供应链下的多级存货管理外文翻译(节选)

中文4800字，3200单词，1.9万英文字符

出处：Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture, 2007, 221(10):1553-1570.

原文

Multi-echelon inventory management in supply chains with uncertain demand and lead times: literature review from an operational research perspective

A TaskinGu¨mu¨s* and A FuatGu¨neri

Abstract:

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. The current paper reviews the literature, addressing multiechelon inventory management in supply chains from 1996 to 2005. The behavior of the papers against demand and lead-time uncertainty is the key analysis point of the literature review presented here and it is conducted from an operational research point of view. Finally, directions for future research are suggested.

Keywords: supply chain, multi-echelon inventory management, demand uncertainty, lead-time uncertainty

1 INTRODUCTION

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 participant.

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 [4]. 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, becausethere 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.

In the current paper, a detailed literature review is presented, addressing multi-echelon inventory management in SCs from 1996 to 2005. Here, the behavior of the papers against demand and lead time uncertainty is emphasized. First, echelon concept and multi-echelon inventory management in SCs are defined. Then, the literature review conducted from an operational research point of view between 1996 and 2005, is presented. Finally, directions for future research are suggested.

2 MULTI-ECHELON INVENTORY MANAGEMENT IN SUPPLY CHAINS

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 networ ks (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, minusthe 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 studiedtheir 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 arborescent structures. The depot warehouse problem described above was addressed by Eppen and Schrage who analysed 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 Whitinrecommend 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 stockouts at the retailers arecompletely backlogged. Also, Sherbrookeconstructed the METRIC (multi-echelon technique for recoverable item control) model, which identifies the stock levels that minimize the expected number of backorders at the lower-echelon subject to a budget 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 interactions of the multi-echelon inventory problems.

Figure 1 shows a multi-echelon system consisting of a number of suppliers, plants, warehouses, distribution centres, and customers.

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 systems 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

3 LITERATURE REVIEW: FROM 1996 TO 2005

In this section, a detailed literature review, conducted from an operational research point of view, is presented. It addresses multi-echelon inventory management in SCs, from 1996 to 2005. The selection criteria of the papers that are reviewed are: using operational research techniques to overcome multiechelon inventory management problems, and being demand and lead time sensitive (there are uncertain demand and lead times). Here, the behavior of the papers against demand and lead time uncertainty is emphasized.

The papers reviewed here are categorized into groups on the basis ofthe research

techniques in which they are used. These techniques can be grouped as:

(a) mathematicmodeling (only);

(b) mathematicmodeling and other techniques (in the same paper); (c) METRIC modeling;

(d) Markov decision process;

(e) simulation (only);

(f) Stackelberg game;

(g) literature review;

(h) other techniques (vari-METRIC method, heuristics, scenario analysis, fuzzy logic, etc.).

While the research techniques are common for papers that are grouped according to their research techniques, the number of echelons they consider, inventory/system policies, demand and lead time assumptions, the objectives, and the solutions’ exactness may be different. Therefore these factors are also analysed.

Mathematic modeling technique

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 modelling 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 Nozick and Turnquist assume that the retailers face stationary and independent Poisson demand. Mitra and Chatterjee examine De Bodt andGraves’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, sh ortage 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 analyse 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 the retailer 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 multiechelon 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.

Mathematic modeling and other techniques together

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 van der Haiden et al. is an echelon stock, periodic review, order up-to (R,S) policy, under both stochastic demand and lead times.

Andersson and Markland’sapproach is based on an approximate cost-evaluation technique. Axsater presents a method for exact evaluation of control policies that provides the complete probability distributions of the retailer inventory levels. Mitra and Chatterjee examine the effect of utilizing demand information in a multi-echelon system. Seferlis and Giannelos present an optimization-based control approach that applies multivariable model-predictive control principles to the entire network. The inventory system under Seifbarghy and Jokar’s consideration uses continuous review inventory policy (R,Q) and assumes constant lead times. In Moinzadeh’s paper, each retailer places their order to the supplier according to the well-known ‘Q,R’ policy. It is assumed that the supplier has online information about the demand, as well as inventory activities of the product at each retailer, and uses this information when making order/replenishment decisions. Tang formulae aredeveloped for solving the optimal planned lead times with the objective of minimizing total stock out and invent or holding costs. Axsater assumes that the system is controlled by continuous review installation stock (R,Q) policies with given batch quantities and presents a simple technique for approximate optimization of the reorder points.

