inventory control of a multiproduct system with a limited production resourse

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物流成本控制中英文对照外文翻译文献

中英文翻译原文Logistics costs and controllingAbstractLogistic costs are defined differently in companies. In many cases, the reported logistic costs of companies even within the same business differ more than justified by their operations. Some companies do not count interest and depreciation on inventories as logistic costs. Others include the distribution costs of their suppliers or the purchasing costs. In some cases, even the purchase value of the procured goods is included in the logistic costs (Baumgarten et al. 1993; Gudehus and Kotzab 2004; Weber 2002).Logistic costs are defined differently in companies. In many cases, the reported logistic costs of companies even within the same business differ more than justified by their operations. Some companies do not count interest and depreciation on inventories as logistic costs. Others include the distribution costs of their suppliers or the purchasing costs. In some cases, even the purchase value of the procured goods is included in the logistic costs (Baumgarten et al. 1993; Gudehus and Kotzab 2004; Weber 2002).Another problem, which arises not only in logistics, is costing and pricing of intangible goods. Intangible goods, such as logistic services, provide immediate utility and are generally not storable. Therefore, the conventional methods ofaccounting, costing and pricing, which have been developed for tangible goods, are of limited value for logistics (Cooper 1992; Horvàth 1999; Johnson 1987).More appropriate for the calculation of performance costs are process-related cost accounting and activity based costing. However, in logistics as well as in other areas of business, the definition and calculation of process costs differs (Bragg 2001; Hicks 2002; Horvàth 1999; Pohlen and LaLonde 1994; Poist 1974). This holds especially for the performance costs of multifunctional logistic systems, for pricing of integrated performances and for the consideration of fixed costs.As long as they are defined, measured and calculated differently, logistic costs, cost rates and prices cannot be compared. Any benchmarking based on such doubtful indicators is misleading (see Sect. 4.5). Hence, reported market volumes and market shares of logistics are at best educated guesses (Baumgarten et al. 1993; Kille and Klaus 2007; Müller-Steinfahrt 1998).The situation in logistic controlling and supply chain controlling is even worse (Cooper and Kaplan 1998; Manrodt et al. 1999; Seuring 2006). Only a minority of companies records and monitors logistic costs separately and continuously (Weber 2002). Whereas in industry the total logistic costs range between 5 and 15% of turnover, in trade companies they make up between 10 and 25% of turnover (Baumgarten et al. 1993; Gudehus 1999/2007). For retailers, logistic costs can use up more than one third of the profit margin. Despite this, it is still the exception for retailers to record and monitor the logistic costs from the ramp of the suppliers to the point of sales.Logistic controlling does not only include calculation, budgeting and recording of costs, but also the monitoring of performance and quality. Controlling should consult management in the planning, set up and operation of optimal systems. To enable this, it has to determine and specify for improvements in service, performance, quality, and costs (Cooper 1992; Darkow 2001; Horvàth 1999; Johnson 1987; Weber et al. 1993, 2002).Where and with what accuracy costs, performances and quality should be recorded and monitored depends on the contribution of logistics to the value creation, on the core competencies and objectives of the company, and on current projects. In logistic controlling, as in other areas, less is more: it is better to control a small number of meaningful key performance indicators (KPI) with adequate accuracy in longer time spans, than to monitor all possible performance, quality and cost data with high precision permanently without knowing the demand for these information(Manrodt et al. 1999). For controlling, not the precision of the performance and cost data, but their practical use and application are decisive.In this chapter, the logistic costs are consistently defined, the fundamental issues of logistic costing are presented, and practicable methods for the calculation of use dependent cost rates are developed. This includes a discussion of the fixed-cost dilemma of logistics, the relationship between logistic costs and performance rates and the most effective options for reducing logistic costs. Using the results of this chapter, in the following chapter cost-based prices and pricing systems for logistic performances and services are derived.Cost Accounting and Performance CostingCorresponding to the stationary or structural aspect and to the dynamic or process aspect, two different types of accounting are necessary. Cost accounting for longer periods keeps a stationary point of view, while performance costing for shorter periods reflects the dynamic perspective.Logistic Cost CalculationAs the general cost calculation of a company, the logistic cost calculation comprises standard cost calculation, accompanying cost calculation and final cost calculation (Horváth 1999; Weber 2002; Wöhe and Döring 2008).Standard Cost CalculationSubjects of standard cost calculation or planned cost calculation are the future operating costs for an existing or a planned system. Results are standard logistic costs and target performance costs.Standard cost calculation is necessary for investment decisions, for planning systems, processes and projects, for cost accounting and benchmarking of future periods and for the calculation of prices and tariffs.Accompanying Cost CalculationAccompanying cost calculation aims for a continuous control of all costs caused by the execution of logistic tasks and services during the current accounting period. The result of accompanying cost calculation is information for management about the current costs and utilization of resources.Knowing the costs and the utilization of the resources allows initiating appropriate measures for reducing costs, adaptation of resources and improving capacity utilization in due time. The results of the accompanying cost calculation can be used also for invoicing and compensation of logistic service providers, if costs-based prices have been agreed.Final Cost CalculationSubjects of final cost calculation or post calculation are the operating costs of closed periods in the past. The real logistic costs and cost rates can be compared with the respective target values and benchmarks. This allows conclusions for standard costing and pricing.Most important causes for deviations of real costs from the target values in logistics are:●Cost factors, especially personnel costs, have been planned, assumed orexpected too high or too low.