UPSTREAM VOLATILITY IN THE SUPPLY CHAIN: THE MACHINE TOOL INDUSTRY AS A CASE STUDY

Cyclicality is a well‐known and accepted fact of life in market‐driven economies. Less well known or understood, however, is the phenomenon of amplification as one looks “upstream” in the industrial supply chain. We examine the amplification phenomenon and its implications through the lens of one upstream industry that is notorious for the intensity of the business cycles it faces: the machine tool industry. Amplification of demand volatility in capital equipment supply chains, e. g., machine tools, is particularly large relative to that seen in distribution and component parts supply chains. We present a system dynamics simulation model to capture demand volatility amplification in capital supply chains. We explore the lead‐time, inventory, production, productivity, and staffing implications of these dynamic forces. Several results stand out. First, volatility hurts productivity and lowers average worker experience. Second, even though machine tool builders can do little to reduce the volatility in their order streams through choice of forecast rule, a smoother forecasting policy will lead companies to retain more of their skilled work force. This retention of skilled employees is often cited as one of the advantages that European and Japanese companies have had relative to their U. S. competitors. Our results suggest some insights for supply chain design and management: downstream customers can do a great deal to reduce the volatility for upstream suppliers through their choice of order forecast rule. In particular, companies that use smoother forecasting policies tend to impose less of their own volatility upon their supply base and may consequently enjoy system‐wide cost reduction.     

Decision support for lead time and demand variability reduction

Companies undertaking operations improvement in supply chains face many alternatives. This work seeks to assist practitioners to prioritize improvement actions by developing analytical expressions for the marginal values of three parameters – (i) lead time mean, (ii) lead time variance, and (iii) demand variance – which measure the marginal cost of an incremental change in a parameter. The relative effectiveness of reducing lead time mean versus lead time variance is captured by the ratio of the marginal value of lead time mean to that of lead time variance. We find that this ratio strongly depends on whether the lead time mean and variance are independent or correlated. We illustrate the application of the results with a numerical example from an industrial setting. The insights can help managers determine the optimal investment decision to modify demand and supply characteristics in their supply chain, e.g., by switching suppliers, factory layout, or investing in information systems.     

The Decision of the Supply Chain Executive to Support or Impede Supply Chain Integration: A Multidisciplinary Behavioral Agency Perspective*

Applying the behavioral agency model developed by Wiseman and Gomez‐Mejia (1998) , this article analyzes human resource factors that induce supply chain executives (SCEs) to make decisions that foster or hinder supply chain integration. We examine two internal sources (compensation and employment risk) and one external source (environmental volatility) of risk bearing that can make SCEs more reluctant to make the decision to promote supply chain integration. We argue and empirically confirm the notion that an employment and compensation system that increases SCE risk bearing reduces the SCE's willingness to make risky decisions and thus discourages supply chain integration. We also reveal that this negative relationship becomes stronger under conditions of high environmental volatility. In addressing the “so what?” question, we found empirical support for the hypothesis that supply chain integration positively influences operational performance. Even though this decision has a positive value for the firm, we showed that SCEs discourage supply chain integration when they face higher risk bearing. Hypotheses are tested using a combination of primary survey data and archival measures in a sample of 133 Spanish firms.     

Willingness to Pay for Shifting Inventory Risk: The Role of Contractual Form

In order to reduce their inventory risk, firms can attempt to contract with their suppliers for shorter supply lead‐times, with their buyers for longer demand lead‐times, or both. We designed a controlled laboratory experiment to study contracts that shift a focal firm's inventory risk to its supply chain partners and address two questions. First, is it more effective if the cost of shifting inventory risk is framed as a fixed fee or in per‐unit cost terms? We find that, generally, our participants are willing to pay more to avoid supply–demand mismatches than the expected costs from such mismatches. This tendency to overpay is mitigated under fixed fee schemes. Second, does it matter whether the option to reduce inventory risk is the outcome of either increased responsiveness from the upstream supplier or advanced demand information from the downstream buyer? Our results suggest that this difference, when only a matter of framing, has no significant effect on willingness‐to‐pay.     

