供应商协同能力评价指标体系研究

供应链协同已经成为供应链集团在与其他集团之间日趋激烈的竞争中创造竞争优势的势在必行的现代管理战略,供应商选择对供应链协同至关重要。通过控制进化种群划分与进化过程,利用压缩变异与Gauss变异设计一种组合变异方式,进而提出改进的模糊C-均值聚类遗传算法（IFCMGA）;在初步确定面向供应链协同的供应商评价指标后,利用IFCMGA算法对供应商协同能力评价指标进行分类,构建了面向供应链协同的供应商评价指标体系。结合模糊层次分析法与重要指标筛选法进行指标分析和筛选以及指标体系重构,以为供应商评价与选择提供科学决策依据。     

B2B协同决策半自动一致性模型设计与实现

具有群一致特性的多属性协同决策（MACD）技术对于支持供应链中复杂多变、资源动态的大型群体供应商与客户B2B协同是十分重要的,现有的群一致性模型存在明显不足：难于管理较大群体,需要大量复杂手工操作以确保协同决策过程有效。提出了面向供应商与客户B2B协同的多属性协同决策半自动一致性模型（SACM）。SACM包括4阶段：（1）供应商与客户B2B协同资源数据初始化;（2）形成协同决策问题;（3）一致性达到过程及供应商与客户B2B协同计划生成;（4）协同计划执行实时监控分析及计划重构。研究结果描述了供应链中,供应商与客户B2B协同由单向、局部的优化扩展进化为具有多层一致性与抗风险性的闭环、反馈的计划与执行协同优化的有效方法,提出的方法深化了对供应商与客户B2B协同管理的理解。     

采用电子合同

书生公司电子合同技术中标东风汽车有限公司东风商用车公司(以下简称东风有商)"供应商协同信息交互平台(简称SCIP系统)",全面提升东风商用车供应链整体管理水平.供应商协同信息交互平台是东风有商采购部门与供应商之间的协同工作平台,承担着采购政策介绍、采购询价/报价、新品竞标、合同签订、工程更改通知单签发等多项功能.……     

电力物资管理系统的设计与实现

为了推动物资计划工作前延,深入前期项目管理,精准物资需求,协同项目管理和物资管理,建立预警和提醒机制,支撑公司物资全供应链高效运转,实现物资里程碑体系管理,为此设计并实现了电力物资管理系统。该系统分为年度物资计划、物资需求计划、物资供应计划三大主要模块。     

连锁零售供应链多级库存协同决策研究

针对连锁零售供应链多级库存资源的动态优化配置问题,提出了在上层对库存策略和下层对物流分配方案协同寻优的多级库存双层规划模型。借鉴细粒度模型遗传算法的遗传操作具有局部性的特点,模拟微观群体交互作用的局部性,基于细粒度模型遗传算法的Agent群体行为优化算法和基于复杂适应系统涌现机理的协同决策机制,进行连锁零售供应链多级库存协同决策研究。通过算例实验对模型的有效性进行了验证。仿真实验结果表明,通过连锁零售供应链微观个体Agent的群体行为优化,从系统工程的角度,实现了连锁零售供应链多级库存的动态资源优化配置和信息共享,降低了多级库存管理与运营的总成本。     

采用电子合同

东风汽车采用电子合同全面提升产业链协同工作水平构造安全而开放的无纸化供应商系统信息交互平台书生公司电子合同技术中标东风汽车有限公司东风商用车公司(以下简称东风有商)供应商协同信息交互平台(简称SCIP系统),全面提升东风商用车供应链整体管理水平。供应商协同信息交互平台是东风有商采购部门与     

基于安全多方计算的产能约束供应商协同供货研究

针对供应链中核心制造商对产能约束的多个零部件供应商的“末位惩罚”问题, 构建基于安全多方计算的多个供应商协同模型, 并分析了分散决策和供应商协同决策下各个成员的最优决策. 通过Monte Carlo 模拟仿真方法和数例分析表明, 采用基于安全多方计算的供应链信息共享机制, 可在保护供应商的私有产能信息不公开的同时还能进行集中决策, 从而明显降低供应商的损失成本和整个供应链的全局损失成本, 最终让供应链达到帕累托改善.