Cachon and Fisherand Tsiakis et al. use mathematical modelling and scenario analysis in their studies. Cachon and Fisher consider a twoechelon inventory system with stochastic demand, while Tsiakis et al.consider a four-echelon inventory system with time-invariant demand, differently from most studies. Cachon and Fisher [58] study the value of sharing demand and inventory data in a two-echelon inventory system, while Tsiakis et al.’s objective is the minimization of the total annualized cost

of the network Chiu and Huang use mathematical modeling and simulated annealing algorithm in their studies and consider an N-echelon serial SC. Their paper proposes a multi-echelon integrated just-in-time inventory (MEIJITI) model with random-delivery lead times for a serial SC in which members exchange information to make purchase, production, and delivery decisions jointly.

Parker and Kapuscinski use mathematical modeling and Markov decision processes in their paper, and consider a two-echelon inventor system with stochastic demand. Extending the Clark and Scarf model to include installations with production capacity limits, they demonstrate that a modified echelon base-stock policy is optimal in a twostage system when there is a smaller capacity at the downstream facility.

A multi-product, multi-stage, and multi-period production and distribution planning model is proposed in Chen and Lee to tackle the compromised sales prices and the total profit problem of a multi-echelon SC network with uncertain sales prices. They use mathematical modeling (mixed integer non-linear programming) and fuzzy optimization in their study.

Jalbar et al. use mathematical modeling, Schwarz heuristic, Graves and Schwarz procedure, Muckstadt and Roundy approach, and O(N log N) heuristic in their paper, and consider a two-echelon inventory system with one-warehouse and N-retailers. The goal is to determine single-cycle policies that minimize the average cost per unit time, that is, the sum of the average holding and set-up costs per unit time at the retailers and at the warehouse.

In Routroy and Kodali’s paper mathematical modeling and differential evolution algorithms are used. A three-echelon inventory system is considered consisting of a retailer, a warehouse, and a manufacturer.

Han and Damrongwongsiri’s purpose is establishing a strategic resource allocation model to capture and encapsulate the complexity of the modern global SC management problem. A mathematical model is constructed to describe the stochastic multi-period two-echelon inventory with the many to-many demand–supplier network problem. Genetic algorithm (GA) is applied to derive near optimal solutions through a two-stage optimization process. Demand in each period can be represented by the probability distribution, such as normal distribution or exponential distribution.

Most of the papers reviewed here use simulation with mathematical modeling. They consider intensively two-echelon inventory system with stochastic demand, 1, 3, or N-echelon systems are rarely considered. They gain exact or approximate solutions.

METRIC modeling technique

Moinzadeh and Aggarwal use METRIC modelling and simulation techniques in their study, while Andersson and Melchiorsand Wang et al. use METRIC modelling only. The three of them consider a two-echelon inventory system with stochastic demand, and obtain approximate solutions.

Moinzadeh and Aggarwal study a (S-1,S)-type multi-echelon inventory system where all the stocking locations have the option toreplenish their inventory through either a normal or a more expensive emergency resupply channel. Wang et al. study the impact of such centre-dependent depotreplenishment lead times (DRLTs) on

system performance. Andersson and Melchiorsevaluate and optimize S-1,S-policies for a two-echelon inventory system consisting of one central warehouse and an arbitrary number of retailers.

Markov decision process technique

Iida , Chen and Song , Chen et al. , and Minner et al. use the Markov decision process in their studies, while Chiang and Monahanuse Markov decision process and scenario analysis, and Johansen uses Markov decision process, simulation, and Erlang’s loss formula together. Iida and Chen and Song consider an N-echelon inventory system, but under stochastic demand in the first study and Markov-modulated demand in the second one, respectively. Chen et al. ,Minner et al. , and Chiang and Monahan consider a two-echelon inventory system with stochastic demand. Johansen considers a single-item inventory system and a sequential supply system with stochastic demand.

