Effect of coordinated replenishment policies on quality

Coordinated replenishment is a supply chain policy that affects many operational performance measures, including cost, lead time, and quality. In this paper, we develop a mathematical model of a simplified supply chain in which conformance quality is one of the supplier's decision variables and both the supplier and its customer are trying to minimize expected annual cost. Our expected cost model includes the important quality costs (appraisal, prevention, internal failure, and external failure) as well as holding, set-up, and ordering costs. Our results indicate that coordination leads to a decline in total cost but that coordination does not necessarily lead to an improvement in quality. In other words, buyers who are using coordinated replenishment may be trading higher quality for lower cost.     

多因素干扰下的供应链最优定价与订货决策

在信息不对称条件下,市场需求、价格和风险厌恶等因素同时干扰时,构建数量折扣契约的供应链博弈模型,寻找其实现供应链协调的内在约束条件。根据显示原理,构建信息不对称条件下多因素干扰的CVaR模型并求解,然后用算例仿真,结果表明:供应商隐瞒生产成本信息会带来额外收益,但给零售商造成额外损失。价格随机与零售商厌恶风险的耦合作用是导致供应链上各决策变量发生分岔现象的内因,与信息是否对称无关。但信息不对称会损害供应链系统的稳定性。在分岔突变区域内供应链失调,在区域之外供应链协调。零售商风险中性的态度和提高信息透明度有利于提高整个供应链的绩效。     

网络传媒业供应链质量控制策略与组织模式选择

网络传媒供应链由内容制作商提供有价值的信息产品(如娱乐、资讯等)并通过网站提供给消费者.产品质量控制对网络传媒供应链至关重要.首先利用博弈论的方法分析内容制作商和网站分别独立决策时的策略,纳什均衡揭示双方的"搭便车"动机使供应链整体利润无法达到最优.然后利用最优化的方法分析他们在垂直整合模式下的策略,发现协调决策可以提高整体利润.最后考虑垂直整合模式的新增成本,分析需求质量弹性、利润分配均匀性和质量控制成本等因素对供应链是否采取垂直整合模式的影响.帮助理解网络媒体产业的质量控制策略和供应链组织模式.     

Profit oriented supply chain network optimization

This paper proposes a novel mixed integer linear programming model to solve a supply chain network design problem. The proposed model deals with major issues for supply chains; product quality and cost. These issues are usually solved separately, but in this paper, we investigate effects of product quality on supply chain design and transportation flow. A trade-off between raw material quality, its purchasing and reprocessing costs was considered. Assuming decision maker (DM) wishes to work with a supplier which serves a low quality raw material; this raw material should be in need of reprocessing. To avoid the reprocessing costs, a supplier which serves a high quality raw material should be chosen but at this time the DM has to face a high purchasing cost. A supply chain network which consists of multiple suppliers, manufacturers, distribution centers and retailers is tried to be designed to accomplish aforementioned above trade-offs. The paper examines and discusses the relationship between product quality and supply chain design and offers several managerial insights.     

Inequity aversion in a joint economic lot sizing environment with asymmetric holding cost information

Screening contracts (or ‘menu of contracts’) are frequently used for aligning the incentives in supply chains with private information. In this context, it is assumed that all supply chain parties are strictly (expected) profit-maximizing. However, previous empirical work shows that this is a critical assumption. In fact, it seems that inequity adverse subjects are willing to invest money for achieving higher relative payoffs. Interestingly, the classical approach to design incentive compatible mechanisms gives the agent cheap leeway to increase relative pecuniary payoffs and thereby achieving more equitable profit allocations, because the agent is left (almost) indifferent between two contract alternatives. In other words, we argue (and actually observe in laboratory experiments) that this classical approach of contract design allows the agent to achieve more equitable outcomes at low cost. Since the agent’s better relative performance solely stems from reducing the principal’s payoffs, we observe a substantial negative impact on the overall supply chain performance. The present work relaxes the assumption of the profit-maximizing buyer (agent) in a serial supply chain for a lot sizing framework with asymmetrically distributed holding cost information and deterministic end-customer demand. The study provides researchers and managers an approach on how to account for disadvantageous inequity aversion (ie, the agent suffers from profits being lower than the principals profits) by designing a contract that anticipates such behaviour while providing a solution method for the resulting non-linear mathematical program. We denote the resulting contract as ‘behavioural robust’, since it limits the inefficiency losses that result if agents exhibit disadvantageous inequity aversion instead of being strictly profit-maximizing. A numerical study compares the advantages of the ‘behavioural robust’ contract against the classical screening contract. The results highlight that supply chain performance losses can be substantially reduced under the behavioural robust contract.     

