Dynamic Supply Risk Management with Signal‐Based Forecast,Multi‐Sourcing,and Discretionary Selling

We examine the critical role of advance supply signals—such as suppliers’ financial health and production viability—in dynamic supply risk management. The firm operates an inventory system with multiple demand classes and multiple suppliers. The sales are discretionary and the suppliers are susceptible to both systematic and operational risks. We develop a hierarchical Markov model that captures the essential features of advance supply signals, and integrate it with procurement and selling decisions. We characterize the optimal procurement and selling policy, and the strategic relationship between signal‐based forecast, multi‐sourcing, and discretionary selling. We show that higher demand heterogeneity may reduce the value of discretionary selling, and that the mean value‐based forecast may outperform the stationary distribution‐based forecast. This work advances our understanding on when and how to use advance supply signals in dynamic risk management. Future supply risk erodes profitability but enhances the marginal value of current inventory. A signal of future supply shortage raises both base stock and demand rationing levels, thereby boosting the current production and tightening the current sales. Signal‐based dynamic forecast effectively guides the firm's procurement and selling decisions. Its value critically depends on supply volatility and scarcity. Ignoring advance supply signals can result in misleading recommendations and severe losses. Signal‐based dynamic supply forecast should be used when: (a) supply uncertainty is substantial, (b) supply‐demand ratio is moderate, (c) forecast precision is high, and (d) supplier heterogeneity is high.     

Bounds of Relative Regret Limit in p‐Robust Supply Chain Network Design

This research studies the p‐robust supply chain network design with uncertain demand and cost scenarios. The optimal design integrates the supplier selection together with the facility location and capacity problem. We provide a new framework to obtain the relative regret limit, which is critical in the robust supply chain design but is assumed to be a known value in the existing literature. We obtain lower and upper bounds for relative regret limit and obtain a sequence of optimal solutions for series relative regret limits between the upper and lower bounds. An algorithm for p‐robust supply chain network design is provided. A series of numerical examples are designed to find the properties of the bottleneck scenarios. A scenario with low probability and a low optimal objective function value for the scenario has a greater chance of being a bottleneck. To focus only on the influence from the relative regret, we also introduce three separate new objective functions in p‐robust design. The proposed new theories and approaches provide a sequence of options for decision makers to reduce the marketing risks effectively in supply chain network design.     

Demand–Supply Mismatches and Stock Market Performance: A Retailing Perspective

We provide empirical evidence that the volatility of inventory productivity relative to the volatility of demand is a predictor of future stock returns in a sample of publicly listed U.S. retailers over the period 1985–2013. This key performance indicator, entitled demand–supply mismatch (DSM), captures the fact that low variation in inventory productivity relative to variation in demand is indicative of the superior synchronization of demand‐ and supply‐side operations. Applying the Fama and French (1993) three‐factor model augmented with a momentum factor (Carhart 1997), we find that zero‐cost portfolios formed by buying the two lowest and selling the two highest quintiles of DSM stocks yield abnormal stock returns of up to 1.13%. These strong market anomalies related to DSM are observed over the entire sample period and persist after controlling for alternative inventory productivity measures and firm characteristics that are known to predict future stock returns. Further, we reveal that DSM is indicative of lower future earnings and lower sales growth and provide evidence that the observed market inefficiency results from investors’ failure to incorporate all of the information that inventory contains into the pricing of stocks.     