Economic analysis of demand uncertainty and delayed information sharing in a third-party managed supply chain

Designers and retailers in consumer products industry are faced with high demand volatility and potential loss of profit from design piracy. Many retailers rely on third-party supply chain managers (SCMs) to manage global supply chains. A SCM starts raw materials procurement and production process based on expected demand and takes financial risks associated with demand uncertainty. But a retailer often delays sharing product design information with SCM forcing it to expedite production and distribution processes incurring additional financial penalties. To analyse economic impact of delayed information sharing under uncertain demand, we develop a mathematical model. Our model indicates that higher demand volatility lessens the effect of penalty associated with delayed information sharing for retailers. The model also shows that for a given demand volatility, per-unit premium increases asymptotically for a retailer compared to marginal production cost increase for SCM. Such findings are not intuitive for SCMs or retailers.     

Demand and Production Management with Uniform Guaranteed Lead Time

Recently, innovation‐oriented firms have been competing along dimensions other than price, lead time being one such dimension. Increasingly, customers are favoring lead time guarantees as a means to hedge supply chain risks. For a make‐to‐order environment, we explicitly model the impact of a lead time guarantee on customer demands and production planning. We study how a firm can integrate demand and production decisions to optimize expected profits by quoting a uniform guaranteed maximum lead time to all customers. Our analysis highlights the increasing importance of lead time for customers, as well as the tradeoffs in achieving a proper balance between revenue and cost drivers associated with lead‐time guarantees. We show that the optimal lead time has a closed‐form solution with a newsvendor‐like structure. We prove comparative statics results for the change in optimal lead time with changes in capacity and cost parameters and illustrate the insights using numerical experimentation.     

The Impact of Forecast Errors on Early Order Commitment in a Supply Chain*

Supply chain partnership involves mutual commitments among participating firms. One example is early order commitment, wherein a retailer commits to purchase a fixed‐order quantity and delivery time from a supplier before the real need takes place. This paper explores the value of practicing early order commitment in the supply chain. We investigate the complex interactions between early order commitment and forecast errors by simulating a supply chain with one capacitated supplier and multiple retailers under demand uncertainty. We found that practicing early order commitment can generate significant savings in the supply chain, but the benefits are only valid within a range of order commitment periods. Different components of forecast errors have different cost implications to the supplier and the retailers. The presence of trend in the demand increases the total supply chain cost, but makes early order commitment more appealing. The more retailers sharing the same supplier, the more valuable for the supply chain to practice early order commitment. Except in cases where little capacity cushion is available, our findings are relatively consistent in the environments where cost structure, number of retailers, capacity utilization, and capacity policy are varied.     

石油储备价值研究:基于供应链视角

石油储备是应对石油供应链危机的重要途径.构建了反映石油供应链运营的线性规划模型,利用该模型模拟供应链发生不同程度的供应危机和价格危机时,30 d、60 d和90 d的石油储备在应对需求及价格危机中的作用.研究结果表明:当需求及价格发生较大幅度上升时,供应链石油储备可以有效抑制需求及价格上升引起的运作成本上涨,且不同规模...     

Optimal Procurement Design of an Assembly Supply Chain with Information Asymmetry

We study a decentralized assembly supply chain in which an assembler (she) assembles a set of n components, each produced by a different supplier (he), into a final product to satisfy an uncertain market demand. Each supplier holds private cost information to himself, for which the assembler only has a subjective estimate. Furthermore, the assembler believes that the suppliers' costs follow a joint discrete probability distribution. The assembler aims to design an optimal menu of contracts to maximize her own expected profit. The assembler's problem is a complex multi‐dimensional constrained optimization problem. We prove that there exists a unique optimal menu of contracts for the assembler, and we further develop an efficient algorithm with a complexity of O(n) to compute the optimal contract. In addition, we conduct a comprehensive sensitivity analysis to analyze how environmental parameters affect individual firm's performance and the value of information to the assembler, to each supplier, and to the supply chain. Our results suggest that each supplier's private cost information becomes more valuable to the assembler and each supplier when the average market demand increases or when the final product unit revenue increases. Surprisingly, when a supplier's cost volatility increases and its mean remains the same, the value of information to the assembler or to each supplier does not necessarily increase. Furthermore, we show that when the suppliers' cost distributions become more positively correlated, the suppliers are always worse off, but the assembler is better off. However, the value of information for the assembler might increase or decrease.     

