Modeling Supply Chain Dynamics: A Multiagent Approach*

A global economy and increase in customer expectations in terms of cost and services have put a premium on effective supply chain reengineering. It is essential to perform risk-benefit analysis of reengineering alternatives before making a final decision. Simulation provides an effective pragmatic approach to detailed analysis and evaluation of supply chain design and management alternatives. However, the utility of this methodology is hampered by the time and effort required to develop models with sufficient fidelity to the actual supply chain of interest. In this paper, we describe a supply chain modeling framework designed to overcome this difficulty. Using our approach, supply chain models are composed from software components that represent types of supply chain agents (e.g., retailers, manufacturers, transporters), their constituent control elements (e.g., inventory policy), and their interaction protocols (e.g., message types). The underlying library of supply chain modeling components has been derived from analysis of several different supply chains. It provides a reusable base of domain-specific primitives that enables rapid development of customized decision support tools.     

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。     

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.     

Integrating performance and risk aspects of supply chain design processes

Abstract

Supply chain design is a complex and relatively poorly structured process, involving choosing many decisional parameters and it usually requires consideration of numerous sources of uncertainty. Many conventional processes of supply chain design involve taking a deterministic approach, using point estimates, on important measures of supply chain effectiveness such as cost, quality, delivery reliability and service levels. Supply chain disruptions are often separately considered as risks, both in the research literature and in practice, meaning that a purely traditional risk management and risk minimization approach is taken. We have developed and applied an approach that combines the intellect and experience of the supply chain designer with the power of evaluation provided by a Monte Carlo simulation model, which uses decision analysis techniques to explicitly incorporate the full spectrum of uncertain quantities across the set of alternative supply chain designs being considered. After defining and setting out the general decision variables and uncertainty factors for 16 distinct supply chain design decision categories, we then apply that approach to combine the decision-makers’ heuristics with the probabilistic modeling approach, iteratively, to achieve the best of both elements of such an approach. This novel approach to fully integrating performance and risk elements of supply chain designs is then illustrated with a case study. Finally, we call for further developmental research and field work to refine this approach.     

Supplier Selection Behavior Under Uncertainty: Contextual and Cognitive Effects on Risk Perception and Choice

Buyers often make supplier selection decisions under conditions of uncertainty. Although the analytical aspects of supplier selection are well developed, the psychological aspects are less so. This article uses supply chain management and behavioral decision theories to propose that attributes of the purchasing situation (category difficulty, category importance, and contingent pay) affect cognition that, in turn, affects a supply manager's choice. We conducted a supplier selection behavioral experiment with practicing managers to test the model's hypotheses. When the context involves an important or difficult sourcing category, higher risk perceptions exist that increase preference for a supplier with more certain outcomes, even when that choice has a lower expected payoff. However, the presence of contingent pay decreases risk perceptions through higher perceived supplier control. We also find that a manager's risk propensity increases preferences for a supplier with less certain outcomes regardless of perceived risk. Our model and results provide a theoretical framework for further study into the cognitive aspects of supplier selection behavior and provide insight into biases that influence practicing supply chain managers.     

Designing Distribution Systems to Support Vendor Strategies in Supply Chain Management*

The interactions among a firm's distribution strategy, market share, and distribution costs are an important consideration in the design of supply chain networks. However, these interactions are largely ignored by existing distribution system design methodologies, which assume demand is constant regardless of the firm's distribution strategy. This paper describes a multidisciplinary framework that considers these interactions in the design of “profit maximizing” distribution networks. The framework employs two major decision support methodologies: (1) binary logit models for estimating market share considering various demand-influencing parameters such as product price and distribution service, and (2) a mixed-integer programming (MIP) model for finding optimal distribution network designs. We applied the framework to an actual design problem facing a national distributor of industrial chemical products. The test results verify the framework's large-scale capability and the potential benefit of the integrated solution methodology.     

