Managing Slow‐Moving Perishables in the Grocery Industry

We address the value of information and value of centralized control in the context of a two‐echelon, serial supply chain with one retailer and one supplier that provide a single perishable product to consumers. Our analysis is relevant for managing slow‐moving perishable products with fixed lot sizes and expiration dates of a week or less. We evaluate two supply chain structures. In the first structure, referred to as decentralized information sharing, the retailer shares its demand, inventory, and ordering policy with the supplier, yet both facilities make their own profit‐maximizing replenishment decisions. In the second structure, centralized control, incentives are aligned and the replenishment decisions are coordinated. The latter supply chain structure corresponds to the industry practices of company‐owned stores or vendor‐managed inventory. We measure the value of information and value of centralized control as the marginal improvement in expected profits that a supply chain achieves relative to the case when no information is shared and decision making is decentralized. Key assumptions of our model include stochastic demand, lost sales, and fixed order quantities. We establish the importance of information sharing and centralized control in the supply chain and identify conditions under which benefits are realized. As opposed to previous work on the value of information, the major benefit in our setting is driven by the supplier's ability to provide the retailer with fresher product. By isolating the benefit by firm, we show that sharing information is not always Pareto‐improving for both supply chain partners in the decentralized setting.     

A Stochastic Inventory Model With Fast‐Ship Commitments

We present a multiperiod model of a retail supply chain, consisting of a single supplier and a single retailer, in which regular replenishment occurs periodically but players have the option to support fast delivery when customers experience a stockout during a replenishment period. Because expedited shipments increase the supplier's transportation cost, and possibly production/inventory costs, the supplier typically charges a markup over and above the prevailing wholesale price for fast‐shipped items. When fast shipping is not supported, items are backordered if customers are willing to wait until the start of the next replenishment period. We characterize the retailers and the supplier's optimal stocking and production policies and then utilize our analytical framework to study how the two players respond to changes in supply chain parameters. We identify a sufficient condition such that the centralized supply chain is better off with the fast‐ship option. We find a range of markups for fast‐ship orders such that the fast‐ship option is preferred by both the supplier and the retailer in a decentralized chain. However, a markup that is a win–win for both players may not exist even when offering fast‐ship option is better for the centralized chain. Our analysis also shows that depending on how the markup is determined, greater customer participation in fast‐ship orders does not necessarily imply more profits for the two players. For some predetermined markups, the retailer's profit with the fast‐ship option is higher when more customers are willing to wait. However, the retailer may not be able to benefit from the fast‐ship option because the supplier may choose not to support the fast‐ship option when fast‐ship participation increases due to the fact that the fast‐ship participation rate adversely affects the initial order size.     

Quality Management in Multi‐Level Supply Chains with Outsourced Manufacturing

Outsourcing stretches supply chains longer with added contract manufacturers responsible for the manufacturing of parts and final products. Should a firm change its quality management approach as its supply chain becomes longer with outsourced manufacturing? This paper studies a brand owner's optimal choice between two commonly used quality management approaches: an inspection‐based approach and an external failure‐based approach, in two supply chains – a dyadic supply chain and a multi‐level supply chain where the brand owner outsources manufacturing to an independent contract manufacturer. Our study finds that the brand owner's optimal choice between the two quality management approaches could be opposite in the two supply chains. Specifically, we show that if agency costs exist between the contract manufacturer and the brand owner, the brand owner may prefer an inspection‐based approach in the multi‐level supply chain in contrast to preferring an external failure‐based approach in the dyadic supply chain. In particular, inspections can be effective for the brand owner to limit the manufacturer's profit by excluding defective finished products and components, which in turn reduce agency costs in the multi‐level supply chain. Hence, the efficiency of an inspection‐based approach relative to an external failure‐based approach can be higher in the multi‐level supply chain as compared to the dyadic one. Our findings suggest that firms should adjust to changes in supply chain structures and re‐evaluate the efficiencies of different quality management approaches accordingly.     

