Coordination of Supply Chains with Risk‐Averse Agents

The extant supply chain management literature has not addressed the issue of coordination in supply chains involving risk‐averse agents. We take up this issue and begin with defining a coordinating contract as one that results in a Pareto‐optimal solution acceptable to each agent. Our definition generalizes the standard one in the risk‐neutral case. We then develop coordinating contracts in three specific cases: (i) the supplier is risk neutral and the retailer maximizes his expected profit subject to a downside risk constraint; (ii) the supplier and the retailer each maximizes his own mean‐variance trade‐off; and (iii) the supplier and the retailer each maximizes his own expected utility. Moreover, in case (iii), we show that our contract yields the Nash Bargaining solution. In each case, we show how we can find the set of Pareto‐optimal solutions, and then design a contract to achieve the solutions. We also exhibit a case in which we obtain Pareto‐optimal sharing rules explicitly, and outline a procedure to obtain Pareto‐optimal solutions.     

Collaboration and Coordination in Supply Chain Management and E‐Commerce

Research in Supply Chain Management and Electronic Commerce has grown dramatically in the past decade as firms have intensified efforts to streamline operations and improve service to a diverse and demanding customer base. Central to this theme is the need for firms to look outside of their organizations for opportunities to collaborate and coordinate with partners to ensure that the supply chain is both efficient and responsive to dynamic market needs. Such collaboration and coordination opportunities introduce new challenges and complexities as a result of increased problem scale and scope, and potentially conflicting incentives among different supply chain players. Motivated by these new challenges, this special issue explores a range of coordination and collaboration problems, stressing the role of information and associated technologies in facilitating and enabling supply chain integration.     

Scenario Discovery with Multiple Criteria: An Evaluation of the Robust Decision‐Making Framework for Climate Change Adaptation

There is increasing concern over deep uncertainty in the risk analysis field as probabilistic models of uncertainty cannot always be confidently determined or agreed upon for many of our most pressing contemporary risk challenges. This is particularly true in the climate change adaptation field, and has prompted the development of a number of frameworks aiming to characterize system vulnerabilities and identify robust alternatives. One such methodology is robust decision making (RDM), which uses simulation models to assess how strategies perform over many plausible conditions and then identifies and characterizes those where the strategy fails in a process termed scenario discovery. While many of the problems to which RDM has been applied are characterized by multiple objectives, research to date has provided little insight into how treatment of multiple criteria impacts the failure scenarios identified. In this research, we compare different methods for incorporating multiple objectives into the scenario discovery process to evaluate how they impact the resulting failure scenarios. We use the Lake Tana basin in Ethiopia as a case study, where climatic and environmental uncertainties could impact multiple planned water infrastructure projects, and find that failure scenarios may vary depending on the method used to aggregate multiple criteria. Common methods used to convert multiple attributes into a single utility score can obscure connections between failure scenarios and system performance, limiting the information provided to support decision making. Applying scenario discovery over each performance metric separately provides more nuanced information regarding the relative sensitivity of the objectives to different uncertain parameters, leading to clearer insights on measures that could be taken to improve system robustness and areas where additional research might prove useful.     

Timing the Use of Breast Cancer Risk Information in Biopsy Decision‐Making

Available clinical evidence is inconclusive on whether radiologists should use the patient risk profile information when interpreting mammograms. On the one hand, risk profile information is informative and can improve radiologists’ performance, but on the other hand, it may impair their judgment by introducing biases in mammography interpretation. Therefore, it is important to assess whether and when profile information use translates into improved outcomes. We model the use of profile information in mammography, using a decision theoretic approach and explore the value of profile information using three process design choices: mammography only, unbiased, and biased reading. We estimate the parameters of our model using clinical data and find that using profile information along with the mammography information can achieve a better performance than not using the profile information. However, the better performance is contingent on the weight assigned to the profile information as well as the extent of bias due to profile information. Translating our findings into clinical practice would require properly designed experiments aiming to quantify the effect of the timing and the use of profile information on performance while accounting for radiologist and patient characteristics. When conducting an experiment is not feasible, a uniform operational sequence for interpreting mammograms and related guidelines may be a useful starting point to improve the quality of mammography operations.     