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

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

Use of Advances in Technology for Maritime Risk Assessment

The maritime industry is moving toward a "goal-setting" risk-based regime. This opens the way to safety engineers to explore and exploit flexible and advanced risk modeling and decision-making approaches in the design and operation processes. In this article, following a brief review of the current status of maritime risk assessment, a design/operation selection framework and a design/operation optimization framework are outlined. A general discussion of control engineering techniques and their application to risk modeling and decision making is given. Four novel risk modeling and decision-making approaches are then outlined with illustrative examples to demonstrate their use. Such approaches may be used as alternatives to facilitate risk modeling and decision making in situations where conventional techniques cannot be appropriately applied. Finally, recommendations on further exploitation of advances in general engineering and technology are suggested with respect to risk modeling and decision making.     

考虑延时效应的供应链动态合作广告策略研究

广告的延时效应是供应链广告过程中的普遍现象,对供应链合作广告策略的制定具有重要影响.文章研究当产品品牌信誉受广告延时效应影响时供应链的合作广告策略问题,建立了含有时间延迟的品牌信誉动态模型和考虑品牌信誉的产品销售量模型.运用极大值原理,得到了制造商和零售商在分散式决策和集中式决策下的最优广告投入、品牌信誉和利润,以及分散式决策下制造商的最优合作广告参与率.研究发现:在集中式决策下制造商和零售商的最优广告投入和产品销售量均高于分散式决策下的相应值;延迟时间存在一个阈值,当延迟时间低于该阈值时,集中式决策下的供应链利润较高,反之则分散式决策下的供应链利润较高.研究结果为供应链合作广告策略的制定及供应链决策机制的选择提供了一定的参考.最后,通过数值算例分析了广告延迟时间对供应链最优广告策略及决策机制的影响.     

The Development Manager's Advisory System: A Knowledge-Based DSS Tool for Project Assessment*

This research investigates whether the knowledge-based decision support system (KBDSS) paradigm provides the necessary supporting structure and developmental framework for product development evaluation. To address the research questions posed in this study, it is necessary to develop and implement KBDSS's at specific decision points along the product development cycle. This paper describes the design, development, and implementation of a KBDSS to support a product development manager's decision concerning full-scale development of a new product. From the systems design perspective, this paper addresses the integration and innovative use of a variety of techniques for knowledge acquisition, modeling, and processing. The approach utilized obtains the benefits of normative modeling as well as the flexibility and developmental advantages of knowledge-based systems. Since its implementation, the system has been successfully used by a development manager to support his recommendation for an ongoing project. His complete satisfaction with this system served as the impetus for the design and development of a multi-expert system which was implemented at the strategic level.     

A hybrid approach to configure eco-efficient supply chains under consideration of performance and risk aspects

Formal models that support multi-criteria decision making represent a strongly growing area in sustainable supply chain management research. However, uncertainties and risks are seldom considered in quantitative models for green supply chain (SC) design. The paper at hand suggests a hybrid approach to configure an eco-efficient SC for a new product under consideration of economic and environmental risks. Discrete-event simulation is applied to assess the financial, operational and environmental performance of different SC configuration options while the value-at-risk concept is adapted to evaluate related SC risks. The analytic hierarchy process is employed to solve the resulting multi-criteria decision problem of choosing the best option. The approach is illustrated at a case example of a fast moving consumer goods manufacturer.     

A Public Transportation Decision‐Making Model within a Metropolitan Area

Understanding the decision‐making factors associated with public transportation is essential in strategic development of public transportation to improve acceptance and utilization of mass transit systems. This research analyzes factors affecting attitudes toward public transportation and the choice of transportation mode by investigating the public transportation decision‐making process of working professionals using a survey methodology. The objectives of this research are to model the transportation decision‐making process of public transportation users in a metropolitan area and to determine key factors that affect the public transportation choices made by potential public transportation users. This study contributes to the literature by developing and testing an integrated theoretical framework for modeling an individual's public transportation decision‐making process using four independent variables: Perceived Public Transportation Security, Knowledge, Price, and Convenience. We develop the proposed theoretical framework based upon the extant literature and tested it using partial least squares structural equation modeling (PLS‐SEM). Based on the Theory of Reasoned Action, the Theory of Planned Behavior, and utility theory, we develop the factors and refine associated items using confirmatory factor analysis.     