考虑保鲜努力与数量/质量弹性的农产品三级供应链协调优化

以供应具有季节性的农产品为研究对象,研究了由农户-第三方物流供应商（TPLP）-零售商组成的三级供应链协调问题。将农产品的数量和质量损耗均视为TPLP保鲜努力的内生变量,分别建立了分散与集中决策模式下的动态博弈模型。分析表明分散决策模式下供应链各成员的决策会导致产品销售量和销售价格扭曲,严重影响供应链绩效。针对三级供应链中的两个交易过程,并基于对系统中相关决策变量的变化及影响因素分析,分别设计了成本分担和收益共享协调契约,并论证了在一定条件下该组合协调机制可以显著扩大农产品的销售量,实现供应链各成员收益的帕累托改进。最后,通过算例验证了文章结论,并分析了努力水平对不同农产品质量和数量损耗的改善程度（数量/质量弹性）对农产品三级供应链决策及协调效果的影响。     

Three energy scenarios for the United Kingdom

The general principles of constructing scenarios and using them to explore policy options have been described in an earlier paper¹. This paper summarizes the results of a preliminary examination of the three scenarios described there and referred to as ‘business-as-usual’, ‘technical-fix’ and ‘low-growth’. The investigation departs from conventional discussions of fuel policy in that it emphasizes the management of fuel demand, and the interaction between fuel demand and life-style, rather than emphasizing fuel supply policies. The relationship between life style and fuel demand can be quantified using the methods of energy analysis.² This method is capable of fine disaggregation and relatively high accuracy. However the aim of the studies reported here was to establish the range of feasible options and the types of policies needed to accomplish them. For this purpose high accuracy is not important so considerable data aggregation has been retained. The detailed analyses presented in this paper are estimated to be accurate to ±15 per cent.The first part of the paper sets out the analysis of the base year (1968) on which the future projections are based. This involves explaining the principles of energy analysis and the method of projection used. The next sections examine the three scenarios in some detail, starting with the estimation of fuel demand and then looking at the fuel supply and management policies needed.     

On the fair optimization of cost and customer service level in a supply chain under disruption risks

This paper presents a new decision-making problem of a fair optimization with respect to the two equally important conflicting objective functions: cost and customer service level, in the presence of supply chain disruption risks. Given a set of customer orders for products, the decision maker needs to select suppliers of parts required to complete the orders, allocate the demand for parts among the selected suppliers, and schedule the orders over the planning horizon, to equitably optimize expected cost and expected customer service level. The supplies of parts are subject to independent random local and regional disruptions. The fair decision-making aims at achieving the normalized expected cost and customer service level values as much close to each other as possible. The obtained combinatorial stochastic optimization problem is formulated as a stochastic mixed integer program with the ordered weighted averaging aggregation of the two conflicting objective functions. Numerical examples and computational results, in particular comparison with the weighted-sum aggregation of the two objective functions are presented and some managerial insights are reported. The findings indicate that for the minimum cost objective the cheapest supplier is usually selected, and for the maximum service level objective a subset of most reliable and most expensive suppliers is usually chosen, whereas the equitably efficient supply portfolio usually combines the most reliable and the cheapest suppliers. While the minimum cost objective function leads to the largest expected unfulfilled demand and the expected production schedule for the maximum service level follows the customer demand with the smallest expected unfulfilled demand, the equitably efficient solution ensures a reasonable value of expected unfulfilled demand.     

An optimal replenishment model for inventory items with normally distributed deterioration

This paper deals with the inventory replenishment problem for deteriorating items with normally distributed shelf life, continuous time-varying demand, and shortages under the inflationary and time discounting environment. The reasons of choosing normal are twofold: it is one of the most important probability phenomena in the real world due to the classical central limit theorem, and it is also one of the most commonly used lifetime distributions in reliability contexts. The problem is formulated as a dynamic programming model and solved by numerical search techniques. The solutions of the model determine the optimal replenishment schedule over a finite planning horizon so that the present worth of the future costs associated with the system is minimized. In the extensive experiments, we validate the model, demonstrate the optimal replenishment schedule and lot-size, and carry out a comparative study to ascertain its contribution. In addition, sensitivity analysis was provided to help identify the most crucial factors that affect system performance. The experimental result shows that the deteriorating problem solved by an appropriate model (i.e. the proposed normal model) can save the total cost up to 2% approximately. It also identifies that the magnitudes of purchase cost per unit and demand rate are the most significant parameters that affect the replenishment decisions and cost.     