Using Real‐Time Decision Tools to Improve Distributed Decision‐Making Capabilities in High‐Magnitude Crisis Situations*

Multi‐organizational collaborative decision making in high‐magnitude crisis situations requires real‐time information sharing and dynamic modeling for effective response. Information technology (IT) based decision support tools can play a key role in facilitating such effective response. We explore one promising class of decision support tools based on machine learning, known as support vector machines (SVM), which have the capability to dynamically model and analyze decision processes. To examine this capability, we use a case study with a design science approach to evaluate improved decision‐making effectiveness of an SVM algorithm in an agent‐based simulation experimental environment. Testing and evaluation of real‐time decision support tools in simulated environments provides an opportunity to assess their value under various dynamic conditions. Decision making in high‐magnitude crisis situations involves multiple different patterns of behavior, requiring the development, application, and evaluation of different models. Therefore, we employ a multistage linear support vector machine (MLSVM) algorithm that permits partitioning decision maker response into behavioral subsets, which can then individually model and examine their diverse patterns of response behavior. The results of our case study indicate that our MLSVM is clearly superior to both single stage SVMs and traditional approaches such as linear and quadratic discriminant analysis for understanding and predicting behavior. We conclude that machine learning algorithms show promise for quickly assessing response strategy behavior and for providing the capability to share information with decision makers in multi‐organizational collaborative environments, thus supporting more effective decision making in such contexts.     

Decision Making in Cross‐Functional Teams: The Role of Decision Power*

Through a series of game‐theoretical models, this study systematically examines decision making in cross‐functional teams. It provides a framework for the design of an organization‐specific decision‐making process and for the alignment of a team's microdecision with the “optimal” decision that maximizes the firm's payoff. This study finds that even without changing the team leader, firms could change and even dictate the team's microdecision outcome via adjusting the team member's seniority, empowering team members with veto power or involving a supervisor as a threat to overrule the team decision. This finding implies that to reposition products in the marketplace, structuring cross‐functional teams’ microdecision‐making processes is essential.     

Controversial Role of GPOs in Healthcare‐Product Supply Chains

This paper examines the controversial role that Group Purchasing Organizations (GPOs) play in the supply chains for healthcare products. Among the controversies, perhaps the most fundamental one is whether or not GPOs reduce purchasing costs for their members. However, the fiercest controversy is around the “contract administration fees (CAFs)” that GPOs charge to manufacturers. We examine these and other controversies using a Hotelling duopoly model. Among our conclusions: GPOs increase competition between manufacturers and lower prices for healthcare providers. However, GPOs reduce manufacturers' incentives to introduce innovations to existing products. We also demonstrate that the existence of lower off‐contract prices is not, per se, evidence of anticompetitive behavior on the part of GPOs. Indeed, we demonstrate that, under certain circumstances, the presence of a GPO lowers off‐contract prices. We also examine the consequences of eliminating the “safe harbor” provisions that permit healthcare GPOs to charge CAFs to manufacturers, and conclude that it would not affect any party's profits or costs.     

Complexity and Self‐Sustainment in Disaster Response Supply Chains

Governmental organizations play a major role in disaster relief operations. Supply chains set up to respond to disasters differ dramatically in many dimensions that affect the cost of relief efforts. One factor that has been described recently is self‐sustainment, which occurs when supplies consumed by intermediate stages of a supply chain must be provided via the chain itself because they are not locally available. This article applies the concept of self‐sustainment to response supply chains. A mathematical model of a self‐sustaining response supply chain is developed. Analysis of this model yields insights about the relationships and interactions among self‐sustainment, speed of disaster onset, dispersion of impact, and the cost of the relief efforts.     

The Cry Wolf Effect and Weather‐Related Decision Making

Despite improvements in forecasting extreme weather events, noncompliance with weather warnings among the public remains a problem. Although there are likely many reasons for noncompliance with weather warnings, one important factor might be people's past experiences with false alarms. The research presented here explores the role of false alarms in weather‐related decision making. Over a series of trials, participants used an overnight low temperature forecast and advice from a decision aid to decide whether to apply salt treatment to a town's roads to prevent icy conditions or take the risk of withholding treatment, which resulted in a large penalty when freezing temperatures occurred. The decision aid gave treatment recommendations, some of which were false alarms, i.e., treatment was recommended but observed temperatures were above freezing. The rate at which the advice resulted in false alarms was manipulated between groups. Results suggest that very high and very low false alarm rates led to inferior decision making, but that lowering the false alarm rate slightly did not significantly affect compliance or decision quality. However, adding a probabilistic uncertainty estimate in the forecasts improved both compliance and decision quality. These findings carry implications about how weather warnings should be communicated to the public.     