●Utilization of resources, such as transport means, storage systems,machines, and production facilities, has been planned or expectedfalsely.●Empty runs of transport means and filling degrees of transport and loadunits were incorrectly planned.●The actual utilization structure of the logistic system differs from theanticipated structure.The first two reasons for differences between real and target costs are normally caused by the planner and the operator of a logistic system. A too high share of empty runs and bad utilization of storage capacities is in many cases also the result of unqualified planning or poor scheduling. However, this can be caused also by a user, who changed transport relations, demand structure or stock levels. Aninsufficient utilization can also be initiated by a wrong demand forecast or false information from the customers.For a dedicated logistic system, which is used for a longer period of time by one or a small number of companies based on individual contracts, the users must bear the risk of changing demand and the cost differences resulting from a deviating utilization of the ready held resources. Final cost calculation for dedicated logistic systems can be used for the utilization based allocation of surpluses or additional costs to the different usersFor a multi-user logistic system, where tasks and services are offered on the market and used only for shorter periods of time by many different customers, the risk for changing demand and insufficient utilization is born by the logistic service provider. This risk is compensated by the chances for higher profit from better utilization or favorable demand structure. Furthermore, the service provider can influence the demand by his sales efforts and by offering utilization dependent prices. For multi-user logistic systems the structure and utilization risk are incorporated in the pricesComponents of Logistic CostsThe total logistic costs are a sum of specific logistic costs, additional logistic costs and administrative costs:●Specific logistic costs are all costs of a performance station, a profit centeror a company, which are caused by executing the genuine operativelogistic tasks transport, handling, storing and commissioning.●Additional logistic costs are caused by executing additional operative taskswhich are directly connected with the genuine logistic tasks, such aspacking, labeling, loading and unloading, quality control or handling ofempties.●Administrative logistic costs are costs for related administrative services,such as scheduling, quality management and controlling, which go alongwith the execution of logistic performances and additional services.Costs for non-logistic tasks, such as research and development, construction, production, assembling, marketing, sales and general administration, are not part of the logistic costs. Also, the costs for buying and procuring merchandise, parts, material and equipment are not logistic costs as long as they are not directly caused by the execution of logistic tasks and related services. For instance, the costs for packing sales units are production costs, whereas the costs for packing material, pallets, bins and load carriers are material costs of logistics.When designing and optimizing company logistics as well as when scheduling orders and inventories, it is necessary to keep in mind that many logistic activities also have an effect on non-logistic costs and revenues. They influence setup costs, out-of-stock costs, disruption costs and ordering costs as well as prices, profit margins and turnover. Hence, logisticians always have to bear in mind the economic principle:Logistic activities as all other activities in the company should maximize the difference between revenues and costs at lowest capital investment.Elements of Logistic Costs●Personnel costs: wages for workers and salaries for employees with logisticresponsibilities, including personal taxes, vacation, illness, absence, etc.●Space and area costs: Depreciation and interest for the owned assets andbuildings, rents and leasing fees for external buildings, halls and areas,including related heating, climate, maintenance and surveillance costs.●Route and network costs: Depreciation and interest for own and fees forexternal driveways, routes, roads, highways, railroads and transshipmentpoints●Costs for logistic equipment: Depreciation, interest and operating costs forown as well as rental fees and leasing costs for external logistic equipmentsuch as racks, forklifts, transport means, cranes, conveyors and handlingequipment, control systems and process computers, including theequipment-caused energy, cleaning, repair and maintenance costs.●Load carrier costs: Depreciation and interest for own as well as rental feesand leasing costs for external load carriers, such as pallets, bins, barrels,racks, cassettes and containers, including the costs for cleaning, repair,maintenance and empties management.●Logistic material costs: Expenditures for packing material, transportpacking, load securing, labels and other material, which is needed in orderto perform logistic tasks and services.●Logistic IT-costs: Depreciation, interest and operating costs for ownIT-systems as well as costs for external IT-systems as far as used forlogistic purposes.●Third party logistic expenses: Freights, rental fees and other expenses forlogistic service providers.●Taxes, duties and insurance fees, which accumulate during the execution oflogistic tasks and services, as far as related to logistic purposes.●Planning and project costs: Depreciation and interest on activated expensesfor planning, project management and implementation accumulated up tothe start of the economic utilization of a logistic system.●Inventory holding costs: Interest and write offs on all stationary andmoving inventories, in stocks, on buffer places and in transport.In some companies the inventory holding costs include only the interest caused by the capital commitment. Obsolescence costs as well as write-offs due to non-marketability, deterioration or stock decline are often neglected. However, the write-offs on inventories of fashion, perishable, high value or electronic goods can be as high as or even higher than the interest.译文物流成本和控制物流成本定义在不同公司是不同的。

