, the prepo stock is a simple Newsvendor solution, and (iii) discuss the value of emergency funds. Managerial Implications: We show that budget level is a key determinant of the optimal policy. When it is above a threshold, inventory increases in disaster frequency and severity, but the reverse is true otherwise. When budget is limited, the rate of savings from improved forecasts is amplified (attenuated) for critical (noncritical) items, reflecting opposing directional effects of mismatch cost and cost of insufficient funding. Our model can also be used to estimate the value of initiatives to mitigate constraints on local spend, (e.g., a line of credit underwritten by large donors that is available during the immediate relief period).

Problem definition: We study the optimal inventory ordering, expediting and allocation decisions in a multi-echelon supply chain over a finite horizon, in which customer orders are quoted with a fixed fulfillment time window, termed the service time target (STT). Academic/Practical Relevance: Service time target is commonly used as a marketing strategy to increase customer satisfaction and strengthen firms' competitive edge. However, how to efficiently manage a multi-stage supply chain to meet the targets has received relatively scant attention in the literature. Our study fills this gap. Methodology: We use dynamic programming to characterize the optimal policy. Results: We show that an echelon base stock policy and a rationing policy are optimal for inventory ordering and inventory allocation/expediting, respectively. We also develop a polynomial-time algorithm to compute the optimal policy. To derive these results, we uncover a new functional property named the decomposable of degree 2 property, which is a non-trivial generalization of the celebrated Clark-Scarf decomposition. This property further allows us to derive induced penalty and compensation to coordinate a decentralized serial system with STT and expediting. Managerial Implications: Our result provides an efficient decision tool for managing centralized and decentralized serial supply chains with STT and expediting. Our model can be used to quantify the tradeoff between marketing and operational decisions, such as the impact of a marginal reduction in STT on system cost and expedition frequency, as explored in our numerical studies.

"Spillover" learning is defined as customers' learning about the quality of a service (or product) from their previous experiences with similar yet not identical services. In this paper, we propose a novel, parsimonious and general Bayesian hierarchical learning framework for estimating customers' spillover learning. We apply our model to a one-year shipping/sales historical data provided by a world-leading third party logistics company and study how customers' experiences from shipping on a particular route affect their future decisions about shipping not only on that route, but also on other routes serviced by the same logistics company. Our empirical results are consistent with information spillovers driving customer choices. Customers also display an asymmetric response such that they are more sensitive to delays than early deliveries. In addition, we find that customers are risk averse being more sensitive to their uncertainty about the mean service quality than to the intrinsic variability of the service. Finally, we develop policy simulation studies to show the importance of accounting for customer learning when a firm considers service quality improvement decisions.

A retail cluster refers to a collection of horizontally differentiated retailers of a particular business sector locating in close proximity. Retail clusters are commonly seen in developing countries. In this paper, we develop a game-theoretic model to explore why the retail cluster phenomenon is so popular in developing countries and how the governments in these countries can foster retail clusters and leverage them to improve social welfare. Our model captures two salient characteristics of developing countries – product fit uncertainty and transportation cost. We classify retailers into two types (mainstream and niche retailers) and study their incentives to join a cluster. We find that there are only two types of retail clusters possible in equilibrium: a grand cluster where all retailers locate together or a niche-only cluster where only niche retailers locate together. More importantly, we show that the government can improve social welfare through mandating the physical location of the cluster. Our findings suggest that when the unit misfit cost or the unit transportation cost is medium, the preferences of retailers and the government are misaligned and thus government intervention is necessary. Specifically, retail clusters are preferred under a larger range of market conditions under such government intervention.

We consider a single-product, two-source inventory system with Poisson demand and backlogging. Inventory can be replenished through a normal supply source, which consists of a two-stage tandem queue with exponential production time at each stage. We can also place an emergency order by skipping the first stage, for a fee. There is no fixed order cost. There are linear order, holding and backorder costs. Through a new approach, we obtain optimal ordering policies for the discounted or long-run average cost, and also characterize near-optimal heuristic policies. The approach consists of four steps. The first step is to establish an equivalent system, in the sense that it has the same optimal policy as the original system. The second step is to construct a tandem queueing system, where costs are charged in accord with the equivalent system's cost structure. The third step derives an optimal control of the service rate at each server so as to minimize the tandem queue's system-wide cost. The fourth and final step is to translate the queue's optimal policy to an optimal policy for the equivalent system and hence the original system.