Decision Making and Cognition in Multi‐Echelon Supply Chains: An Experimental Study

Supply chain performance often depends on the individual decisions of channel members. Even when individuals have access to relevant information, order variation tends to increase when moving up the supply chain, a phenomenon known as the bullwhip effect. While prior research has investigated several structural/environmental factors which can mitigate the bullwhip effect, the underlying behavioral factors contributing to it are an open question. Using a production and distribution decision‐making simulation representing a four‐stage serial supply chain, we find that the cognitive profile of decision makers contributes to the bullwhip effect. We found that the specific decision tendency to underweight the supply line is linked to an individual's level of cognitive reflection. Furthermore, performance differs for entire supply chains and for specific echelons, and holds under standard mitigation efforts. The findings have implications for supply chain design, education, and industry.     

The Impact of Product Lifecycle on Capacity Planning of Closed‐Loop Supply Chains with Remanufacturing

Product recovery operations in reverse supply chains face rapidly changing demand due to the increasing number of product offerings with reduced lifecycles. Therefore, capacity planning becomes a strategic issue of major importance for the profitability of closed‐loop supply chains. This work studies a closed‐loop supply chain with remanufacturing and presents dynamic capacity planning policies developed through the methodology of System Dynamics. The key issue of the paper is how the lifecycles and return patterns of various products affect the optimal policies regarding expansion and contraction of collection and remanufacturing capacities. The model can be used to identify effective policies, to conduct various “what‐if” analyses, and to answer questions about the long‐term profitability of reverse supply chains with remanufacturing. The results of numerical examples with quite different lifecycle and return patterns show how the optimal collection expansion/contraction and remanufacturing contraction policies depend on the lifecycle type and the average usage time of the product, while the remanufacturing capacity expansion policy is not significantly affected by these factors. The results also show that the collection and remanufacturing capacity policies are insensitive to the total product demand. The insensitivity of the optimal policies to total demand is a particularly appealing feature of the proposed model, given the difficulty in obtaining accurate demand forecasts.     

Robust Climate Policies Under Uncertainty: A Comparison of Robust Decision Making and Info‐Gap Methods

This study compares two widely used approaches for robustness analysis of decision problems: the info‐gap method originally developed by Ben‐Haim and the robust decision making (RDM) approach originally developed by Lempert, Popper, and Bankes. The study uses each approach to evaluate alternative paths for climate‐altering greenhouse gas emissions given the potential for nonlinear threshold responses in the climate system, significant uncertainty about such a threshold response and a variety of other key parameters, as well as the ability to learn about any threshold responses over time. Info‐gap and RDM share many similarities. Both represent uncertainty as sets of multiple plausible futures, and both seek to identify robust strategies whose performance is insensitive to uncertainties. Yet they also exhibit important differences, as they arrange their analyses in different orders, treat losses and gains in different ways, and take different approaches to imprecise probabilistic information. The study finds that the two approaches reach similar but not identical policy recommendations and that their differing attributes raise important questions about their appropriate roles in decision support applications. The comparison not only improves understanding of these specific methods, it also suggests some broader insights into robustness approaches and a framework for comparing them.     

Joint Mail‐In Rebate Decisions in Supply Chains Under Demand Uncertainty

We study the joint decisions of offering mail‐in rebates (MIRs) in a single‐manufacturer–single‐retailer supply chain using a game theoretic framework. Either party can offer an MIR to the end consumer if it is in his best interest. The consumer demand is stochastic and depends on the product price and the amount of MIRs. When the retail price is exogenous, we show the existence of a unique Nash equilibrium under both additive and multiplicative demand functions and characterize it completely. We show that any of the following four scenarios can be the equilibrium: both parties offer MIR, only one party offers MIR, none offers MIR. When the retail price is a decision variable for the retailer and the rebate redemption rate increases with the amount of MIR, we once again prove the existence of a unique Nash equilibrium where both the retailer and the manufacturer offer MIRs. Using a numerical study, we show that the average post‐purchase price of the product is higher not only than the perceived pre‐purchase price but also than the newsvendor optimal price without an MIR. This implies that an MIR makes a product look cheaper while the consumers actually pay more on average.     

