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.     

航空公司与旅行社的协作机制研究

旅行社是航空公司机票销售网络中非常重要的一环。作为一种特殊的机票代理,为了应对旅游旺季的旅客需求,旅行社往往会提前较长时间从航空公司预订机票。对于航空公司来说,提前预订机票给旅行社是降低销售风险的一个重要途径。在进行机票预订交易时,双方都必须进行慎重的决策;旅行社决策最优的预订数量;航空公司决策最优的供给量。本文正是在对双方各自最优决策探讨的基础上,对双方的协作机制进行了研究。在模型中,我们考虑了双方的缺票损失、航空公司的超额预订等因素。双方协作的目标是在实现整体收益最大化的基础上,同时实现航空公司和旅行社各自的收益更大化。本文提出了一种中间价格调整的协作机制,并说明了这种机制的可行性和收敛性。     

The Role of Behavioral Responses in the Total Economic Consequences of Terrorist Attacks on U.S. Air Travel Targets

U.S. airports and airliners are prime terrorist targets. Not only do the facilities and equipment represent high‐value assets, but the fear and dread that is spread by such attacks can have tremendous effects on the U.S. economy. This article presents the methodology, data, and estimates of the macroeconomic impacts stemming from behavioral responses to a simulated terrorist attack on a U.S. airport and on a domestic airliner. The analysis is based on risk‐perception surveys of these two scenarios. The responses relate to reduced demand for airline travel, shifts to other modes, spending on nontravel items, and savings of potential travel expenditures by U.S. resident passengers considering flying domestic routes. We translate these responses to individual spending categories and feed these direct impact results into a computable general equilibrium (CGE) model of the U.S. economy to ascertain the indirect and total impacts on both the airline industry and the economy as a whole. Overall, the estimated impacts on GDP of both types of attacks exceed $10B. We find that the behavioral economic impacts are almost an order of magnitude higher than the ordinary business interruption impacts for the airliner attack and nearly two orders of magnitude higher for the airport attack. The results are robust to sensitivity tests on the travel behavior of U.S. residents in response to terrorism.     

Sales Force and Competition in Financial Product Markets: The Case of Mexico's Social Security Privatization

This paper examines how sales force impacts competition and equilibrium prices in the context of a privatized pension market. We use detailed administrative data on fund manager choices and worker characteristics at the inception of Mexico's privatized social security system, where fund managers had to set prices (management fees) at the national level, but could select sales force levels by local geographic areas. We develop and estimate a model of fund manager choice where sales force can increase or decrease customer price sensitivity. We find exposure to sales force lowered price sensitivity, leading to inelastic demand and high equilibrium fees. We simulate oft proposed policy solutions: a supply‐side policy with a competitive government player and a demand‐side policy that increases price elasticity. We find that demand‐side policies are necessary to foster competition in social safety net markets with large segments of inelastic consumers.     

The Economic Impacts of a Terrorist Attack on the U.S. Commercial Aviation System

We use data on air passenger travel expenditures per passenger as well as statistical analysis of the air traffic lost for the two-year aftermath of the September 11, 2001, attacks to estimate direct demand losses for air transportation services. These are used along with a national input-output model to assess the full costs of these losses. Depending on assumptions made, the full losses to the U.S. economy were between $214.3 and $420.5 billion. These estimates are similar to those from other studies of such an event, and suggest that the high costs of effective countermeasures may be justified.     

Promotion Planning and Supply Chain Contracting in a High–Low Pricing Environment

Demand forecast errors threaten the profitability of high–low price promotion strategies. This article shows how to match demand and supply effectively by means of two‐segment demand forecasting and supply contracts. We find that demand depends on the path of past retail prices, which leads to only a limited number of reachable demand states. However, forecast errors cannot be entirely eliminated because competitive promotions entail some degree of random (i.e., last‐minute) pricing. A hedging approach can be deployed to distribute demand risk efficiently over multiple promotional campaigns and within the supply chain. A retailer that employs a portfolio of forward, option, and spot contracts can avoid both stockouts and excess inventories while achieving the first‐best solution and Pareto improvements. We provide an improved forecasting method as well as stochastic programs to solve for optimal production and purchasing policies such that the right amount of inventory is available at the right time. By connecting a stockpiling model of demand with the supply side, we derive insights on optimal risk management strategies for both manufacturers and retailers in a market environment characterized by frequent price promotions and multiple discount levels. We employ a data set of the German retail market for a key generator of store traffic—namely, diapers.     