A multi-phase mathematical programming approach for effective supply chain design

A supply chain is an alliance of independent business processes, such as supplier, manufacturing, and distribution processes that perform the critical functions in the order fulfillment process. Effective design and management of supply chains assists in production and delivery of a variety of products at low cost, high quality, and short lead times. Although the importance of supply chain design is emphasized in the literature, few formal decision models have been proposed for this purpose. This paper presents a multi-phase mathematical programming approach for effective supply chain design. More specifically, the methodology develops and applies a combination of multi-criteria efficiency models, based on game theory concepts, and linear and integer programming methods. Model application and insights are detailed through numerical illustrations.     

考虑决策者失望规避的供应链协同绿色创新动态优化与协调研究

针对决策者存在失望规避行为且产品绿色水平具有动态变化特征的情形,基于失望理论,运用微分博弈的方法研究了供应链协同绿色创新动态优化与协调问题,探讨了供应链成员失望规避程度对绿色创新决策及供应链绩效的影响,并提出双向成本分担契约对供应链进行协调。结果表明,供应链成员是否选择协同绿色创新及产品的绿色水平均与成员的失望规避程度密切相关;分散式决策下,若制造商选择分担供应商部分绿色创新成本,其分担比例与制造商失望规避程度负相关,与供应商失望规避程度正相关;集中式决策下,产品绿色水平、成员绿色创新水平及效用现值均高于协调前分散系统中的对应值;在一定条件下,双向成本分担契约的设计与实施,能有效提升供应链成员效用现值,实现供应链协调。     

A multi-objective approach to supply chain visibility and risk

This paper investigates the twin effects of supply chain visibility (SCV) and supply chain risk (SCR) on supply chain performance. Operationally, SCV has been linked to the capability of sharing timely and accurate information on exogenous demand, quantity and location of inventory, transport related cost, and other logistics activities throughout an entire supply chain. Similarly, SCR can be viewed as the likelihood that an adverse event has occurred during a certain epoch within a supply chain and the associated consequences of that event which affects supply chain performance. Given the multi-faceted attributes of the decision making process which involves many stages, objectives, and stakeholders, it beckons research into this aspect of the supply chain to utilize a fuzzy multi-objective decision making approach to model SCV and SCR from an operational perspective. Hence, our model incorporates the objectives of SCV maximization, SCR minimization, and cost minimization under the constraints of budget, customer demand, production capacity, and supply availability. A numerical example is used to demonstrate the applicability of the model. Our results suggest that decision makers tend to mitigate SCR first then enhance SCV.     

Managerial and economic impacts of reducing delivery variance in the supply chain

This paper addresses the managerial and economic impacts of improving delivery performance in a serial supply chain when delivery performance is evaluated with respect to a delivery window. Building on contemporary management theories that advocate variance reduction as the critical step in improving the overall performance of a system, we model the variance of delivery time to the final customer as a function of the investment to reduce delivery variance and the costs associated with untimely delivery (expected earliness and lateness). A logarithmic investment function is used and the model solution involves the minimization of a convex–concave total cost function. A numerical example is provided to illustrate the model and the solution procedure. The model presented provides guidelines for determining the optimal level of financial investment for reducing delivery variance. The managerial implications as well as the economic aspects of delivery variance reduction in supply chain management are discussed.     

软件服务供应链合同设计与协调策略

考虑了整合式软件服务供应链和分散式软件服务供应链两种模式,以整合供应链为基准对分散供应链下的软件开发合同设计与协调问题进行了研究。在分散供应链下,分析了固定价格合同、销量激励合同和成本分担合同三种合同对供应链收益的影响,探索了这些合同的激励机制,考察了这些合同的适用环境。研究表明:三种合同中,成本分担合同对软件开发商的激励最高,达到了整合供应链下的协调水平;固定价格合同适用于软件用户对质量不太注重而对价格比较敏感、以及开发成本较高的情形,销量激励合同适用于软件用户对质量比较注重而对价格不太敏感、以及开发成本较低的情形,成本分担合同适用于平台运营商易于监测软件开发商开发成本的情形。     

A two-stage queueing network on form postponement supply chain with correlated demands

To ease the conflict between quick response and product variety, more and more business models are developed in supply chains. Among these, the form postponement (FP) strategy is an efficient tool and has been widely adopted. To the supply chain with FP strategy, the design mostly involves two problems: determination of customer order decoupling point (CODP) position and semi-finished product inventory control. In this paper, we develop a two-stage tandem queueing network with MAP arrival to address this issue. Particularly, we introduce a Markov arrival process (MAP) to characterize the correlation of the demand. By using of matrix geometric method, we derive several performance measure of the supply chain, such as inventory level and unfill rate. Our numerical examples show that both the variance and the correlation coefficient of the demand lead to more delayed CODP position and more total cost.     