含服务水平约束的可控提前期供应链Staekelberg模型与协调机制研究

为了有效地缩短订货提前期与降低库存成本,首先从Stackelberg主从对策角度建立了含服务水平约束的可控提前期供应链库存优化模型,提出了该模型的求解算法;然后从集中决策角度建立了另一类含服务水平约束的可控提前期供应链库存优化模型,并提出该模型的求解算法。利用数值分析对两类模型的优化效果进行比较,结果表明:集中决策模式下的优化效果明显优于Stackelberg主从对策模式下的优化效果,但在缺乏合理的激励机制情形下,供应链各参与方未必有足够积极性接受集中决策模式。为此,提出一种库存费用分担机制来激励供应链各参与方接受集中决策模式。     

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.     

Flexible Backup Supply and the Management of Lead‐Time Uncertainty

We consider replenishment decisions for a constant rate demand environment from a supplier with uncertain lead times. We study the potential use of a flexible backup supplier as an emergency response to accurate lead‐time information arriving at (or close after) the beginning of the demand interval and well after an original order with the stochastic lead‐time supplier has been placed. The emergency response decisions involve whether to order and how much from the flexible backup supplier, with the objective of minimizing the cost of meeting demand. We derive the optimal emergency‐response policy and clearly outline its implications on the optimized safety lead time of the original order placement and on the cost of meeting demand. We examine the impact on the use of the flexible backup supplier of factors like the arrival time of accurate lead‐time information and the response lead time of the backup supplier. We further study the potential benefits of the use of the flexible backup supplier in a dual role: as one of the two suppliers in a redundant supply system assigned to originally meet the demand and as an emergency response to later‐arriving lead‐time information. Our numerical studies illustrate the benefits from the use of the flexible backup supplier as an emergency response, but for reasonable purchase premiums and short lead times of flexible backup supply options, their use in a dual (regular and emergency response) role often leads to improved performance over safety lead‐time single and uncertain lead‐time supplier‐replenishment strategies. The benefits of the backup supply options are accentuated the higher the lead‐time uncertainty of the stochastic lead‐time supplier is.     

Keep Your Friends Close? Supply Chain Design and Disruption Risk

In this article, we evaluate the relationship between supply chain design decisions and supply chain disruption risk. We explore two supply chain design strategies: (i) the dispersion of supply chain partners to reduce supply chain disruption risk versus (ii) the co‐location of supply chain partners to reduce supply chain disruption risk. In addition, we assess supply chain disruption risk from three perspectives: the inbound material flow from the supplier (supply side), the internal production processes (internal), and the outbound material flow to the customer (customer side) as a disruption can occur at any of these locations. We measure disruption risk in terms of stoppages in flows, reductions in flow, close calls (disruptions that were prevented at the last minute), disruption duration (time until normal operation flow was restored), and the spread of disruptions all the way through the supply chain. We use seemingly unrelated regression (SUR) to analyze our data, finding that lead times, especially supply side lead times, are significantly associated with higher levels of supply chain disruption risk. We find co‐location with suppliers appears to have beneficial effects to the reduction of disruption duration, and, overall supply side factors have a higher impact when it comes to supply chain disruption risk than comparable customer side factors.     