Recent Developments in Dynamic Pricing Research: Multiple Products,Competition, and Limited Demand Information

Dynamic pricing enables a firm to increase revenue by better matching supply with demand, responding to shifting demand patterns, and achieving customer segmentation. In the last 20 years, numerous success stories of dynamic pricing applications have motivated a rapidly growing research interest in a variety of dynamic pricing problems in the academic literature. A large class of problems that arise in various revenue management applications involve selling a given amount of inventory over a finite time horizon without inventory replenishment. In this study, we identify most recent trends in dynamic pricing research involving such problems. We review existing research on three new classes of problems that have attracted a rapidly growing interest in the last several years, namely, problems with multiple products, problems with competition, and problems with limited demand information. We also identify a number of possible directions for future research.     

Interorganizational Collaborative Forecasting and Replenishment Systems and Supply Chain Implications

Collaborative Forecasting and Replenishment (CFAR) is a new interorganizational system that enables retailers and manufacturers to forecast demand and schedule production jointly. The capabilities of CFAR exceed those of the traditional EDI because CFAR allows exchange of complex decision support models and manufacturer/retailer strategies. The proponents of CFAR claim that the fastest way for retailers and manufacturers to slash inventory costs and product shortages is by coordinating their decisions. The impact of CFAR on the supply chain using a model consisting of a manufacturer selling its product through two independent identical retailers is analyzed. The model assumes that the manufacturer doesn't face capacity constraints, the demand is stationary, and retailer prices are constant over time; the shortages are backordered at the manufacturer and retailers to isolate the effects of information sharing in CFAR. The most significant findings relate to the increase in the cost incurred by the nonparticipant in CFAR, the increase in the manufacturer's cost of serving the nonparticipant if the participant can obtain delivery guarantees in return for demand information sharing, and the incentives of the players to move towards universal retailer participation in CFAR. The findings are explained using the risk structure faced by the players in the supply chain. The managerial implications of the impact of CFAR on the supply chain structure are also discussed.     

非平稳需求下考虑碳配额的供应链选址-库存模型与算法研究

针对非平稳需求下考虑碳配额的多期、多需求情景的三级供应链选址-库存问题，构建了库存策略（t，s，S）下供应链运营期望收益最大化的两阶段选址-库存随机优化模型，依据供应链企业不同着眼点下的决策流程，提出了一种三步骤的分层级启发式算法，该算法包含了选址导向和需求导向的两种子问题序贯求解模式。数值算例验证了在不同问题规模及需求类型下算法求解的有效性，同时分析了供应链网络设计、各成本占比和运营收益对不同供应链成本结构、需求不确定性与碳配额的敏感性，并给出了管理上的启示。     

制造商成本削减策略对风险规避型零售商信息共享策略的影响

考虑一个风险中性制造商和一个风险规避零售商构成的供应链，需求随机且受销售价格的影响。在销售季节之前，零售商对需求进行预测，获取需求信号；制造商对生产进行投资降低生产成本。基于零售商的不同信息共享策略及制造商的投资策略，考虑四种不同策略模型，分别得到最优零售价、批发价（及投资水平），并分析需求预测精确度对供应链成员决策和效用的影响。通过四种模型效用的对比分析，探讨制造商的投资策略以及零售商的风险规避态度对零售商信息共享策略的影响。研究发现，零售商共享需求信息对于制造商总是有益的，且制造商总是愿意采取成本削减策略；只有当制造商采取成本削减策略，且其投资成本系数较低时，共享需求信息对零售商才有益。最后，得到了制造商和零售商的均衡策略。     