Approximate analysis and optimization of batch ordering policies in capacitated supply chains

Devising manufacturing/distribution strategies for supply chains and determining their parameter values have been challenging problems. Linking production management to stock keeping processes improves the planning of the supply chain activities, including material management, culminating in improved customer service levels. In this study, we investigate a multi-echelon supply chain consisting of a supplier, a plant, a distribution center and a retailer. Material flow between stages is driven by reorder point/order quantity inventory control policies. We develop a model to analyze supply chain behavior using some key performance metrics such as the time averages of inventory and backorder levels, as well as customer service levels at each echelon. The model is validated against simulation, yielding good agreement of robust performance metrics. The metrics are then used within an optimization framework to design the supply chain so as to minimize expected total system costs. The outcome of the optimization framework specifies how to move inventory throughout the supply chain and how to set inventory control parameters, i.e., reorder levels and replenishment batch sizes.     

Mean–variance analysis of a single supplier and retailer supply chain under a returns policy

In the literature, most of the supply chain coordinating policies target at improving the supply chain’s efficiency in terms of expected cost reduction or expected profit improvement. However, optimizing the expected performance alone cannot guarantee that the realized performance measure will fall within a small neighborhood of its expected value when the corresponding variance is high. Moreover, it ignores the risk aversion of supply chain members which may affect the achievability of channel coordination. As a result, we carry out in this paper a mean–variance (MV) analysis of supply chains under a returns policy. We first propose an MV formulation for a single supplier single retailer supply chain with a newsvendor type of product. The objective of each supply chain decision maker is to maximize the expected profit such that the standard deviation of profit is under the decision maker’s control. We study both the cases with centralized and decentralized supply chains. We illustrate how a returns policy can be applied for managing the supply chains to address the issues such as channel coordination and risk control. Extensive numerical studies are conducted and managerial findings are proposed.     

An integrated guaranteed- and stochastic-service approach to inventory optimization in supply chains

Multi-echelon inventory optimization literature distinguishes stochastic- (SS) and guaranteed-service (GS) approaches as mutually exclusive frameworks. While the GS approach considers flexibility measures at the stages to deal with stockouts, the SS approach only relies on safety stock. Within a supply chain, flexibility levels might differ between stages rendering them appropriate candidates for one approach or the other. The existing approaches, however, require the selection of a single framework for the entire supply chain instead of a stage-wise choice. We develop an integrated hybrid-service (HS) approach which endogenously determines the overall cost-optimal approach for each stage and computes the required inventory levels. We present a dynamic programming optimization algorithm for serial supply chains that partitions the entire system into subchains of different types. From a numerical study we find that, besides implicitly choosing the better of the two pure frameworks, whose cost differences can be considerable, the HS approach enables additional pipeline and on-hand stock cost savings. We further identify drivers for the preferability of the HS approach.     

A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level

Logistic network design has an important and strategic platform in an efficient and effective supply chain management, and usually involves multiple and conflicting goals, such as cost/profit, resource balance, customer responsiveness, quality, and the like. Besides, due to the implementation of government legislation, environmental concern, social responsibility and customer awareness, companies have been forced by customers not only to supply environmentally amicable products but also to be responsible for the returned products. Hence, this paper presents a stochastic multi-objective model for forward/reverse logistic network design under a uncertain environment including three echelons in forward direction (i.e., suppliers, plants, and distribution centers) and two echelons in backward direction (i.e., collection centers and disposal centers). We demonstrate a method to evaluate the systematic supply chain configuration maximizing the profit, customer responsiveness, and quality as objectives of the logistic network. The set of Pareto optimal solutions is obtained and also financial risk relevant to them is computed in order to show the tradeoff between objectives. The results give important insight for fostering the decision making process.     

Designing optimal global supply chains at Dow AgroSciences

The design of the underlying supply chain network can have a tremendous impact on the profitability, manageability, and level of risk of a global supply chain. Taxes, duties, and tariffs vary from country to country as well as trading bloc to trading bloc and can consume as much as 10% of the revenues of certain products. In the highly regulated business environment of agricultural chemicals, the country of origin of an active ingredient can determine where the final product can be marketed and the amount of taxes and duties applied to the product, making it necessary to trace all batches of product through many layers of the supply chain to their sources. This article presents a mixed integer linear programming model in use at Dow AgroSciences LLC that simultaneously optimizes the network design underlying global supply chains and the monthly production and shipping schedules for maximum profitability. This work contributes to the supply chain design literature by demonstrating a novel method of tracing products to their source for inventory valuation, taxation, and duty computation in a production environment where the products change into other products as they pass through nodes in the network. It also demonstrates an iterative scheme for determining unit fixed costs for fixed cost allocation for the same purposes. Finally, it provides a case study of a supply chain design initiative in a global enterprise.     

