供应链下的多级存货管理【外文翻译】

本科毕业论文（设计）

外 文 翻 译

原文：

Multi-echelon inventory management in supply chains Historically, the echelons of the supply chain, warehouse, distributors, retailers, etc., have been managed independently, buffered by large inventories. Increasing competitive pressures and market globalization are forcing firms to develop supply chains that can quickly respond to customer needs. To remain competitive and decrease inventory, these firms must use multi-echelon inventory management interactively, while reducing operating costs and improving customer service. Supply chain management (SCM) is an integrative approach for planning and control of materials and information flows with suppliers and customers, as well as between different functions within a company. This area has drawn considerable attention in recent years and is seen as a tool that provides competitive power .SCM is a set of approaches to integrate suppliers, manufacturers, warehouses, and stores efficiently, so that merchandise is produced and distributed at right quantities, to the right locations and at the right time, in order to minimize system-wide costs while satisfying service-level requirements .So the supply chain consists of various members or stages. A supply chain is a dynamic, stochastic, and complex system that might involve hundreds of participants.

Inventory usually represents from 20 to 60 per cent of the total assets of manufacturing firms. Therefore, inventory management policies prove critical in determining the profit of such firms. Inventory management is, to a greater extent, relevant when a whole supply chain (SC), namely a network of procurement, transformation, and delivering firms, is considered. Inventory management is indeed a major issue in SCM, i.e. an approach that addresses SC issues under an integrated perspective.

Inventories exist throughout the SC in various forms for various reasons. The lack of a coordinated inventory management throughout the SC often causes the bullwhip effect, namely an amplification of demand variability moving towards the upstream stages. This causes excessive inventory investments, lost revenues, misguided capacity plans, ineffective transportation, missed production schedules, and poor customer service.

Many scholars have studied these problems, as well as emphasized the need of integration among SC stages, to make the chain effectively and efficiently satisfy customer requests (e.g. reference). Beside the integration issue, uncertainty has to be dealt with in order to define an effective SC inventory policy. In addition to the uncertainty on supply (e.g. lead times) and demand, information delays associated with the manufacturing and distribution processes characterize SCs.

Inventory management in multi-echelon SCs is an important issue, because there are many elements that have to coordinate with each other. They must also arrange their inventories to coordinate. There are many factors that complicate successful inventory management, e.g. uncertain demands, lead times, production times, product prices, costs, etc., especially the uncertainty in demand and lead times where the inventory cannot be managed between echelons optimally.

Most manufacturing enterprises are organized into networks of manufacturing and distribution sites that procure raw material, process them into finished goods, and

distribute the finish goods to customers. The terms ‘multi-echelon’ or ‘multilevel‘production/distribution networks are also synonymous with such networks (or SC), when an item moves through more than one step before reaching the final customer. Inventories exist throughout the SC in various forms for various reasons. At any manufacturing point, they may exist as raw materials, work in progress, or finished goods. They exist at the distribution warehouses, and they exist in-transit, or ‘in the pipeline’, on each path linking these facilities.

Manufacturers procure raw material from suppliers and process them into finished goods, sell the finished goods to distributors, and then to retail and/or customers. When an item moves through more than one stage before reaching the

final customer, it forms a ‘multi-echelon’ inventory system. The echelon stock of a stock point equals all stock at this stock point, plus in-transit to or on-hand at any of its downstream stock points, minus the backorders at its downstream stock points.

The analysis of multi-echelon inventory systems that pervades the business world has a long history. Multi-echelon inventory systems are widely employed to distribute products to customers over extensive geographical areas. Given the importance of these systems, many researchers have studied their operating characteristics under a variety of conditions and assumptions. Since the development of the economic order quantity (EOQ) formula by Harris (1913), researchers and practitioners have been actively concerned with the analysis and modeling of inventory systems under different operating parameters and modeling assumptions .Research on multi-echelon inventory models has gained importance over the last decade mainly because integrated control of SCs consisting of several processing and distribution stages has become feasible through modern information technology. Clark and Scarf were the first to study the two-echelon inventory model. They proved the optimality of a base-stock policy for the pure-serial inventory system and developed an efficient decomposing method to compute the optimal base-stock ordering policy. Bessler and Veinott extended the Clark and Scarf model to include general arbores cent structures. The depot-warehouse problem described above was addressed by Eppen and Schrage who analyzed a model with a stockless central depot. They derived a closed-form expression for the order-up-to-level under the equal fractile allocation assumption. Several authors have also considered this problem in various forms. Owing to the complexity and intractability of the multi-echelon problem Hadley and Whitin recommend the adoption of single-location, single-echelon models for the inventory systems.

