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.     

A Modeling Framework for Supply Chain Simulation: Opportunities for Improved Decision Making*

Owing to its inherent modeling flexibility, simulation is often regarded as the proper means for supporting decision making on supply chain design. The ultimate success of supply chain simulation, however, is determined by a combination of the analyst's skills, the chain members' involvement, and the modeling capabilities of the simulation tool. This combination should provide the basis for a realistic simulation model, which is both transparent and complete. The need for transparency is especially strong for supply chains as they involve (semi)autonomous parties each having their own objectives. Mutual trust and model effectiveness are strongly influenced by the degree of completeness of each party's insight into the key decision variables. Ideally, visual interactive simulation models present an important communicative means for realizing the required overview and insight. Unfortunately, most models strongly focus on physical transactions, leaving key decision variables implicit for some or all of the parties involved. This especially applies to control structures, that is, the managers or systems responsible for control, their activities and their mutual attuning of these activities. Control elements are, for example, dispersed over the model, are not visualized, or form part of the time‐indexed scheduling of events. In this article, we propose an alternative approach that explicitly addresses the modeling of control structures. First, we will conduct a literature survey with the aim of listing simulation model qualities essential for supporting successful decision making on supply chain design. Next, we use this insight to define an object‐oriented modeling framework that facilitates supply chain simulation in a more realistic manner. This framework is meant to contribute to improved decision making in terms of recognizing and understanding opportunities for improved supply chain design. Finally, the use of the framework is illustrated by a case example concerning a supply chain for chilled salads.     

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.     

An Integrated Framework for Environmental Multi‐Impact Spatial Risk Analysis

Quantitative risk analysis is being extensively employed to support policymakers and provides a strong conceptual framework for evaluating decision alternatives under uncertainty. Many problems involving environmental risks are, however, of a spatial nature, i.e., containing spatial impacts, spatial vulnerabilities, and spatial risk‐mitigation alternatives. Recent developments in multicriteria spatial analysis have enabled the assessment and aggregation of multiple impacts, supporting policymakers in spatial evaluation problems. However, recent attempts to conduct spatial multicriteria risk analysis have generally been weakly conceptualized, without adequate roots in quantitative risk analysis. Moreover, assessments of spatial risk often neglect the multidimensional nature of spatial impacts (e.g., social, economic, human) that are typically occurring in such decision problems. The aim of this article is therefore to suggest a conceptual quantitative framework for environmental multicriteria spatial risk analysis based on expected multi‐attribute utility theory. The framework proposes: (i) the formal assessment of multiple spatial impacts; (ii) the aggregation of these multiple spatial impacts; (iii) the assessment of spatial vulnerabilities and probabilities of occurrence of adverse events; (iv) the computation of spatial risks; (v) the assessment of spatial risk mitigation alternatives; and (vi) the design and comparison of spatial risk mitigation alternatives (e.g., reductions of vulnerabilities and/or impacts). We illustrate the use of the framework in practice with a case study based on a flood‐prone area in northern Italy.     

Role of flexibility in supply chain design and modeling—Introduction to the special issue

In this introductory note, we describe the motivation behind this special issue on supply chain design and modeling. We begin by noting the potential opportunities offered by flexibility of a system in enhancing its functionalities and capabilities. After defining the nature of flexibility, we briefly describe flexibility in a supply chain, pertinent issues, and potential tools and techniques utilized for designing and modeling flexibility in it. We close out with a brief review of the articles in this issue and their relevance to the interests of decision makers.     

Evaluation of the green supply chain management practices: a fuzzy ANP approach

Environmentally responsible manufacturing, green supply chain management (GSCM), and related principles have become important strategies for companies to achieve profit and gain market share by lowering their environmental impacts and increasing their efficiency. As environment has become a key strategic consideration in supply chains, this study examines the components and elements of GSCM and suggests a novel GSCM evaluation framework. It also provides a real-case study of Ford Otosan, one of the pioneering companies about environmental subjects in Turkey, to illustrate the industrial application of our theoretical assessment model. The identified components are integrated into a strategic assessment and evaluation tool using analytical network process (ANP). The dynamic characteristics and complexity of the GSCM analysis environment make the ANP technique a suitable tool for this study. Moreover, to cope with ambiguity and vagueness of the decision maker's evaluations, the fuzzy extension of the ANP method is preferred.     

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.     