Design outsourcing in the fashion supply chain: OEM <Emphasis Type="Italic">versus</Emphasis> ODM

Design innovation is the engine of fashion. Many fashion firms outsource design innovation to their suppliers. Design outsourcing is on the rise in the fashion supply chain, but research in this area lags behind industry practice. In this paper, we examine how design outsourcing affects the supply chain, and we compare supply chain performance under an Original Equipment Manufacturer (OEM) strategy versus an Original Design Manufacturer (ODM) strategy. We evaluate a market size outsourcing model where design enhancement influences market size, and a success probability outsourcing model where design enhancement influences the success probability of innovation. We find that when the supplier trades with the retailer via a wholesale price contract under the ODM strategy, the supplier has no incentive to invest in innovation. In the market size outsourcing model, the design innovation in the centralized supply chain is higher than that under the OEM strategy. However, in the success probability outsourcing model, the success probability of innovation under the OEM strategy is higher than that in the centralized supply chain. Furthermore, we find that a profit sharing contract can achieve supply chain coordination under both OEM and ODM strategies; whereas, revenue sharing and buyback contracts cannot.     

Evaluation of cycle-count policies for supply chains with inventory inaccuracy and implications on RFID investments

Inventory record inaccuracy leads to ineffective replenishment decisions and deteriorates supply chain performance. Conducting cycle counts (i.e., periodic inventory auditing) is a common approach to correcting inventory records. It is not clear, however, how inaccuracy at different locations affects supply chain performance and how an effective cycle-count program for a multi-stage supply chain should be designed. This paper aims to answer these questions by considering a serial supply chain that has inventory record inaccuracy and operates under local base-stock policies. A random error, representing a stock loss, such as shrinkage or spoilage, reduces the physical inventory at each location in each period. The errors are cumulative and are not observed until a location performs a cycle count. We provide a simple recursion to evaluate the system cost and propose a heuristic to obtain effective base-stock levels. For a two-stage system with identical error distributions and counting costs, we prove that it is more effective to conduct more frequent cycle counts at the downstream stage. In a numerical study for more general systems, we find that location (proximity to the customer), error rates, and counting costs are primary factors that determine which stages should get a higher priority when allocating cycle counts. However, it is in general not effective to allocate all cycle counts to the priority stages only. One should balance cycle counts between priority stages and non-priority stages by considering secondary factors such as lead times, holding costs, and the supply chain length. In particular, more cycle counts should be allocated to a stage when the ratio of its lead time to the total system lead time is small and the ratio of its holding cost to the total system holding cost is large. In addition, more cycle counts should be allocated to downstream stages when the number of stages in the supply chain is large. The analysis and insights generated from our study can be used to design guidelines or scorecard systems that help managers design better cycle-count policies. Finally, we discuss implications of our study on RFID investments in a supply chain.     

A formal framework to analyze cost and performance in Map-Reduce based applications

Satisfying the global throughput targets of scientific applications is an important challenge in high performance computing (HPC) systems. The main difficulty lies in the high number of parameters having an important impact on the overall system performance. These include the number of storage servers, features of communication links, and the number of CPU cores per node, among many others.In this paper we present a model that computes a performance/cost ratio using different hardware configurations and focusing on scientific computing. The main goal of this approach is to balance the trade-off between cost and performance using different combinations of components for building the entire system. The main advantage of our approach is that we simulate different configurations in a complex simulation platform. Therefore, it is not necessary to make an investment until the system computes the different alternatives and the best solutions are suggested. In order to achieve this goal, both the system's architecture and Map-Reduce applications are modeled. The proposed model has been evaluated by building complex systems in a simulated environment using the SIMCAN simulation platform.     

供应链纵向结构对EPR政策激励效果的影响研究

为了揭示供应链纵向结构对EPR政策激励效果的影响,本文通过构建EPR政策约束下由上游制造商和下游客户所组成的供应链决策模型,分别在纵向一体化和纵向分离两种典型的供应链纵向市场结构下,考察了EPR政策对制造商环保性能和可循环性设计决策的激励影响。结果表明:在不同的供应链纵向结构下,由于制造商与客户之间的利益协调机制存在着差异,使得EPR政策对制造商环境设计选择的激励效果将发生变化。在纵向一体化结构下EPR政策对制造商可循环性的激励效果更强;而EPR政策对环保性能的激励在哪种供应链纵向结构下更强是不确定的,最终还取决于行业本身在生产成本和环境成本等方面的特征。最后,基于本文的研究结论给出了EPR政策与产业政策相配套的政策建议。