Rational Inattention Dynamics: Inertia and Delay in Decision‐Making

We solve a general class of dynamic rational inattention problems in which an agent repeatedly acquires costly information about an evolving state and selects actions. The solution resembles the choice rule in a dynamic logit model, but it is biased toward an optimal default rule that is independent of the realized state. The model provides the same fit to choice data as dynamic logit, but, because of the bias, yields different counterfactual predictions. We apply the general solution to the study of (i) the status quo bias; (ii) inertia in actions leading to lagged adjustments to shocks; and (iii) the tradeoff between accuracy and delay in decision‐making.     

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).      

Planning and Scheduling in Supply Chains: An Overview of Issues in Practice

This paper gives an overview of the theory and practice of planning and scheduling in supply chains. It first gives an overview of the various planning and scheduling models that have been studied in the literature, including lot sizing models and machine scheduling models. It subsequently categorizes the various industrial sectors in which planning and scheduling in the supply chains are important; these industries include continuous manufacturing as well as discrete manufacturing. We then describe how planning and scheduling models can be used in the design and the development of decision support systems for planning and scheduling in supply chains and discuss in detail the implementation of such a system at the Carlsberg A/S beerbrewer in Denmark. We conclude with a discussion on the current trends in the design and the implementation of planning and scheduling systems in practice.     

Balancing Inventory and Stockout Risk in Retail Supply Chains Using Fast‐Ship

In this study, we consider a two‐retailer, one‐supplier supply chain in which retailers satisfy excess demand by offering to directly ship out‐of‐stock items on an expedited basis at no extra cost to customers. This practice is referred to as the fast‐ship option. We consider two scenarios along with the fast‐ship option. In the first scenario, retailers transship when out of stock, whereas in the second scenario, they do not. If they do not transship, some customers may perform the search on their own. In each scenario, the wholesale prices are either exogenous, or chosen endogenously by the supplier. For both cases, we determine the supplier's and the retailers’ optimal decisions. The key research question we ask and answer is the following: which of the two scenarios is preferred by either player when all decisions are made optimally? We show that when fewer customers are willing to search on their own and wholesale prices are exogenous, both the supplier and the retailers prefer to transship. In addition, the decision maker in a centralized chain will have the exact same preference as that of players in a decentralized setting when the retailers’ and the supplier's preferences coincide and wholesale prices are exogenous. This preference concordance does not hold if wholesale prices are endogenous.     

New Product Development Decision‐Making Effectiveness: Comparing Individuals,Face‐To‐Face Teams,and Virtual Teams*

A total of 411 subjects participated in two decision‐making experiments in order to examine the effectiveness of new product development project continuation decisions. Using escalation of commitment theory, in Study 1, individual versus face‐to‐face team decision‐making effectiveness was compared. Study 2, an extension of Study 1, compared the new product development decision‐making effectiveness of individuals, face‐to‐face teams, and virtual teams. A virtual team is a geographically and temporally dispersed and electronically communicating work group. In Study 2, the virtual teams communicated asynchronously via groupware technology. Our findings suggest that teams make more effective decisions than individuals, and virtual teams make the most effective decisions.     

Optimization and Coordination of Fresh Product Supply Chains with Freshness‐Keeping Effort

We consider a supply chain in which a distributor procures from a producer a quantity of a fresh product, which has to undergo a long‐distance transportation to reach the target market. During the transportation process, the distributor has to make an appropriate effort to preserve the freshness of the product, and his success in this respect impacts on both the quality and quantity of the product delivered to the market. The distributor has to determine his order quantity, level of freshness‐keeping effort, and selling price, by taking into account the wholesale price of the producer, the cost of the freshness‐keeping effort, the likely spoilage of the product during transportation, and the possible demand for the product in the market. The producer, on the other hand, has to determine the wholesale price based on its effect on the order quantity of the distributor. We develop a model to study this problem, and characterize each party's optimal decisions in both decentralized and centralized systems. We further develop an incentive scheme to facilitate coordination between the two parties. Computational results are reported to show the effects of freshness‐keeping efforts.