A multi-criteria ranking algorithm (MCRA) for determining breast cancer therapy

Breast cancer is the leading cause of cancer deaths among women. The selection of an effective, patient-specific treatment plan for breast cancer has been a challenge for physicians because the decision process involves a vast number of treatment alternatives as well as treatment decision criteria, such as the stage of the cancer (e.g., in situ, invasive, metastasis), tumor characteristics, biomarker-related risks, and patient-related risks. Furthermore, every patient's case is unique, requiring a patient-specific treatment plan, while there is no standard procedure even for a particular stage of the breast cancer. In this paper, we first determine a comprehensive set of criteria for selecting the best breast cancer therapy by interviewing medical oncologists and reviewing the literature. We then present two analytical hierarchy process (AHP) models for quantifying the weights of criteria for breast cancer treatment in two sequential steps: primary and secondary treatment therapy. Using the weights of criteria from the AHP model, we propose a new multi-criteria ranking algorithm (MCRA), which evaluates a large variety of patient scenarios and provides the best patient-tailored breast cancer treatment alternatives based on the input of nine medical oncologists. We then validate the predictions of the multi-criteria ranking algorithm by comparing treatment ranks of the algorithm with ranks of five different oncologists, and show that algorithm rankings match or are statistically significantly correlated with the overall expert ranking in most cases. Our multi-criteria ranking algorithm could be used as an accessible decision-support tool to aid oncologists and educate patients for determining appropriate and effective treatment alternatives for breast cancer. Our approach is also general in the sense that it could be adapted to solve other complex decision-making problems in medicine, healthcare, as well as other service and manufacturing industries.     

BPR决策支持系统分布式求解模型的研究

基于多Ａｇｅｎｔ规划技术,提出并建立了支持企业过程重组（ＢＰＲ）的决策支持系统的分布式协同求解模型,包括企业过程的分层规划理论、多Ａｇｅｎｔ协同模式分析、状态空间模型以及任务分解算法等四部分内容．它的提出为企业过程重组的工具支持提供了理论依据和技术方法．实践证明,应用该技术可以有效地指导ＢＰＲＤＳＳ的开发     

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).      

Communicating supply chain risks and mitigation strategies: a comprehensive framework

Concerning the increasing emphasis on risk management in this uncertain global environment, there is an urgent demand for practical decision support tools that support supply chain risk communication and management. This research proposes an integrated framework that takes explicit account of multiple types of risk in aiding decision-making, and compares and ranks alternative risk mitigation strategies individually and collectively in indicator basis using fuzzy set theory and multiple criteria decision analysis methods. Through an illustrative case, the research demonstrates that the proposed framework provides a holistic view of supply chain risks and enables firms to foresee, spot and respond to the exposed risks in an effective and efficient manner.     

Stockout Recovery under Consignment: The Role of Inventory Ownership in Supply Chains

We examine how a supply chain firm may implement an incentive contract under inventory consignment to recover stockouts and to retain customers. Inventory consignment allows an upstream firm (e.g., a manufacturer) to own and control inventory at a downstream firm (e.g., a retailer), representing a structural change in supply chain governance. Motivated by cases in pet toy and electronics component industries, we formulate principal–agent models based on the newsvendor framework to capture the strategic interactions in a supply chain. We find that consignment can be effective in reducing stockouts because the ownership and control of the product by the manufacturer allows better prevention of stockouts, which complements stockout recovery and leads to fewer stockouts. However, the lower level of stockout under consignment does not necessarily translate into profitability—when the manufacturer is highly opportunistic, that is, readily exploring outside opportunities for its product, consignment may lead to lower profitability for both the manufacturer and the supply chain. On the other hand, consignment will improve profitability for the manufacturer and the supply chain, if both the manufacturer opportunism and the retailer opportunism are moderate.      

Models for supply chain reengineering

The goal of this paper is to provide a first step in the development of a coherent set of modelling methods for supply chain reengineering. This paper shows that different fields of attention in supply chain management should be approached with different modelling methods. Three modelling methods are presented: the event process chain (EPC) method; the activity chain model (ACM) method; and the GRAI grid method (adjusted for supply chains). EPC modelling focuses on time relationships between primary processes/functions, and is customer oriented. ACM modelling focuses on functions/processes and information flows. GRAI focuses on decision structures and information flows. The three methods are described, and an application of the methods to a case study company is presented. This paper concludes with a comparison of the methods and a discussion of their applicability.     

An interdependent layered network model for a resilient supply chain

This paper addresses the problem of designing supply chains that are resilient to natural or human-induced extreme events. It focuses on the development of efficient restoration strategies that aid the supply chain in recovering from a disruption, thereby limiting the impact on its customers. The proposed restoration model takes into account possible disruptions to infrastructures, e.g., transportation and communications, by explicitly formulating their logical relationships with supply chains. A problem solving process is proposed that provides for cooperation between the managers of the infrastructures and the managers of the supply chains disrupted by an extreme event. Both the model and the problem solving process are exercised with a realistic industry problem.     