     

基于食品药品供应链质量协同的社会共治实现机制

食品药品安全社会共治本质上是一项制度安排的体制变革。在社会共治中,如何解决好多主体参与的个体理性与集体理性之间的矛盾是一个难题。本文根据文献研究元分析方法,从供应链质量协同视角考察食品药品安全社会共治的实现机制,由此探讨食品药品供应链质量协同中的协同契约设计与全供应链信息披露机制,认为不同权力结构下多主体协同管理需求及其激励机制设计,是构建食品药品供应链质量协同契约设计的前提。同时,建设基于云计算环境的食品药品安全社会共治信息共享机制,是构建食品药品供应链质量协同的运作环境,这是推动食品药品供应链质量协同社会共治制度实现机制的两个关键举措。     

基于Agent的协同商务过程管理系统研究

在供应链管理中,商务过程往往跨越多个商务实体,各实体对相应过程段的管理具有一定的自主性和私有性,并又协同工作.传统集中式的商务过程管理模式已不能适应供应链管理的需求.针对敏捷供应链中分布式、P2P(Peer-to-Peer)式的协同商务过程管理模式,设计了基于Agent的网络协同商务过程管理系统,该系统支持分布式商务过程管理和控制,并进行了原型开发.     

供应链模式下供应商早进入新产品开发框架研究

目前的研究表明供应商早进入新产品开发将使整个供应链受益。然而目前对企业的调查表明供应商早进入新产品开发并没被广泛的实践;它的实现对企业来说是一个挑战。这篇论文对开发基于供应商早进入新产品开发的供应商关系管理进行研究,提出并阐述了供应链模式下的的框架。该框架提出了面向产品的供应链模型,邀请供应商参加竞标的机制以及基于优先方法的供应商选择机制。     

基于FCFS和SOFS的供方主导型供应链订单管理模型与算法研究

本文提出了供应链订单管理问题的供方主导型重要概念,分析了供方主导型供应链订单管理基本特性,研究了先来优先式和小单优先式供方主导型供应链订单管理策略、模型,给出了多个基于先来优先式和小单优先式单供方、多供方主导型供应链订单管理算法,提出了订单管理的等差划分、变差划分新方法.     

Evaluating suppliers via a multiple levels multiple criteria decision making method under fuzzy environment

Suppliers are an important component of the supply chain. Their ability and performance are what largely determine the success or failure of the supply chain. Thus the evaluation of suppliers has become a very important part of the supply chain management of a company. This paper suggests a multiple levels multiple criteria decision making (MCDM) model under fuzzy environment to evaluate and select suppliers, where a general hierarchical structure is developed to depict the relationship among parent criteria and their sub-criteria and sub-sub-criteria and so on. These criteria are classified into quantitative and qualitative ones. The ratings of alternatives versus qualitative criteria and the importance weights of all criteria in the hierarchical structure are assessed in linguistic values represented by triangular fuzzy numbers. The ranking approach of center of area is suggested to rank all the fuzzy numbers before their weighted ratings aggregation. The final evaluation values of alternatives can then be obtained through the additive weighted ratings from the last to the first level in the criteria structure. Finally a numerical example demonstrates the feasibility of the proposed model.     

基于区块链的汽车供应链产品追溯系统

区块链作为新兴的分布式数据库技术,具有公开透明、不可篡改和易于追溯等特征,与供应链产品溯源具有良好的契合度。针对汽车供应链面临的缺乏信任、溯源难度大及信息共享效率低等问题,运用区块链技术,设计了基于区块链的汽车供应链产品追溯系统。选用以太坊作为系统的开发平台,设计了授权管理、信息录入、溯源转移和链上查询等功能模块,根据功能需求设计了智能合约,增加了对敏感数据的处理环节。系统中配置了原材料供应商、零部件供应商、整车厂以及分销商4个参与主体,提供了监管部门与消费者的溯源接口。通过实例对溯源系统进行展示。研究表明：区块链溯源系统与传统的溯源方式相比,产品数据的安全性和溯源效率等方面具有明显优势。     

JIT供应链物料采购协同优化及其粒子群算法

在供应链环境中,传统的物料需求计划没有考虑供应商的供货能力与经济利益,不利于供应链上下游企业的长期合作。研究了一种基于JIT的供应链物料采购协同优化问题,以单一制造商和多供应商构成的二级供应链为研究对象,遵循产品生产的BOM约束和MRP制定原理,以最小化供应链上下游企业的库存、运输、缺货、赶工等总成本为目标,构建了以MRP为引导的供应链订购批量协同优化模型;通过调整制造商的主生产计划变更采购计划,以获得供应商供货方案,据此设计了基于整数编码和带有交叉操作的改进离散粒子群优化算法进行模型求解;结合实例对模型可行性进行了验证,通过算法结果分析及比较,证明了算法的有效性。     

A strategic model for supply chain design with logical constraints: formulation and solution

This paper proposes a strategic production–distribution model for supply chain design with consideration of bills of materials (BOM). Logical constraints are used to represent BOM and the associated relationships among the main entities of a supply chain such as suppliers, producers, and distribution centers. We show how these relationships are formulated as logical constraints in a mixed integer programming (MIP) model, thus capturing the role of BOM in the selection of suppliers in the strategic design of a supply chain. A test problem is presented to illustrate the effectiveness of the formulation and solution strategy. The results and their managerial implications are discussed.Scope and purposeSupply chain design is to provide an optimal platform for efficient and effective supply chain management. The problem is often an important and strategic operations management problem in supply chain management. This paper shows how the mixed integer programming modeling techniques can be applied to supply chain design problem, where some complicated relations, such as bills of materials, are involved. We discuss how to solve such a complicated model efficiently.     