Sherbrooke considered an ordering policy of a two-echelon model for warehouse and retailer. It is assumed that stock outs at the retailers are completely backlogged. Also, Sherbrooke constructed the METRIC (multi-echelon technique for coverable item control) model, which identifies the stock levels that minimize the expected number of backorders at the lower-echelon subject to a bud get constraint. This model

is the first multi-echelon inventory model for managing the inventory of service parts. Thereafter, a large set of models which generally seek to identify optimal lot sizes and safety stocks in a multi-echelon framework, were produced by many researchers. In addition to analytical models, simulation models have also been developed to capture the complex interaction of the multi-echelon inventory problems.

So far literature has devoted major attention to the forecasting of lumpy demand, and to the development of stock policies for multi-echelon SCs Inventory control policy for multi-echelon system with stochastic demand has been a widely researched area. More recent papers have been covered by Silver and Pyke. The advantage of centralized planning, available in periodic review policies, can be obtained in continuous review policies, by defining the reorder levels of different stages, in terms of echelon stock rather than installation stock.

Rau et al. , Diks and de Kok , Dong and Lee ,Mitra and Chatterjee , Hariga , Chen ,Axsater and Zhang , Nozick and Turnquist ,and So and Zheng use a mathematic modeling technique in their studies to manage multi-echelon inventory in SCs. Diks and de Kok’s study considers a divergent multi-echelon inventory system, such as a distribution system or a production system, and assumes that the order arrives after a fixed lead time. Hariga, presents a stochastic model for a single-period production system composed of several assembly/processing and storage facilities in series. Chen, Axsater and Zhang, and Nozick and Turnquist consider a two-stage inventory system in their papers. Axsater and Zhang and Nozickand Turnquist assume that the retailers face stationary and independent Poisson demand. Mitra and Chatterjee examine De Bodt and Graves’ model (1985), which they developed in their paper’ Continuous-review policies for a multi-echelon inventory problem with stochastic demand’, for fast-moving items from the implementation point of view. The proposed modification of the model can be extended to multi-stage serial and two -echelon assembly systems. In Rau et al.’s model, shortage is not allowed, lead time is assumed to be negligible, and demand rate and production rate is deterministic and constant. So and Zheng used an analytical model to analyze two important factors that can contribute to the high degree of order-quantity variability experienced by

semiconductor manufacturers: supplier’s lead time and forecast demand updating. They assume that the external demands faced by there tailor are correlated between two successive time periods and that the retailer uses the latest demand information to update its future demand forecasts. Furthermore, they assume that the supplier’s delivery lead times are variable and are affected by the retailer’s order quantities. Dong and Lee’s paper revisits the serial multi-echelon inventory system of Clark and Scarf and develops three key results. First, they provide a simple lower-bound approximation to the optimal echelon inventory levels and an upper bound to the total system cost for the basic model of Clark and Scarf. Second, they show that the structure of the optimal stocking policy of Clark and Scarf holds under time-correlated demand processing using a Martingale model of forecast evolution. Third, they extend the approximation to the time-correlated demand process and study, in particular for an autoregressive demand model, the impact of lead times, and autocorrelation on the performance of the serial inventory system.

After reviewing the literature about multi-echelon inventory management in SCs using mathematic modeling technique, it can be said that, in summary, these papers consider two, three, or N-echelon systems with stochastic or deterministic demand. They assume lead times to be fixed, zero, constant, deterministic, or negligible. They gain exact or approximate solutions.

Dekker et al. analyses the effect of the break-quantity rule on the inventory costs. The break-quantity rule is to deliver large orders from the warehouse, and small orders from the nearest retailer, where a so-called break quantity determines whether an order is small or large. In most l-warehouse–N-retailers distribution systems, it is assumed that all customer demand takes place at the retailers. However, it was shown by Dekker et al. that delivering large orders from the warehouse can lead to a considerable reduction in the retailer’s inventory costs. In Dekker et al. the results of Dekker et al. were extended by also including the inventory costs at the warehouse. The study by Mohebbi and Posner’s contains a cost analysis in the context of a continuous-review inventory system with replenishment orders and lost sales. The policy considered in the paper by Vander Heijden et al. is an echelon stock, periodic

review, order-up-to policy, under both stochastic demand and lead times.