Vertical Integration with Price‐Setting Competitive Newsvendors*

This article addresses the problem of distribution channel design under demand uncertainty. We consider two manufacturers, each producing a substitutable product and selling it through either a decentralized or an integrated retail store, which is modeled as a price‐setting newsvendor. A multiplicative demand function incorporating a random shock term is assumed. Of primary interest is how demand uncertainty and production cost affect the equilibrium distribution channel structure. Results indicate the following: On the one hand, if the random shock term is uniformly distributed on [0, x], then the equilibrium design does not depend on the variance of the shock. On the other hand, if the random shock term is uniformly distributed on [1 −r, 1 +r], then the equilibrium design does depend on the variance of the shock. In particular, an increase in r favors the integrated structure where both channels are integrated and hurts the decentralized configuration where both channels are decentralized. Additionally, we explain the qualitative similarities and differences between the structural properties of the equilibrium distribution channel structure when demand is, and is not, uncertain. We also establish that production cost always favors the decentralized structure, while it hurts the integrated one. One important managerial implication of our study is that, by reducing the number of decisions made in supply chains, the impact of demand uncertainty can be controlled to a certain extent.     

互补型闭环供应链的收益共享与成本共担契约协调优化

互联网平台开放、协作和分享的特点改变了传统的商业模式。如何将互联网思维与传统产业相融合,理论界和实业界正在进行理论探索和模式实践,突破线上平台商和线下渠道服务商界限以提高服务效率,解决线上平台服务和逆向回收难的瓶颈。本文针对单一线上平台商-单一线下渠道服务商的闭环供应链系统,探讨双方如何在正向供应链进行收益共享、逆向供应链进行渠道成本分担情境下的产品定价和服务优化决策。通过对比收益共享模型和收益共享-成本共担模型发现,线上平台商将部分收益分给线下服务商的同时,如果能分担线下渠道服务建设投入,将更有利于逆向回收和服务商逆向渠道服务水平的提高。最后,还通过数值分析,验证了分成分担比例对平台商和服务商决策的影响,以及对各决策变量的影响关系机理。     

EOQ with independent endogenous supply disruptions

We consider an inventory installation, controlled by the periodic review base stock (S, T) policy and facing a fixed-rate deterministic demand which, if unsatisfied, is backordered. The supply process is unreliable, so supply deliveries may fail according to an independent Bernoulli process; we refer to such failures reflecting the supply service quality and being internal to the supply chain, as endogenous disruptions. We seek to jointly determine the two policy variables, so to minimize long-run average cost. While an approximate model for this problem was recently analyzed, we present an exact analysis, valid for two common accounting schemes for inventory cost evaluation: continuous and end-of-cycle costing. After developing a unified (and exact) average cost model for both costing schemes, the cost for each scheme is analyzed. In both cases, the optimal policy variables and cost prevail in closed-form, having an identical structure to those of EOQ (with backorders). In fact, under continuous costing, the optimal solution reduces to EOQ for perfect supply. Analytical properties, demonstrating the impact of deteriorating supply quality on the optimal policy, are established. Moreover, computations reveal the cost impact of deploying heuristics that either ignore supply disruptions or rely on inaccurate costing information.     

Accurate Response with Refurbished Consumer Returns

Centering around anticipative and reactive capabilities of firms, accurate response is an important supply‐side strategy to deal with demand uncertainty. Clearly, the structure of the possible reaction will crucially influence the optimal anticipative decision making. In this article, we extend the existing literature in this area by including a new reactive capability, namely the utilization of refurbished consumer returns from early sales to react to demand later in the selling season. Because consumer returns depend on previous sales, there is also a direct link to the anticipative supply decision. We capture this effect in a newsvendor‐type model and provide both analytical and numerical insights into the optimal anticipative and reactive decisions as well as the value of refurbishing in terms of the retailer's expected profitability.     

Performance Metric Portfolios: A Framework and Empirical Analysis

The widespread adoption of supply chain management principles suggests that managers recognize the importance of evaluating operational decisions holistically. However, it is often difficult to link specific operational practices to strategic level outcomes and in turn to corporate financial results. This presents problems for both managers and academic researchers attempting to justify the often high cost of operational improvement initiatives in terms of objective accounting metrics. This study provides evidence that it is possible to demonstrate linkages between carefully chosen portfolios of tactical, strategic, and financial metrics. Survey data from 118 manufacturers are used to evaluate hypotheses linking multilevel metrics of performance across three well‐established strategic foci. We present portfolios of metrics drawn from the literature and from the Supply Chain Counciľs supply‐chain operations reference model and related design and customer chain models. Our analysis suggests that metric portfolios in which tactical metrics are designed to match strategic‐level metrics, based on alignment with a specific strategic focus, provide clearer mechanisms for understanding performance linkages.     