Decision knowledge sharing: flexible supply chains in KM context

Supply chain excellence has a real impact on business strategy. Building supply chains as flexible systems represents one of the most exciting opportunities to create value and one of the most challenging tasks for the policy makers. It requires integrated decision making amongst autonomous chain partners with effective decision knowledge sharing between them. The key to success lies in knowing which decision has more impact on the overall performance and this can be achieved by appropriate knowledge sharing. In this context, knowledge management (KM) can be used as an effective approach to achieve knowledge sharing and decision synchronisation in supply chains. Flexible supply chains (FSC_s) are more complex and involve multiple autonomous players with varying technical cultures (affects knowledge mindsets), managerial background (affects decision knowledge) and supply chain management (SCM) exposures (affects knowledge sharing attitudes). Thus there is a need to develop demo models that can encourage chain managers towards collaborative knowledge sharing in the supply chains. This paper presents the application of one such model based on decision knowledge sharing (DKS) for improved supply chain management. A simulation model of a flexible supply chain based on DKS framework is developed for demo purposes. The key results are highlighted along with industry implications. The cost based performance of DKS at different levels of flexibility is studied. Thus a careful analysis of the chain with a focus on collaborative decisions is useful to ensure success. This paper addresses this interesting and challenging domain.     

Special Research Focus on Supply Chain Linkages: Challenges for Design and Management in the 21st Century*

Supply chain management is a central and important area for academic research due to its impact on firms competing in today's global economy. Managing the flow of materials from supply sources to the ultimate customer represents a major challenge for today's managers. To assist managers, the concept of supply chain management has been adopted by many business leaders as an important way to assist in designing, planning, and controlling the network of facilities and tasks that comprise the many stages of the supply chain. In turn, the flow of academic research in the area has increased to provide a better set of guidelines for effective implementation and execution. This article sets the stage for recently completed research concentrating on supply chain management issues. First, a definition of supply chain management is provided and compared to recent usage in this area and logistics management. Also, a framework is provided that structures this dynamic and complex management task. Second, a review of past research is presented to illustrate the many paths supply chain management has traveled, and important contributions to supply management understanding and decision making. Third, recently completed research articles are introduced that have been selected to be part of this special issue of Decision Sciences. And fourth, future research directions for supply chain management that need to be pursued by interested investigators are discussed.     

A Dynamic Collaboration Capability as a Source of Competitive Advantage

The resource‐based view of the firm argues the essence of decision making is to determine how firm and supply chain resources can be configured to achieve inimitable advantage and superior performance. However, combining resources found among diverse members of a supply chain requires higher levels of coordination than exist at most companies. Manifest cross‐functional and interorganizational conflict impedes the relational advantages of collaboration. This research employs a multimethod—survey and interview—approach to evaluate collaboration's influence on operational and firm performance. Our findings show that collaboration, as a dynamic capability, mediates the conflict resulting from functional orientations, and improves performance. Specific structural enablers to enhance an organization's collaborative capability are identified and described, providing insight into how firms can exploit interfirm resources for competitive advantage.     