Operational Hedging and Diversification under Correlated Supply and Demand Uncertainty

When facing supply uncertainty caused by exogenous factors such as adverse weather conditions, firms diversify their supply sources following the wisdom of “not holding all eggs in one basket.” We study a firm that decides on investment and production levels of two unreliable but substitutable resources. Applying real options thinking, production decisions account for actual supply capabilities, whereas investment decisions are made in advance. To model triangular supply and demand correlations, we adapt the concepts of random capacity and stochastic proportional yield while using concordant ordered random variables. Optimal profit decreases monotonically in supply correlation and increases monotonically in supply–demand correlation. Optimal resource selection, however, depends on the trivariate interplay of supply and demand and responds non‐monotonically to changing correlations. Moreover, supply hedges (i.e., excess capacity at alternative sources) can be optimal even if supply resources are perfectly positively correlated. To accommodate changing degrees of correlation, the firm adjusts the lower margin capacities under random capacity; but under stochastic proportional production capability, it uses either low‐ or high‐margin capacities to create tailored “scale hedges” (i.e., excess capacity at one source which can partially substitute for diversification).     

Technical Note—Demand Uncertainty Reduction in Decentralized Supply Chains

This note analyzes the effects associated with reducing demand uncertainty in a decentralized supply chain comprising one manufacturer, one retailer, and a wholesale price contract that governs the transactions between them. The demand uncertainty level is parameterized through a mean‐preserving spread, and the manufacturer's and the retailer's equilibrium decisions are solved accordingly. We consider the case of an exogenous retail price as well as the case of an endogenous retail price, and we find in both cases that the manufacturer's and the retailer's expected profits in equilibrium are not necessarily monotone decreasing in the uncertainty level. Thus, we find that, even if the cost of reducing demand uncertainty is zero, uncertainty reduction can hurt rather than benefit either or both members of the supply chain.     

Intersectoral Labor Mobility and the Growth of the Service Sector

One of the most striking changes in the U.S. economy over the past 50 years has been the growth in the service sector. Between 1950 and 2000, service‐sector employment grew from 57 to 75 percent of total employment. However, over this time, the real hourly wage in the service sector grew only slightly faster than in the goods sector. In this paper, we assess whether or not the essential constancy of the relative wage implies that individuals face small costs of switching sectors, and we quantify the relative importance of labor supply and demand factors in the growth of the service sector. We specify and estimate a two‐sector labor market equilibrium model that allows us to address these empirical issues in a unified framework. Our estimates imply that there are large mobility costs: output in both sectors would have been double their current levels if these mobility costs had been zero. In addition, we find that demand‐side factors, that is, technological change and movements in product and capital prices, were responsible for the growth of the service sector.     

Optimal Sourcing and Lead‐Time Reduction under Evolutionary Demand Risk

We develop a real‐options model for optimizing production and sourcing choices under evolutionary supply‐chain risk. We model lead time as an endogenous decision and calculate the cost differential required to compensate for the risk exposure coming from lead time. The shape of the resulting cost‐differential frontier reveals the term structure of supply‐chain risk premiums and provides guidance as to the potential value of lead‐time reduction. Under constant demand volatility, the break‐even cost differential increases in volatility and lead time at a decreasing rate, making incremental lead‐time reduction less valuable than full lead‐time reduction. Stochastic demand volatility increases the relative value of incremental lead‐time reduction. When demand has a heavy right tail, the value of lead‐time reduction depends on how extreme values of demand are incorporated into the forecasting process. The cost‐differential frontier is invariant to discount rates, making the cost of capital irrelevant for choosing between lead times. We demonstrate the managerial implications of the model by applying it first to the classic Sport‐Obermeyer case and then to a supplier‐selection problem faced by a global manufacturer.     

Hazardous Times for Monetary Policy: What Do Twenty‐Three Million Bank Loans Say About the Effects of Monetary Policy on Credit Risk‐Taking?

We identify the effects of monetary policy on credit risk‐taking with an exhaustive credit register of loan applications and contracts. We separate the changes in the composition of the supply of credit from the concurrent changes in the volume of supply and quality, and the volume of demand. We employ a two‐stage model that analyzes the granting of loan applications in the first stage and loan outcomes for the applications granted in the second stage, and that controls for both observed and unobserved, time‐varying, firm and bank heterogeneity through time*firm and time*bank fixed effects. We find that a lower overnight interest rate induces lowly capitalized banks to grant more loan applications to ex ante risky firms and to commit larger loan volumes with fewer collateral requirements to these firms, yet with a higher ex post likelihood of default. A lower long‐term interest rate and other relevant macroeconomic variables have no such effects.     