Price Incentives and Coordination in a Two‐Product Decentralized Supply Chain

We investigate pricing incentives for competing retailers who distribute two variants of a manufacturer's product in a decentralized supply chain. Under a two‐dimensional Hotelling model, we derive decentralized retailers' prices for the products, and distortions in pricing when compared to centrally optimal prices. We show that price distortions decrease as consumers' travel cost between retailers increases, due to less intense competition. However, price distortions do not change monotonically in consumers' switching cost between products within stores. To fix decentralized retailers' price distortions, we construct a two‐part pricing contract that coordinates the supply chain. We show that the coordinating contract is Pareto‐improving and analyze increase in the supply chain profit under coordination.     

Equity‐Based Incentives and Supply Chain Buy‐Back Contracts*

We analyze the effect of equity‐based incentives in a supply chain with a downstream firm and an upstream supplier. By using the operational decision as a signal to influence external investors’ beliefs, the downstream firm's manager intends to maximize a convex combination of the interim share price and the terminal cash flows. We show that equity‐based incentives create a side effect. Specifically, with a universal buy‐back contract, the deadweight loss of signaling induced by equity‐based incentives could spread throughout the supply chain and cause chain‐wide damages. To mitigate such undesirable consequences, we propose a new mechanism to eliminate the inefficiency. We derive the optimal mechanism that maximizes the downstream firm's profits subject to the constraint that the supply chain efficiency is not undermined. In contrast to the full‐information benchmark, this mechanism gives positive surplus to the supplier. [Submitted: January 5, 2011. Revisions received: June 20, 2011; December 11, 2011. Accepted: December 22, 2011.]     

Coordinated Contract Decisions in a Make‐to‐Order Manufacturing Supply Chain: A Stochastic Programming Approach

One of the important objectives of supply chain S&OP (Sales and Operations Planning) is the profitable alignment of customer demand with supply chain capabilities through the coordinated planning of sales, production, distribution, and procurement. In the make‐to‐order manufacturing context considered in this paper, sales plans cover both contract and spot sales, and procurement plans require the selection of supplier contracts. S&OP decisions also involve the allocation of capacity to support sales plans. This article studies the coordinated contract selection and capacity allocation problem, in a three‐tier manufacturing supply chain, with the objective to maximize the manufacturer's profitability. Using a modeling approach based on stochastic programming with recourse, we show how these S&OP decisions can be made taking into account economic, market, supply, and system uncertainties. The research is based on a real business case in the Oriented Strand Board (OSB) industry. The computational results show that the proposed approach provides realistic and robust solutions. For the case considered, the planning method elaborated yields significant performance improvements over the solutions obtained from the mixed integer programming model previously suggested for S&OP.     

Adaptive Tactical Pricing in Multi‐Agent Supply Chain Markets Using Economic Regimes

In today's complex and dynamic supply chain markets, information systems are essential for effective supply chain management. Complex decision making processes on strategic, tactical, and operational levels require substantial timely support in order to contribute to organizations' agility. Consequently, there is a need for sophisticated dynamic product pricing mechanisms that can adapt quickly to changing market conditions and competitors' strategies. We propose a two‐layered machine learning approach to compute tactical pricing decisions in real time. The first layer estimates prevailing economic conditions—economic regimes—identifying and predicting current and future market conditions. In the second layer, we train a neural network for each regime to estimate price distributions in real time using available information. The neural networks compute offer acceptance probabilities from a tactical perspective to meet desired sales quotas. We validate our approach in the trading agent competition for supply chain management. When competing against the world's leading agents, the performance of our system significantly improves compared to using only economic regimes to predict prices. Profits increase significantly even though the prices and sales volume do not change significantly. Instead, tactical pricing results in a more efficient sales strategy by reducing both finished goods and components inventory costs.     