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

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

受资源限制且带有缺货惩罚的季节性产品供应链协调

季节性产品供应链中,制造商受到资源限制时边际成本随产量递增,市场缺货时销售商面临缺货惩罚.以单个制造商和单个销售商组成的供应链为研究对象,首先分析了非合作状态下制造商和销售商最优决策行为,制造商根据销售商的订购量确定批发价格,并给出了集中控制下整个供应链系统的最优产量.接着建立了基于退货策略的协调模型,对合作状态下参与双方的决策行为进行了分析.结果表明,在退货策略激励下,销售商接受使供应链整体利润最优的产量决策,但在一定取值范围内制造商提供的批发价格和折价系数之间需满足正向变化的约束.此时,合作双方达到了"双赢",从而有效地协调了供应链.最后,进行了数值分析,验证了结果的有效性.     

Stages of Supply Chain Disruption Response: Direct,Constraining, and Mediating Factors for Impact Mitigation

It is well established that supply chain disruptions can have a severe negative impact on firms and general wisdom suggests that this impact can be mitigated by quick responses. Aside from a few anecdotes, however, little is known about the decision‐making process that leads to speedy responses and about its impeding and supporting antecedents. Using the organizational information‐processing perspective, this empirical study unravels the disruption management process along a sequence of four stages—disruption recognition, disruption diagnosis, response development, and response implementation—and hypothesizes constraining and mediating effects of these stages. The findings contribute to an improved understanding of the role that the decision stages play in mitigating supply chain disruptions, and confirm the prediction that the speed with which information is processed and the stages are worked through positively affects supply chain performance. In addition, the findings suggest that one of the stages, diagnosis, acts as a constraining factor to the other stages. The stages also play a mediating role between the impact that the disruption has and a firm's readiness (prior to a disruption), dependence on a key supplier, and supply chain complexity. This provides guidance to decision makers in the application of resources both prior to a negative event and during a disruption recovery.     

Managing Production and Distribution for Supply Chains in the Processed Food Industry

We develop and evaluate a modeling approach for making periodic review production and distribution decisions for a supply chain in the processed food industry. The supply chain faces several factors, including multiple products, multiple warehouses, production constraints, high transportation costs, and limited storage at the production facility. This problem is motivated by the supply chain structure at Amy's Kitchen, one of the leading producers of natural and organic foods in the United States. We develop an enhanced myopic two‐stage approach for this problem. The first stage determines the production plan and uses a heuristic, and the second stage determines the warehouse allocation plan and uses a non‐linear optimization model. This two‐stage approach is repeated every period and incorporates look‐ahead features to improve its performance in future periods. We validate our model using actual data from one factory at Amy's Kitchen and compare the performance of our model to that of the actual operation. We find that our model significantly reduces both inventory levels and stockouts relative to those of the actual operation. In addition, we identify a lower bound on the total costs for all feasible solutions to the problem and measure the effectiveness of our model against this lower bound. We perform sensitivity analysis on some key parameters and assumptions of our modeling approach.     

On the Advantage of Quantity Leadership When Outsourcing Production to a Competitive Contract Manufacturer

This study investigates a supply chain comprising an original equipment manufacturer (OEM) and a contract manufacturer (CM), in which the CM acts as both upstream partner and downstream competitor to the OEM. The two parties can engage in one of three Cournot competition games: a simultaneous game, a sequential game with the OEM as the Stackelberg leader, and a sequential game with the CM as the Stackelberg leader. On the basis of these three basic games, this study investigates the two parties' Stackelberg leadership/followership decisions. When the outsourcing quantity and wholesale price are exogenously given, either party may prefer Stackelberg leadership or followership. For example, when the wholesale price or the proportion of production outsourced to the CM is lower than a threshold value, both parties prefer Stackelberg leadership and, consequently, play a simultaneous game in the consumer market. When the outsourcing quantity and wholesale price are decision variables, the competitive CM sets a wholesale price sufficiently low to allow both parties to coexist in the market, and the OEM outsources its entire production to this CM. This study also examines the impact of the supply chain parties' bargaining power on contract outcomes by considering a wholesale price that is determined via the generalized Nash bargaining scheme, finding a Stackelberg equilibrium to be sustained when the CM's degree of bargaining power is great and the non‐competitive CM's wholesale price is high.