The main purpose of Iida’s paper is to show that near-myopic policies are acceptable for a multi-echelon inventory problem. It is assumed that lead times at each echelon are constant. Chen and Song’s objective is to minimize the long-run average costs in the system. In the system by Chen et al., each location employs a periodic-review, or lot-size reorder point inventory policy. They show that each location’s inventory positions are stationary and the stationary distribution is uniform and independent of any other. In the study by Minner et al., the impact of manufacturing flexibility on inventory investments in a distribution network consisting of a central depot and a number of local stock points is investigated. Chiang and Monahan present a two-echelon dual-channel inventory model in which stocks are kept in both a manufacturer warehouse (upper echelon) and a retail store (lower echelon), and the product is available in two supply channels: a traditional retail store and an internet-enabled direct channel. Johansen’s system is assumed to be controlled by a base-stock policy. The independent and stochastically dependent lead times are compared.

To sum up, these papers consider two- or N-echelon inventory systems, with generally stochastic demand, except for one study that considers Markov-modulated demand. They generally assume constant lead time, but two of them accept it to be stochastic. They gain exact or approximate solutions.

In multi-echelon inventory management there are some other research techniques used in literature, such as heuristics, vary-METRIC method, fuzzy sets, model predictive control, scenario analysis, statistical analysis, and GAs. These methods are used rarely and only by a few authors.

A multi-product, multi-stage, and multi-period scheduling model is proposed by Chen and Lee to deal with multiple incommensurable goals for a multi-echelon SC network with uncertain market demands and product prices. The uncertain market demands are modeled as a number of discrete scenarios with known probabilities, and the fuzzy sets are used for describing the sellers’ and buyers’ incompatible preference on product prices.

In the current paper, a detailed literature review, conducted from an operational research point of view, is presented, addressing multi-echelon inventory management in supply chains from 1996 to 2005.Here, the behavior of the papers, against demand and lead time uncertainty, is emphasized.

The summary of literature review is given as: the most used research technique is simulation. Also, analytic, mathematic, and stochastic modeling techniques are commonly used in literature. Recently, heuristics as fuzzy logic and GAs have gradually started to be used.

Source: A Taskin Gu¨mu¨s* and A Fuat Gu¨neri Turkey, 2007. “Multi-echelon inventory management in supply chains with uncertain demand and lead times: literature review from an operational research perspective”. IMechE Vol. 221 Part B: J. Engineering Manufacture. June, pp.1553-1570.

译文：

供应链下的多级存货管理

从历史上看，多级供应链、仓库、分销商、零售商等，已经通过大量的库存缓冲被独立管理。竞争压力的增加和市场的全球化迫使企业发展能够快速满足客户需要的供应链。为了保持竞争力，降低库存，这些企业必须交互使用多级库存管理，同时降低运营成本，改善客户服务。

供应链管理是一种集成的管理思想和方法，它执行供应链中从供应商到最终用户以及公司内部不同部门之间的物流的计划和控制等职能。最近几年，供应链管理这个领域已经引起了广泛关注，并且它被视作能够帮助企业获得竞争优势的一种工具。供应链管理是为了在满足服务水平的同时使系统成本达到最小，从而把供应商、制造商、仓库和商店的业务效率都整合起来，使商品以正确的品质、在正确的地点、在正确的时间进行生产和销售的一套方法。可见，供应链由各种各样的成员或者阶段组成。供应链是一个动态的、随机的和复杂的系统，因此，需要许许多多的人来参与。

在制造企业中，存货通常占企业总资产的20%到60%。因此，对于那些追求

利润的企业，存货管理政策是关键。当考虑到整个供应链 ，也就是一个由采购、转化和运输公司组成的网络时，存货管理在很大程度上就与这整个供应链有关。在供应链管理中，存货管理的确是一种主要方法，它能从整体上解决供应链问题。

因各种不同的原因，存货以不同形式存在在供应链中。在整个供应链中，存货管理失衡，经常会引起“牛鞭效应”，即需求逆流而上，逐级变异放大的一个阶段。这种效应引起企业过多的存货积压，使收入减少，运输效率降低，扰乱了库存计划和产品生产计划，同时降低了企业的服务水平。

许多学者已经对这些问题进行了研究，并且强调了对有效地满足客户需求的供应链各阶段之间进行整合的必要性。除了整合问题，为了确定一个有效地供应链库存政策，还必须处理不确定性问题。除了对供应（比如交货时间）和需求的不确定性，与生产和销售过程相关的信息延迟也是供应链的一个特点。