Dynamic Pricing and Inventory Management with Regular and Expedited Supplies

We consider a periodic‐review inventory system with regular and expedited supply modes. The expedited supply is faster than the regular supply but incurs a higher cost. Demand for the product in each period is random and sensitive to its selling price. The firm determines its order quantity from each supply in each period as well as its selling price to maximize the expected total discounted profit over a finite or an infinite planning horizon. We show that, in each period if it is optimal to order from both supplies, the optimal inventory policy is determined by two state‐independent thresholds, one for each supply mode, and a list price is set for the product; if only the regular supply is used, the optimal policy is a state‐dependent base‐stock policy, that is, the optimal base‐stock level depends on the starting inventory level, and the optimal selling price is a markdown price that decreases with the starting inventory level. We further study the operational impact of such supply diversification and show that it increases the firm's expected profit, reduces the optimal safety‐stock levels, and lowers the optimal selling price. Thus that diversification is beneficial to both the firm and its customers. Building upon these results, we conduct a numerical study to assess and compare the respective benefit of dynamic pricing and supply diversification.     

Joint Initial Stocking and Transshipment—Asymptotics and Bounds

A common problem faced by many firms in their supply chains can be abstracted as follows. Periodically, or at the beginning of some selling season, the firm needs to distribute finished goods to a set of stocking locations, which, in turn, supply customer demands. Over the selling season, if and when there is a supply‐demand mismatch somewhere, a re‐distribution or transshipment will be needed. Hence, there are two decisions involved: the one‐time stocking decision at the beginning of the season and the supply/transshipment decision throughout the season. Applying a stochastic dynamic programming formulation to a two‐location model with compound Poisson demand processes, we identify the optimal supply/transshipment policy and show that the optimal initial stocking quantities can be obtained via maximizing a concave function whereas the contribution of transshipment is of order square‐root‐of T. Hence, in the context of high‐volume, fast‐moving products, the initial stocking quantity decision is a much more important contributor to the overall profit. The bounds also lead to a heuristic policy, which exhibits excellent performance in our numerical study; and we further prove both the bounds and the heuristic policy are asymptotically optimal when T approaches infinity. Extension to multiple locations is also discussed.     

Reliability evaluation for airline network design in response to fluctuation in passenger demand

This study develops a reliability evaluation method for airline network design. Reliability evaluation in the airline network design phase is considered herein as a post-design task. A series of models for determining flight frequencies on individual routes, evaluating reliability under normal/abnormal demand fluctuations, and providing a priori adjustment of flight frequencies are presented. A case study demonstrating the feasibility of applying the proposed models is provided. Study results not only suggest a post evaluation and adjustment method for airline network design in response to future uncertain fluctuation in passenger demand, but also provide ways to improve flexibility in airline flight frequency decision-making.     

产品召回供应链中线性保险合约与订货决策

考虑一个由单供应商和单制造商组成的二级供应链,在不确定市场需求情形下考虑产品召回,站在制造商角度研究供应链线性保险合约下的最优订货决策以及最优线性保险合约决策。研究表明,最优的订货决策与产品召回概率以及线性保险系数相关,存在唯一的最优订货量来最大化制造商的利润。对于密度函数倒数的导数较大的一般需求分布函数,最优的线性保险系数与线性保险合约的固定费用以及产品召回概率有关。当产品召回概率和线性保险合约的固定费用均较小时或者产品召回概率较大但线性保险合约的固定费用适中时,制造商通过选择线性保险合约可以改善自己的利润。