Competitive and Collaborative Quality and Warranty Management in Supply Chains

Product quality and product warranty coverage are two important and closely related operational decisions. A longer warranty protection period can boost sales, but it may also result in dramatically increased warranty cost, if product quality is poor. To investigate how these two decisions interact with each other and influence supply chain performance, we develop a single‐period model with a supplier that provides a product to an original equipment manufacturer, which in turn sells it to customers. Customer demand is random and affected by the length of the product warranty period. Warranty costs are incurred by both the supplier and the manufacturer. We analyze two different scenarios based on which party sets the warranty period: manufacturer warranty and supplier warranty. Product quality is controlled by the supplier, and the manufacturer determines the ordering quantity. We analyze these decentralized systems and provide the structural properties of the equilibrium strategies. We also compare the results of centralized and decentralized systems and identify the conditions under which one system provides a longer warranty and better product quality than the other. Our numerical study further shows that, in decentralized settings, when the warranty period is determined by the firm sharing the larger proportion of total warranty costs, the supply chain can achieve greater system‐wide profit. Both parties can therefore benefit from properly delegating the warranty decision and sharing the resulting additional profit. We further design a supplier‐development and buy‐back contract for coordinating decentralized supply chains. Several extensions are also discussed.     

不确定环境下的Supply-hub协同补货决策

个性化需求与零部件创新使得产品需求和补货提前期不确定,对供应链补货决策和运行成本产生重要影响。将提前期不确定因素引入Supply-hub协同补货研究中,探讨提前期随机和需求不确定情况下,考虑零部件配套性的三供应商单制造商生产两定制产品的Supply-hub协同补货决策问题;提出了三种补货策略,以供应链运行成本最小化为目标,建立不同策略下的供应链补货模型并求解最优补货批量和供应链最小运行成本;发现三种补货策略均存在唯一最优补货批量,基于Supply-hub的两种协同补货策略和基于分散决策的供应商独立补货策略各有优势,但基于Supply-hub的批量及时间协同的补货策略恒优于基于Supply-hub的集中补货策略。最后,通过MATLAB进行算例分析验证结论,发现基于Supply-hub的批量及时间协同的补货策略能有效降低需求不确定性带来的成本增加风险;通用件的提前期波动对于供应链期望运行成本的影响要高于定制件提前期波动的影响,因此在进行供应链补货策略选择时更加关注通用件提前期。     

Managing Engineering Design for Competitive Sourcing in Closed‐Loop Supply Chains

We examine the strategic interplay between a buyer's design decision and the ensuing competition between suppliers in a three‐tier closed‐loop supply chain setting with significant recycling considerations. The nature of the engineering design decision in our research entails choice of integral versus modular design that has direct implications for the input raw material waste and ensuing competition between suppliers (i.e., incumbent and new). Whereas the integral design requires a large blank and generates excessive material scrap, the modular design reduces the generated scrap, and enhances cut‐to‐fit modularity, but incurs joining cost and yield loss. The incumbent supplier who supports the status quo choice of integral design can effectively recycle excessive material waste, as it is strategically located close to the source of material. The engineering design team at our study firm is currently exploring the option to source from alternative suppliers that can support either integral or modular designs, but have significantly lower effectiveness in recycling scrap material. We characterize the buyer's price sensitivity levels, component characteristics, supply chain configurations, and virgin and scrap specialty material prices that yield various design and sourcing policy alternatives. The buyer's optimal policy choice, the ensuing price–demand dynamics, and the resulting recycling implications demonstrate that the buyer can benefit from strategically tailoring his design decisions to affect the suppliers' material requirements and costs. We show that utilizing an alternative supply option is particularly valuable for components made from a material with a low price differential in virgin and scrap forms in supply chains wherein the new supplier base can recycle effectively. In such cases, the buyer induces severe price competition by dual sourcing the integral design, and competition may negate the seemingly obvious benefits of operational improvements (e.g., higher scrap material return rate).      