How Excessive Stage Time Reduction in NPD Negatively Impacts Market Value

We empirically investigate how time reductions in particular product development stages impact market value. Using longitudinal project data from 107 firms, we compare stage times prior to and following investments in new product development process changes. Our analysis reveals a predominance of focus on time reduction in the late stages of product development. We also find support for the existence of an inverted‐U relationship between market performance and time reductions for some of these stages: beta testing and technical implementation. Therefore, while time reductions can improve time to market, we observe a clear limit to the benefits associated with stage time reductions at particular stages. We also investigate the role of strategic contextual factors such as the extent to which a firm's patented innovations rely upon a variety, as opposed to a limited range, of diverse technology classes. The extent of this technology‐span impacts optimal stage time reductions. We perform an in‐depth post hoc analysis with a small set of firms to uncover how they should invest in stage time reduction given our empirical results. The post hoc analysis highlights that some firms are likely overinvesting in stage time reductions and destroying market value.     

IMPACT OF POS DATA SHARING ON SUPPLY CHAIN MANAGEMENT: AN EXPERIMENTAL STUDY

We examine the impact of point of sale (POS) data sharing on ordering decisions in a multi‐echelon supply chain. In particular, we focus on how exposure to POS data may help reduce the “bullwhip effect,” the tendency of orders to increase in variability as one moves up a supply chain. Theoretical studies have shown that exposure to POS data can lead to a reduction in the bullwhip effect when suppliers have no prior knowledge of the demand distribution. The benefit of sharing POS data in stable industries, where the demand distribution is commonly known, is less clear. We study this phenomenon from a behavioral perspective in the context of a simple, serial, supply chain subject to information lags and stochastic demand. We find, using a controlled simulation experiment, that sharing POS information does help reduce some components of the bullwhip effect in a stable demand setting, namely the order oscillation of upstream members. We offer one possible explanation for this improvement by examining the relationship between order decisions and demand line information.     

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.     

Class‐Based Storage with a Finite Number of Items: Using More Classes is not Always Better

Class‐based storage is widely studied in the literature and applied in practice. It divides all stored items into a number of classes according to their turnover. A class of items with higher turnover is allocated to a region closer to the warehouse depot. In the literature, it has been shown that the use of more storage classes leads to a shorter travel time for storing and retrieving items. A basic assumption in this literature is that the required storage space for all items equals their average inventory level, which is valid only if an infinite number of items can be stored in each storage region. This study revisits class‐based storage by considering each storage space to contain only a finite number of items. We develop a travel time model and an algorithm that can be used for determining the optimal number and boundaries of storage classes in warehouses. Different from the conventional research, our findings illustrate that commonly a small number of classes is optimal. In addition, we find the travel time is fairly insensitive to the number of storage classes in a wide range around the optimum. This suggests that a manager can select a near‐optimal number of storage classes in an easy way and need not be worried about the impact of storage‐class reconfigurations. We validate our findings for various cases, including different ABC‐demand curves, space‐sharing factors, number of items, storage rack shapes, discrete storage locations, and stochastic item demand.     

Durability,Transit Lags,and Optimality of Inventory Management Decisions

Two laboratory experiments on a single‐echelon inventory task show that inventory durability interacts with transit lags to create order volatility that exceeds demand volatility. Thus, inventory durability and transit lags cause managers to deviate from inventory decision optimality. Durability creates a large increase in order volatility because players adjust orders insufficiently to reflect current inventory and backlogs, much as they adjust orders insufficiently to reflect holding and backlog costs in newsvendor studies (e.g., Schweitzer and Cachon 2000). Transit lags exacerbate non‐optimal ordering by interfering with players' ability to correct prior errors. Our results suggest that non‐optimal inventory decisions can be driven by inventory and supply chain characteristics, even in the absence of the coordination and information sharing problems studied by Croson et al. (2005) and Sterman (1989a,b). We also examine the influence of features related to personality. We find little evidence that the interactive effects of durability and transit lags are altered by need for cognition, impulsiveness, or locus of control, suggesting that these features make supply chain management extremely difficult. These results imply that retailers and their upstream partners must consider the characteristics of their product and supply chains when interpreting demand signals received from downstream partners.     