面向集群式供应链的服务系统设计方法研究

为了帮助中小型企业(SMEs)应对经济全球化的挑战,"集群式供应链"的概念作为一种新型管理模式被提了出来,它集成了工业集群和供应链两个方面的优势。目前,对集群供应链的研究仍然处于初级阶段,其理论结果难以应用到实践当中。基于此,本文提出采用Agent建模技术作为桥梁,用以弥补集群式供应链理论和实践之间的鸿沟。最后,给出了一个服务系统的实现模式,用于指导集群式供应链的构建和开发。     

An intelligent supplier management tool for benchmarking suppliers in outsource manufacturing

A global corporation's supply chain usually consists of enterprises and manufacturers that are graphically dispersed around the world, whereby each company is involved in a wide variety of supply chain activities such as order fulfillment, international procurement, acquisition of information technology, manufacturing, and customer service. However, selecting suppliers based on accumulated experience is not both effective and scientific due to subjective judgment and lack of systematic analysis. Therefore, continuously tracking and benchmarking performance of suppliers and forming an appropriate supplier selection mechanism is one of the crucial activities in supply chain management. This paper presents an intelligent supplier management tool (ISMT) using the case-based reasoning (CBR) and neural network (NN) techniques to select and benchmark suppliers. The development of ISMT and how the CBR and NN techniques are used in benchmarking suppliers during the process of new product development in Honeywell Consumer Products (Hong Kong) Limited are presented.     

Structured methodology for supplier selection and evaluation in a supply chain

Supply chain management (SCM) is one of the most important competitive strategies used by modern enterprises. The main aim of supply chain management is to integrate various suppliers to satisfy market demand. Meanwhile, supplier selection and evaluation plays an important role in establishing an effective supply chain. Traditional supplier selection and evaluation methods focus on the requirements of single enterprises, and fail to consider the entire supply chain. Therefore, this study proposes a structured methodology for supplier selection and evaluation based on the supply chain integration architecture.In developing the methodology for supplier selection and evaluation in a supply chain, enterprise competitive strategy is first identified using strengths weaknesses opportunities threats (SWOT) analysis. Based on the competitive strategy, the criteria and indicators of supplier selection are chosen to establish the supplier selection framework. Subsequently, potential suppliers are screened through data envelopment analysis (DEA). Technique for order preference by similarity to ideal solution (TOPSIS), a multi-attribute decision-making (MADA) method is adapted to rank potential suppliers. Finally, the Taiwanese textile industry is used to illustrate the application and feasibility of the proposed methodology.This study facilitates the improvement of collaborator relationships and the management of potential suppliers to help increase product development capability and quality, reduce product lifecycle time and cost, and thus increase product marketability.     

Machine learning for dynamic multi-product supply chain formation

Recent trend in eCommerce applications toward effectively reducing supply chain costs—including spatial, temporal, and monetary resources—has spurred interest among researchers as well as practitioners to efficiently utilize supply chains. One of the least studied of these views is adaptive or dynamic configuration of supply chains. This problem is relatively new since faster communications over the Internet or by any other means and the willingness to utilize it for effective management of supply chains did not exist a few decades ago. The proposed framework addresses the problem of supply chain configuration. We incorporate machine-learning techniques to develop a dynamically configurable supply chain framework, and evaluate its effectiveness with respect to comparable static supply chains. Specifically, we consider the case where several parts go into the production of a product. A single supplier or a combination of suppliers could supply these parts. The proposed framework automatically forms the supply chain dynamically as per the dictates of incoming orders and the constraints from suppliers upstream.     

Machine learning for dynamic multi-product supply chain formation

Recent trend in eCommerce applications toward effectively reducing supply chain costs—including spatial, temporal, and monetary resources—has spurred interest among researchers as well as practitioners to efficiently utilize supply chains. One of the least studied of these views is adaptive or dynamic configuration of supply chains. This problem is relatively new since faster communications over the Internet or by any other means and the willingness to utilize it for effective management of supply chains did not exist a few decades ago. The proposed framework addresses the problem of supply chain configuration. We incorporate machine-learning techniques to develop a dynamically configurable supply chain framework, and evaluate its effectiveness with respect to comparable static supply chains. Specifically, we consider the case where several parts go into the production of a product. A single supplier or a combination of suppliers could supply these parts. The proposed framework automatically forms the supply chain dynamically as per the dictates of incoming orders and the constraints from suppliers upstream.