Distributed Decision‐Making in Supply Chains and Private E‐Marketplaces

This paper develops a distributed decision‐making framework for the players in a supply chain or a private e‐marketplace to collaboratively arrive at a global Pareto‐optimal solution. In this model, no player has complete knowledge about all the costs and constraints of the other players. The decision‐making framework employs an iterative procedure, based on the Integer L‐shaped method, in which a master problem is solved to propose global solutions, and each player uses his local problems to construct feasibility and optimality cuts on the master problem. The master problem is modeled as a mixed‐integer program, and the players' local problems are formulated as linear programs. Collaborative planning scenarios in private e‐marketplaces and in supply chains were formulated and solved for test data. The results show that this distributed model is able to achieve near‐optimal solutions considerably faster than the traditional centralized approach.     

Mitigating the U.S. Drug Shortages Through Pareto‐Improving Contracts

Drug shortages have been a major challenge facing the US pharmaceutical industry and government in recent years. Although the problem has drawn tremendous attention from the government and media, limited academic research has been devoted to this problem, and few solutions have been proposed based on rigorous research. This study addresses the drug shortage problem from a supply chain perspective, a key aspect missing in the literature, and proposes to mitigate shortages through drug purchase contracts. By modeling the drug supply chain, we capture the objectives of various supply chain parties, and investigate Pareto‐improving contracts that mitigate drug shortages, improve drug manufacturer's and group purchasing organization (GPO)'s profits, and cut government spending and healthcare providers’ costs. We explore structural properties of key supply chain decisions and the Pareto‐improving contracts, and conduct scenario analysis with realistic industry data to evaluate shortage mitigation solutions. Our analysis shows that increasing drug prices only, a solution advocated by many, is not very effective in shortage mitigation. Price increases must be paired with strengthened failure‐to‐supply clauses (called the IPS approach) to achieve consistent and significant shortage reduction as well as Pareto improvement. Across all scenarios tested, a 30% price increase under IPS can lead to a minimum, average, and maximum shortage reduction of 25%, 53%, and 70%, respectively. Our analysis also shows the impacts of IPS on different parties in the supply chain and the impacts of various model parameters on shortage mitigation. The IPS approach rewards reliability of drug supply, which is in line with the FDA's strategic plan to reward quality, but is easier to achieve in this regulation‐based industry. Interactions with the government and industry practitioners indicate that IPS also challenges the current mindset in pharmaceutical contracting.     

Stable and Coordinating Contracts for a Supply Chain with Multiple Risk‐Averse Suppliers

We analyze a decentralized supply chain with a single risk‐averse retailer and multiple risk‐averse suppliers under a Conditional Value at Risk objective. We define coordinating contracts and show that the supply chain is coordinated only when the least risk‐averse agent bears the entire risk and the lowest‐cost supplier handles all production. However, due to competition, not all coordinating contracts are stable. Thus, we introduce the notion of contract core, which reflects the agents' “bargaining power” and restricts the set of coordinating contracts to a subset which is “credible.” We also study the concept of contract equilibrium, which helps to characterize contracts that are immune to opportunistic renegotiation. We show that, the concept of contract core imposes conditions on the share of profit among different agents, while the concept of contract equilibrium provide conditions on how the payment changes with the order quantity.     

Mechanisms for Increasing Sourcing from Capacity‐Constrained Local Suppliers

The fresh produce supply chain is characterized by large (mainstream) farms that are located far from consumers, and capacity‐constrained (local) farms that are located close to the consumer. In this setting, we study: (i) how leadtime and capacity asymmetry between mainstream and local farms affect a retail grocer's order policy for fresh produce, and (ii) how various operational mechanisms can increase the amount sourced from local farms. We show that this supply chain structure is disadvantageous for local suppliers (farms) because it induces the retailer to treat the local supply as a de facto responsive supply without paying a premium for the responsiveness. This disadvantage is exacerbated when the retailer's objective is to achieve a high service level. We study three mechanisms that can improve conditions for local farms: working with an intermediary, backhauling, and a retail order policy, purchase guarantee, that explicitly supports local farms. The intermediary and backhauling mechanisms help the local farm by making local supply more attractive to the retailer, inducing her to order more locally sourced produce. The intermediary reduces the retailer's overstock and stockout costs whereas backhauling increases the average margin. The purchase guarantee order policy helps local farms at the expense of retail profit. However, we show that purchase guarantee and backhauling are complementary mechanisms that together can benefit the retailer and local farms.     