The main purpose of Iida’s paper is to show that near-myopic policies are acceptable for a multiechelon inventory problem. It is assumed that lead times at each echelon are constant. Chen and Song’sobjective is to minimize the long-run average costs in the system. In the system by Chen et al. , each location employs a periodic-review (R,nQ), or lot-size reorder point inventory policy. They show that ea ch 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 localOther techniques

In multi-echelon inventory management there are some other research techniques used in literature, such as heuristics, vari-METRIC method, fuzzy

sets, model predictive control, scenario analysis, statistical analysis, and GAs. These methods are used rarely and only by a few authors.

The paper by Chandra and Grabis quantifies the bullwhip effect in the case of serially correlated external demand, if autoregressive models are applied to obtain multiple steps demand forecasts. Here, under autoregressive demand, inventory management of a two-echelon SC consisting of a retailer and a distributor is considered. It is assumed that the lead time is deterministic. The papers using the other techniques consider (one-, two-, three-, four-, five-, or N-echelon systems) assume stochastic, constant, fuzzy, or deterministic demand and lead times. All of them obtain approximate solutions.

4 FINDINGS OF THE LITERATURE REVIEW

Limited echelons of a multi-echelon inventory system is usually considered in the literature. They rarely generalize their models to N-echelon. Similarly, they usually consider serial systems, instead of a tree conformation.

The authors generally assume demand and lead times to be stochastic, deterministic, constant, or negligible. There are only a few studies that find these variables with heuristics, fuzzy logic, and GAs. These techniques are not examined adequately yet in inventory management in multi-echelon SC.

In addition, the papers present mostly approximate models. There are a small amount of papers that give exact solutions.

译文

不确定的需求和交货期供应链下的多级存货管理：

运筹学的角度的一个文献回顾

摘要：从历史上看,供应链下的仓储、分销商、零售商等各层级一直都是独立管理，通过维持很多库存来保证价交易的正常进行。日益增长的竞争压力和市场全球化，迫使企业开发供应链，以快速的响应客户需求。为了保持竞争力,降低库存,这些公司必须使用多级库存管理交互系统,同时减少运营成本来改善客户服务。当前的文献资料，着力于研究从1996年到2005年的供应链库存管理。本文对需求和交货时间的不确定性研究，主要是从运筹学的角度来进行的。最后,对未来的研究方向做了一个建议。

关键词:供应链；多级库存管理；需求的不确定性；交货时间的不确定性

1.引言

供应链管理(SCM)是一个综合的材料控制、供应商和客户之间信息流动的计划和方法。这个领域方面的研究，近年来吸引了学者们的广泛关注。它被看作是一个提高企业竞争力的工具。供应链管理是一组方法，用来整合供应商、制造商、仓储和零售商，进行有效地生产，并在正确的数量，在正确的地点和时间，以最小的成本满足客户的服务要求。因此,供应链由不同的成员或阶段所组成的。供应链是一个动态的、随机的复杂的系统，可能涉及数百个参与者。

库存成本通常占制造业公司的总资产的20%—60%。因此，库存管理策略对这些公司的利润来说很重要。库存，在更大程度上说,关系到整个供应链(SC),即供应链是一个加工、转换和传递到公司的一个系统。库存管理也确实是供应链管理方面的一个关键问题,需要在集成的视角下解决了供应链问题。

库存管理由于种种原因，以各种形式贯穿于供应链过程的始终。在供应链系统中，如果缺乏一个协调的库存管理，那么经常会导致牛鞭效应,即一个放大的需求的变化，不断地向上游阶段放大需求。这将导致过多的库存成本，造成利润损失,也会造成错误的生产计划和低水平的客户服务。许多学者研究过这些问题，强调要整合供应链的各个阶段,有效地满足客户请求。关于集成问题,必须处理好交货期的不确定性。除了供应的不确定性(如交货期和需求信息延迟)。

供应链下的多级库存管理是一个重要的问题，因为在供应链中，有许多元素互相协调。他们必须协调好他们的库存。有很多复杂的因素会影响供应链的协调，比如不确定的需求、交货期、生产时间、产品价格、成本等,特别是需求和交货期的不确定性。在当前的文献中,对于解决多级库存问题，从1996年到2005年的文献已经做了一个详细的文献综述。在这里, 本文对需求和交货时间的不确定