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。     

The Perishability of Online Grocers*

In this article we explore the profitability of different operations models used by online grocers and develop a linear demand model in a competitive setting to better understand the trade‐offs made by two competing online grocers in choices for distribution strategy (leverage or direct) and product focus (perishable or nonperishable). We find that the results derived in the duopoly setting are different from those in a monopolistic setting. Specifically, we determine that there is a threshold value for the secondary competitive effects in the demand function that determines how the prices and profitability of an online grocer will be affected by the supply chain length of its competitor. There is also a threshold value for the ratio of supply chain lengths of the two competitors that determines whether product perishability increases or decreases profits. We demonstrate that the existence of this threshold is robust when considering capacity constraints. Further, we show, assuming that supply chain length can be optimized, how the relative size of the infrastructure change cost (when compared with that of the competitor) coupled with the perishability of the product determines the profitability of an investment leading to a shorter supply chain.     

On Optimal Expediting Policy for Supply Systems with Uncertain Lead‐Times

We examine the role of expediting in dealing with lead‐time uncertainties associated with global supply chains of “functional products” (high volume, low demand uncertainty goods). In our developed stylized model, a retailer sources from a supplier with uncertain lead‐time to meet his stable and known demand, and the supply lead‐time is composed of two random duration stages. At the completion time of the first stage, the retailer has the option to expedite a portion of the replenishment order via an alternative faster supply mode. We characterize the optimal expediting policy in terms of if and how much of the order to expedite and explore comparative statics on the optimal policy to better understand the effects of changes in the cost parameters and lead‐time properties. We also study how the expediting option affects the retailer's decisions on the replenishment order (time and size of order placement). We observe that with the expediting option the retailer places larger orders closer to the start of the selling season, thus having this option serve as a substitute for the safety lead‐time and allowing him to take increased advantages of economies of scale. Finally we extend the basic model by looking at correlated lead‐time stages and more than two random lead‐time stages.     

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。     

Optimal Dynamic Order Scheduling under Capacity Constraints Given Demand‐Forecast Evolution

We consider a manufacturer without any frozen periods in production schedules so that it can dynamically update the schedules as the demand forecast evolves over time until the realization of actual demand. The manufacturer has a fixed production capacity in each production period, which impacts the time to start production as well as the production schedules. We develop a dynamic optimization model to analyze the optimal production schedules under capacity constraint and demand‐forecast updating. To model the evolution of demand forecasts, we use both additive and multiplicative versions of the martingale model of forecast evolution. We first derive expressions for the optimal base stock levels for a single‐product model. We find that manufacturers located near their market bases can realize most of their potential profits (i.e., profit made when the capacity is unlimited) by building a very limited amount of capacity. For moderate demand uncertainty, we also show that it is almost impossible for manufacturers to compensate for the increase in supply–demand mismatches resulting from long delivery lead times by increasing capacity, making lead‐time reduction a better alternative than capacity expansion. We then extend the model to a multi‐product case and derive expressions for the optimal production quantities for each product given a shared capacity constraint. Using a two‐product model, we show that the manufacturer should utilize its capacity more in earlier periods when the demand for both products is more positively correlated.     

Forecast Updating and Supplier Coordination for Complementary Component Purchases

We study a supply chain where an original equipment manufacturer (OEM) buys subassemblies, comprised of two complementary sets of components, from a contract manufacturer (CM). The OEM provides a demand forecast at the time when the CM must order the long lead‐time set of components, but must decide whether or not to provide updated forecasts as a matter of practice. Forecast updates affect the CM's short lead‐time purchase decision, and the anticipation of updates may also affect the long lead‐time purchase decision. While the OEM and CM both incur lost sales costs, the OEM can decide whether or not to share the overage costs otherwise fully borne by the CM. We investigate when the OEM is better served by committing to provide updated forecasts and/or committing to share overage costs. For a distribution‐free, two‐stage forecast‐update model, we show that (1) the practice of providing forecast updates may be harmful to the OEM and (2) at the OEM's optimal levels of overage risk sharing, the CM undersupplies relative to the supply chain optimal quantity. For a specific forecast‐update model, we computationally investigate conditions under which forecast updating and risk sharing are in the best interest of the OEM.     

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.