Safety Stock,Excess Capacity or Diversification: Trade‐Offs under Supply and Demand Uncertainty

Firms mitigate uncertainty in demand and supply by carrying safety stock, planning for excess capacity and diversifying supply sources. In this study, we provide a framework to jointly optimize these three levers in a periodic review infinite horizon setting, and in particular we examine how one can reduce inventory and capacity investments through proper diversification strategies. Observing that a modified base‐stock inventory policy is optimal, we find that the capacity‐diversification problem is well behaved and characterize the optimal mix of safety stock, excess capacity and extra number of supply sources. We find that higher supply uncertainty results in higher safety stock, more excess capacity, and higher diversification. But safety stock and diversification are non‐monotonic in demand uncertainty. Our results can be extended to situations in which suppliers are heterogeneous, and can be used to develop effective heuristics.     

Impacts of Power Structure on Supply Chains with Uncertain Demand

In this study, we use a game‐theory‐based framework to model power in a supply chain with random and price‐dependent demand and examine how power structure and demand models (expected demand and demand shock) affect supply chain members' performance. We demonstrate that whether a firm benefits from its power depends on the expected demand model but not on demand shock model. A firm benefits from its power only for linear but not for constant elasticity expected demand. The impact of power structure on supply chain efficiency depends on the models of both expected demand and demand shock. With additive shock, supply chain efficiency is highest (lowest) when neither firm dominates for linear (constant elasticity) expected demand. With multiplicative shock, the supply chain efficiency is highest with a power retailer (manufacturer) for linear (constant elasticity) expected demand. The manufacturer always benefits from a reduction in demand uncertainty. However, the retailer loses (benefits) from demand uncertainty reduction for linear (constant elasticity) expected demand. With a power retailer, the retail price is always on the higher end for linear expected demand, and the customer service level is the lowest for constant elasticity expected demand. Consequently, consumers do not necessarily benefit from a power retailer.     

Health Care Supply Chain Design: Toward Linking the Development and Delivery of Care Globally*

This article is motivated by the gap between the growing demand and available supply of high‐quality, cost‐effective, and timely health care, a problem faced not only by developing and underdeveloped countries but also by developed countries. The significance of this problem is heightened when the economy is in recession. In an attempt to address the problem, in this article, first, we conceptualize care as a bundle of goods, services, and experiences—including diet and exercise, drugs, devices, invasive procedures, new biologics, travel and lodging, and payment and reimbursement. We then adopt a macro, end‐to‐end, supply chain–centric view of the health care sector to link the development of care with the delivery of care. This macro, supply chain–centric view sheds light on the interdependencies between key industries from the upstream to the downstream of the health care supply chain. We propose a framework, the 3A‐framework, that is founded on three constructs—affordability, access, and awareness—to inform the design of supply chain for the health care sector. We present an illustrative example of the framework toward designing the supply chain for implantable device–based care for cardiovascular diseases in developing countries. Specifically, the framework provides a lens for identifying an integrated system of continuous improvement and innovation initiatives relevant to bridging the gap between the demand and supply for high‐quality, cost‐effective, and timely care. Finally, we delineate directions of future research that are anchored in and follow from the developments documented in the article.     

Value of and Interaction between Production Postponement and Information Sharing Strategies for Supply Chain Firms

We analyze the value of and interaction between production postponement and information sharing, which are two distinct strategies to reduce manufacturers’ uncertainty about demand. In both single‐level and two‐level supply chains, from the manufacturer's perspective, while information sharing is always valuable, production postponement can sometimes be detrimental. Furthermore, the value of production postponement is not merely driven by savings in inventory holding cost as postponement enables the manufacturer to avoid both excess and shortfall in production. We find that production postponement and information sharing strategies may substitute, complement, or conflict with each other, depending on the extent of the increase in the unit production cost when production is postponed. In a two‐level supply chain, from the retailer's perspective, information sharing and production postponement can be beneficial or detrimental. When information sharing is beneficial to the retailer, the retailer always shares her demand information with the manufacturer voluntarily. In addition, this voluntary information sharing is truthful because inflated or deflated demand information hurts the retailer through a higher wholesale price or a stock‐out. However, the retailer never shares her demand information voluntarily if the manufacturer has already adopted production postponement because production postponement and information sharing strategies always conflict with each other. Even when the retailer does not benefit from information sharing, we show that the manufacturer can always design an incentive mechanism to induce the retailer to share the demand information, irrespective of whether the manufacturer has already implemented production postponement or not. The above findings underscore the need for a careful assessment of demand uncertainty‐reduction strategies before the supply chain players embark upon them.