Assessing End‐Of‐Supply Risk of Spare Parts Using the Proportional Hazard Model

Operators of long field‐life systems like airplanes are faced with hazards in the supply of spare parts. If the original manufacturers or suppliers of parts end their supply, this may have large impacts on operating costs of firms needing these parts. Existing end‐of‐supply evaluation methods are focused mostly on the downstream supply chain, which is of interest mainly to spare part manufacturers. Firms that purchase spare parts have limited information on parts sales, and indicators of end‐of‐supply risk can also be found in the upstream supply chain. This article proposes a methodology for firms purchasing spare parts to manage end‐of‐supply risk by utilizing proportional hazard models in terms of supply chain conditions of the parts. The considered risk indicators fall into four main categories, of which two are related to supply (price and lead time) and two others are related to demand (cycle time and throughput). The methodology is demonstrated using data on about 2,000 spare parts collected from a maintenance repair organization in the aviation industry. Cross‐validation results and out‐of‐sample risk assessments show good performance of the method to identify spare parts with high end‐of‐supply risk. Further validation is provided by survey results obtained from the maintenance repair organization, which show strong agreement between the firm's and the model's identification of high‐risk spare parts.     

Item‐Level RFID in the Retail Supply Chain

Analyzing the proliferation of item‐level RFID, recent studies have identified the cost sharing of the technology as a gating issue. Various qualitative studies have predicted that conflict will arise, in particular in decentralized supply chains, from the fact that the benefits and the costs resulting from item‐level RFID are not symmetrically distributed among supply chain partners. To contribute to a better understanding of this situation, we consider a supply chain with one manufacturer and one retailer. Within the context of this retail supply chain, we present analytic models of the benefits of item‐level RFID to both supply chain partners. We examine both the case of a dominant manufacturer as well as the case of a dominant retailer, and we analyze the results of an introduction of item‐level RFID to such a supply chain depending on these market power characteristics. Under each scenario, we show how the cost of item‐level RFID should be allocated among supply chain partners such that supply chain profit is optimized.     

Revenue‐Sharing vs. Wholesale‐Price Contracts in Assembly Systems with Random Demand

Assembly and kitting operations, as well as jointly sold products, are rather basic yet intriguing A decentralized supply chains, where achieving coordination through appropriate incentives is very important, especially when demand is uncertain. We investigate two very distinct types of arrangements between an assembler/retailer and its suppliers. One scheme is a vendor‐managed inventory with revenue sharing, and the other a wholesale‐price driven contract. In the VMI case, each supplier faces strategic uncertainty as to the amounts of components, which need to be mated with its own, that other suppliers will deliver. We explore the resulting components' delivery quantities equilibrium in this decentralized supply chain and its implications for participants' and system's expected profits. We derive the revenue shares the assembler should select in order to maximize its own profits. We then explore a revenue‐plus‐surplus‐subsidy incentive scheme, where, in addition to a share of revenue, the assembler also provides a subsidy to component suppliers for their unsold components. We show that, by using this two‐parameter contract, the assembler can achieve channel coordination and increase the profits of all parties involved. We then explore a wholesale‐price‐driven scheme, both as a single lever and in combination with buybacks. The channel performance of a wholesale‐price‐only scheme is shown to degrade with the number of suppliers, which is not the case with a revenue‐share‐only contract.     

Newsvendor Pricing Problem in a Two‐Sided Market

We study the pricing problem of a “platform” intermediary to jointly determine the selling price of the platforms (hardware) sold to consumers and the royalty charged to content developers for content (software), when the demands for content and for platforms are interdependent. Our model elucidates the impact of supply chain replenishment costs and demand uncertainty on the strategic issues of platform pricing in a two‐sided market.