多级供应链中的库存管理是一项重要的内容，因为有许多方面两者都必须相互配合，协调合作。它们还必须对它们的库存进行协调安排。有许多因素使成功的库存管理变得复杂，例如。需求的不确定、交货时间、投产日期、产品价格、成本等，尤其是在不确定性的需求和交货时间下，管理者不能够将多级供应链中的存货管理得最优。

大多数制造企业被组织起来形成了一个制造和分销为一体的网络，这个网络包括了原材料的采购、加工和产品的销售。当一个产品经过多个阶段才到达最终用户时，多级或者多层次生产/分销网络这些代名词也和前面所述的这样的网络意思相同。因各种不用的原因，存货以不用的形式存在在整个供应链中。在任何一个制造过程中，它们可能作为原材料、在制品或者产成品存在。它们存在于配送仓库，存在于运输途中，或者存在于管道里，它们存在于这些设备的每个链接处。

制造商从供应商处采购原材料，将它们加工成产品并销售给分销商，然后由分销商销售给零售商或者用户。当一个产品经过多个阶段才到达最终用户，它就形成了一个多级库存系统。某一库存节点的级库存等于这个库存节点上的所有库存加上转移或者正在转移的任何一个后续节点的库存，减去后续节点的缺货。

在商界有关多级库存系统的分析已经有着悠久的历史。在许多领域，多级库存管理系统被广泛运用于向客户分销产品。鉴于这些系统的重要性，许多研究人

员通过各种各样的条件和假设开始研究他们的运行特点。自从哈里斯提出经济订货批量模型以来，研究人员和实际工作者更加积极地关注在不同操作参数和模型假设条件下系统的分析和模型设计。在过去的十年里，对于多级库存管理模型的研究已经获得了重要成就，主要是因为通过利用现代信息技术，使各个过程和分销阶段的供应链的整体控制逐渐变成可能。克拉克和斯卡夫最早研究两阶段存货模型。他们证实了库存系统的基础存货政策的最优性，并提出了一种用于计算最佳订货批量的政策。贝斯勒和凡诺特进一步发展了两阶段模型，使其包含一般块茎结构。上面提到的车间仓库问题通过埃本和施拉格分析一个缺货的中央仓库模型解决了。他们在相等的订货点分配假设条件下，对订购批量做出了更近似的表述。一些作者也已经考虑到了在各种形式下的这个问题。由于多阶段问题的复杂和棘手，哈德利和怀廷建议对库存系统采用单层次、单阶级模型。

夏布鲁克把一个订购政策看做是一个仓储和零售商的两级模型，他假设零售商的缺货是完全积压的，而且，夏布鲁克还建立了矩阵（可收回项目控制的多级技术）模型，它明确了在有预算约束的一个低级阶段中使库存水平最小化，这个模型是管理服务部分库存的第一个多级模型，此后，很多研究者提出了一大套模型，他们一般都是在多级框架下寻求最佳批量和安全库存。除了分析性模型，仿真模型也被开发了出来用于研究多级库存问题中复杂的相互作用问题。

到目前为止，相关的一些文献主要关注于对需求的预测，以及对多阶段供应链库存政策的发展。需求随机的多阶段系统的存货控制政策已经具有了一个广泛的研究领域。近年来有许多论文都包含了斯尔福和派克的观点。用于定期评估标准的统一采购的优点是可以通过规定不同阶段的订购水平获得连续不断的评估标准，这是就所有库存而言，而不是单指设备。

劳以及其他人，迪克斯和戴科克，唐格和里，密特拉和查特基，哈里加，陈，阿克斯特和章，诺齐克和特纳基斯特以及赛欧和郑都在他们的研究中利用了数学模型技术去管理供应链中的多级存货。迪克斯和戴科克的研究考虑到了不同的多级存货系统，比如配送系统或者生产系统，并且假设订单在一个固定的时间内到达。哈里加提出了由若干个装配或者整理和储存设备串联在一起组成的单个周期生产系统的随机模型。阿克斯特，诺齐克和特纳基斯特在他们的文章中都提到了两阶段库存系统。阿克斯特和特纳基斯特认为零售商都面临不变的无偏好的泊松