大型复杂产品制造业集群供应链绩效评价模式研究

本文在已有文献的基础上,结合大型复杂产品制造业集群供应链的特点,提出了用协同性、可靠性、柔性、质量管理和成本控制等5个潜在变量和16个观测变量来评价供应链绩效;在此基础上,用结构方程建模的方法构建了四种评价大型复杂产品制造业集群供应链绩效的可能模式,即单因素一阶模式,潜变量间互不相关的一阶模式,潜变量间存在相关的一阶模式和二阶模式。由于探索性因素分析方法无法建立一致性的绩效评价构面及其体系,因此本文采用验证性因素分析方法从匹配度和稳定性方面来验证和筛选上述四种可能的供应链绩效评价模式。研究结果表明,二阶验证性因素分析模式与样本数据的拟合程度最佳,是有效且可靠的集群供应链绩效评价模式。     

三级装备制造业供应链质量管理行为研究

作为我国工业化建设和国民经济发展的根本保证,供应链环境下装备制造企业的产品质量问题受到了广泛关注。本文重点考虑产品质量对供应链收益的影响,运用微分对策理论研究由单一制造商、单一部件供应商和单一零件供应商构成的三级装备制造业供应链质量管理行为的协调问题。通过对比Nash非合作博弈和Stackelberg主从博弈两种分散式决策模式及集中式决策模式下协同合作博弈的均衡结果,得到相关结论,并利用算例进行验证。研究表明,①分散式决策下,制造商的质量管理行为相同,但相比于Nash非合作博弈,Stackelberg主从博弈能够改善部件供应商和零件供应商的质量管理行为,提升供应链成员的收益水平;②集中式决策下,供应链成员的质量管理行为达到最优,系统整体收益实现最大。研究结论为装备制造业供应链成员间质量管理战略联盟的构建提供了理论依据。     

The Impact of Modular Assembly on Supply Chain Efficiency

This article studies the impact of modular assembly on supply chain efficiency. In the modular assembly approach, a manufacturer acquires pre‐assembled modules from its suppliers, rather than the individual components, as in the traditional assembly approach. We analyze the competitive behavior of a two‐stage modular assembly system consisting of a manufacturer, and a supplier who pre‐assembles two components into a module. The firms can choose their own inventory policies and we show the existence of Nash equilibrium in the inventory game. Moving from the traditional to the modular approach has a twofold effect on the supply chain. First, we investigate the effect of centralizing the component suppliers. It can be shown that when there is no production time shift, the module supplier always holds more component inventories than suppliers do in the traditional approach, which yields a lower cost for the manufacturer. However, the suppliers, and therefore the supply chain may incur a higher cost in the modular approach. Second, we study the effect of a shift in production time from the manufacturing stage to the supplier stage. From numerical studies, it has been found that such a lead time shift always benefits a centralized supply chain, but not necessarily so for a decentralized system. Combining the two effects, we find that the modular approach generally reduces the cost to the manufacturer and the supply chain, which explains the prevalence of modular assembly from the perspective of inventory management. These results also provide some insight into how firms can improve supply chain efficiency by choosing the right decision structure and lead time configuration.     

Supply chain configuration for diffusion of new products: An integrated optimization approach

We develop an integrated/hybrid optimization model for configuring new products’ supply chains while explicitly considering the impact of demand dynamics during new products’ diffusion. The hybrid model simultaneously determines optimal production/sales plan and supply chain configuration. The production and sales plan provides decisions on the optimal timing to launch a new product, as well as the production and sales quantity in each planning period. The supply chain configuration provides optimal selection of options and safety stock level kept at each supply chain function. Extensive computational experiments on randomly generated testbed problems indicate that the hybrid modeling and solution approach significantly outperforms non-hybrid alternative modeling and solution approaches under various diffusion and supply chain topologies. We provide insights on optimal production/sales plan and supply chain configuration for new products during their diffusion process. Also, managerial implications relevant to effectiveness of the hybrid approach are discussed.     

供需不确定下基于MOI和VMI模式的供应链协同比较研究

为分析供应链运作机制对降低随机供应商产出和随机需求的影响,对比研究了两供应商-单制造商系统在MOI和VMI两种模式下的供应链协同模型。MOI模式下,制造商管理并持有库存,提出了供应风险共享的协同机制;VMI模式下,供应商管理并持有库存,提出了收益共享与额外惩罚的协同机制。分析了集中决策、MOI和VMI模式下的最优批量决策,证明了VMI模式下存在唯一的纳什均衡。研究还发现,VMI模式更容易协调供应链,有效降低供需不确定的影响。MOI模式下的供应链可实现帕累托改进,但不能实现协调;而当参数满足一定关系时,VMI模式下供应链的期望利润可达到集中决策。     

基于税盾效应的供应链贸易信用融资优化决策研究

针对中小企业融资困境的现实,本文基于贸易信用融资模式,引入税盾效应,建立了由单一核心供应商和单一经销商组成的含税盾的贸易信用模型,探讨有融资需求的经销商进行订货、核心供应商定价的决定策略,探究不同计息方式的价值实现和融资模式优选。通过stackbelberg博弈、比较分析和算例验证,研究发现以订货量确定为基准,融资需求出现的时点对供应链企业计息方式的选择均有影响。税法关于利息抵扣上限、贸易信用贷款利率、订货量等均影响经销商计息方式的选择,融资额度影响其订货策略;生产成本的差额、定价策略影响供应商的计息方式。