Problem definition: We study the management of social responsibility in a three-tier supply chain with a tier 2 supplier selling to a tier 1 supplier, in turn selling to a tier 0 buyer. The tier 2 supplier may violate social and environmental standards, resulting in harm to all firms in the supply chain; we analyze the equilibrium allocation of costly effort by each firm to improve responsibility in tier 2. We also examine how pressure from external stakeholders (consumers, nongovernmental organizations, and governments) influences the optimal level of responsibility. Academic/practical relevance: Recently, there have been numerous serious responsibility violations in tiers 2+ of multinational supply chains, leading to significant negative consequences for firms and society; understanding how best to manage such violations is of practical importance to multiple stakeholders. Methodology: We employ a game theoretic model wherein each firm in the supply chain optimizes its responsibility efforts to maximize its own profit and study the implications of this decentralized optimization for the overall supply chain. Results: Under the conditions of our model, the buyer’s optimal strategy is one of extremes, consisting of direct control (only tier 0 works with tier 2), delegation (only tier 1 works with tier 2), or no effort (neither firm works with tier 2); we determine when each is optimal and discuss key drivers of the optimality of these extreme strategies. We further find that increasing some types of external pressure can backfire, leading to a lower level of responsibility. Managerial implications: For firms using multitier supply chains, we show how to manage risk by choosing between different responsibility management strategies. For external stakeholders seeking to encourage responsibility, we provide insights on how to achieve this while avoiding “backfiring.” For researchers, we provide a modeling framework to study responsibility and risk management problems in multitier supply chains. History: This paper has been accepted for the Manufacturing & Service Operations Management Special Section on Responsible Research in Operations Management. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2021.1063 .

In today's increasingly globalized environment, more and more companies recognize the mutual dependence of supply chain partners in value creation. When making business decisions, they take into consideration their partners' bottom line profitability, especially in emerging markets. The question is, is this kind of practice sustainable? This paper makes an attempt to formalize this issue by examining a stylized two-party supply chain model in which each player maximizes its own profit while making a certain commitment to its partner. We compare five different games between the two supply-chain partners, which reflect different power positions of the players and different levels of commitment. We identify conditions under which both players are better off with mutual commitments than without, a situation we call win-win. We show that win-win can be achieved if and only if the mutual commitments are comparable. Thus, the recognition of mutual dependence of the supply chain members needs to be translated into reciprocal concerns. In addition, different players' commitments play different roles but together they have a similar effect as a profit sharing contract. Finally, we discuss the implications of our findings in the context of socially responsible operations. In particular, our analyses show that it is possible to care about the supply chain partners' bottom line without sacrificing one's own profitability, and our models can be used as a tool to determine the commitment levels by evaluating the predicted outcome.

Globalization, innovation, social media, and exposure to natural and man-made disasters have increased organizations’ need to cope with demand surges: random, significant increases in demand in an otherwise relatively stable demand environment. To build supply chain capabilities, organizations face a choice between two fundamentally different sourcing strategies—reactive capacity and safety stock. We develop a framework to guide the joint sourcing strategy that minimizes the long-run average cost under a target service level. A salient feature of our modeling framework is a novel demand model that captures important continuous-time, non-Markovian characteristics of demand surge trajectories. In addition to the total magnitude as typically modeled in the literature, we define several other metrics of surges, including duration, intensity, compactness, peak position, volatility, and frequency. The resulting optimization problem is challenging because it requires evaluating, for any sample path, whether surge demand can be satisfied at every point in time—a refined feature that traditional models do not have. To identify the optimal strategy, we first characterize the optimal production and deployment policy for any given strategy and then transform the original problem into two equivalent but more tractable problems. Finally, through stochastic comparison techniques, we show how the magnitude and predictability of surge demand characteristics (mentioned above) and the cost profiles of each strategy impact the optimal joint strategy.

With the increasingly open global economy and advanced technologies, some third-party-logistics providers (3PLs) have emerged as supply chain orchestrators, linking buying firms’ needs with dispersed manufacturers worldwide. In addition to their traditional transportation services, these orchestrators provide procurement and financial assistance to players in the supply network, especially small and medium sized enterprises (SMEs) in developing countries. Oftentimes, the 3PLs can obtain payment delay arrangements from the financially stronger manufacturers, which in turn can be partially extended to the SME buyers, alleviating their high cost of capital. This paper explores the conditions under which this innovation benefits all parties in the chain so that the business model is sustainable. Using a model that explicitly captures the cash dynamics in a supply chain consisting of a manufacturer, several buyers and a 3PL firm, we characterize these conditions and show that they are more likely to occur with many small buyers. We also show that the supply chain profit is higher under leadership by the 3PL than by the manufacturer. We find that the intermediary role of the 3PL is crucial, in that its benefit vanishes if the manufacturer chooses to grant payment delay to the buyers directly.

This paper presents a model to characterize the dynamics of and rationale for voluntary horizontal collaborations commonly seen in global supply chains and organizations. Examples of such collaborations include supply clusters, supplier alliances, and teams of peers within organizations. The model consists of two key elements. One is a repeated game framework that captures the dynamics of the underlying environment, such as the long-term interactions between individual players who have limited capabilities and face uncertain demand. The second element is a peer help mechanism, which articulates the implicit short-term cost and long-term benefit of team production and generate synergies among the players. Our analysis shows that the emergence of horizontal collaborations depends on the difference between the individual players’ productivity levels, players’ patience, and the prevalence of high performers on the team. We also show that equilibrium team size is determined by the cost/technology structure of the collaboration, which varies depending on what kind of asset (e.g., inventory or capacity) is shared. We further extend the model to a principal-agent framework, in which the principal can benefit from forming teams of agents but cannot enforce or monitor whether peer help occurs. Finally, we characterize the optimal compensation and team structure for the principal to assign agents to teams.