需要。麦彻和查特基研究了博特和格拉夫模型，并在他们关于实行快速配送商品的观点的论文“对随机需求下的多级存货问题策略不间断回顾”中进行了进一步的阐述。这个模型的提出和改进能够扩展多个阶段和两阶段配送系统的内容。在劳尔的模型中，假设订货时间忽略不计，需求率和生产率是确定的，而且保持固定不变的情况下，缺货是不允许的。赛欧和郑运用分析模型分析两个重要因素，这两个因素能够帮助半导体制造商根据经验推测订单数量变化的最高程度：一个是供应商的订货时间，另一个是预测的需求更新情况。他们认为那儿的零售商面临的外部需求与两个连续的时间段相联系，并且零售商利用最新的需求信息来更新它们未来的需求预测。此外，他们还认为供应商的供货时间是变动的，而且受零售商的订货量的影响。顿和里在他们的论文中再次阐述了克拉克和斯卡夫的连续多级存货系统并得出了三个关键的结论。第一，他们提出了最佳多级存货水平的最小近似值以及克拉克和斯卡夫关于基本模型的整个系统成本的最大值。第二，他们利用马丁格尔预测理论模型说明了克拉克和斯卡夫的最优存货政策结构保留了在与时间线关联下的按需处理。第三，他们把近似值拓展到了与时间相关联的需求的过程和研究，特别是对于一个回归需求模型，订货时间的影响和一系列存货系统性能的相关性。

通过对有关利用数学模型技术研究供应链下的多级存货管理的文献的回顾，总括起来，可以说，这些文章都考虑到了具有不确定的或者确定的需求的两级，三级或者若干级系统。他们认为订货时间是固定的，为零，是一个常量，确定的或者是可以忽略的。他们获得了准确的或者是相似的结论。

德克尔等人分析了数量分段规律对存货成本的影响。数量分段规律是指传递来自供应商的大订单, 以及来自最近的零售商的小订单, 也就是所谓的进行分段数量判定订单是小型的还是大型。在由一个供应商和多个零售商组成的系统中,假设所有零售商的客户都存在需求。然而,德克尔等人指出传递来自供应商的那些大型的订单会导致零售商们考虑降低自身的存货成本。德克尔等人的研究成果还涉及到了供应商的存货成本。在莫诃比和波斯纳的研究中包含了存在补充订单和销售损失的不断审查的存货系统的成本分析。在范德等人的文章中考虑到了当同时存在需求和订货时间不确定情况下的多级存货，周期审查，订制点这些政策。

饭田这篇文章的主要目的是表明近期政策对于多级库存问题是可接受的。他

假设在各阶段的订货时间是固定的。陈和宋的目标是缩小系统中的长期平均成本。在陈的系统中，各地应用一种定期回顾或者订货点库存政策。他们表明各地的库存位置是稳定的，并且这种稳定的分销是均匀且独立于其他的。在明纳等人的研究中，他调查了在一个由中心仓库和一些当地库存点组成的分销网络中，生产不确定性对库存投资的影响。将和莫纳罕论述了一个两梯队双通道库存模型，在这个模型中库存是由生产商仓库（上游）和零售店（下游）共同负责的，而产品使用两种供应渠道：传统的零售店和网络直销。约翰森的系统被假设由基本库存策略控制，其中比较了独立的和随机不独立的订货期。

总之，这些文章都基于一般随机需求来考虑两梯队或者Ｎ梯队库存系统，但有一篇例外，它考虑了马尔可夫需求调节。通常他们假设固定的订货点，但是其中有两个认为那是随机的。而他们得出了一样或者相近的解决方法。

在这些多级库存管理文献中用到了很多其他研究方法，比如启发法、变化度量法、隐约集法、模型预测控制法、情景分析法、数据分析法和汇编语言，这些方法很少用而且只有少数作者会用到。

陈和李提出了一个多产品、多阶段、多时期计划模型来解决带有市场需求和产品价格不确定性的多级存货供应网络中的多目标。其中不确定的市场需求通过一系列各种可能性建成的离散方案模型解释，而模糊设置用于解释买卖者基于产品价格的不相容偏好。

最近有一篇文章从运筹学的角度详细回顾了从1996年到2005年在供应链中的多级库存管理。而本文，再次强调了需求和订货时间的不确定性。

文献回顾总结如下：使用最广泛的研究方法是模拟法，除此之外，分析法、数学法和随机建模法也普遍用于文献中。最近启发式模糊逻辑法和汇编语言也开始慢慢使用起来。

出处：[土耳其]塔斯金，福阿德.《基于不确定需求和交货期的供应链下的多级

存货管理：从操作研究角度进行文献回顾》，英国机械工程师协会，第221卷（B），2007(6):1553-1570.

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