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. Thelack 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 thereare many elements that have to coordinate with each other. They must also arrangetheir 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 manufacturingand 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. Atany 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 thefinal 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 modelis 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 bysemiconductor 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 V ander Heijden et al. is an echelon stock, periodicreview, 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 V ol. 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。

A production-inventory model for a two-echelon supply chain

A production-inventory model for a two-echelon supply chainwhen demand is dependent on sales teams'initiativesLeopoldo Eduardo Cárdenas-Barrón a,n,Shib Sankar Sana ba Department of Industrial and Systems Engineering,School of Engineering,Tecnológico de Monterrey,Ave.E.Garza Sada2501Sur,C.P.64849Monterrey,N.L.,Méxicob Department of Mathematics,Bhangar Mahavidyalaya,University of Calcutta,Bhangar,24Parganas(S),Indiaa r t i c l e i n f oArticle history:Received31May2013Accepted8March2014Available online18March2014Keywords:InventoryProductionSupply chainCollaborationBacklogginga b s t r a c tThis paper investigates the issue of channel coordination for a two echelon supply chain consisting of onemanufacturer and one retailer.In this supply chain,the demand is sensitive to promotional efforts/salesteams'initiatives.A production-inventory model is developed that considers the procurement cost perunit as a function of the production rate.To resolve the issues of channel coordination and promotion-based demand,a variety of centralized coordinating systems are explored.An analytical method isemployed to achieve optimal production rate,production lot size,backlogging and the initiatives of salesteams so that the proﬁts of both manufacturer and retailer are maximized.Some numerical examples aresolved in order to better understand the proposed production-inventory model.The results of a sensitivityanalysis are also provided.Finally,some conclusions and future researches are included.&2014Elsevier B.V.All rights reserved.1.IntroductionInventory theory and,in particular,the economic order quantity(EOQ)inventory model wereﬁrst introduced by Harris(1913).Sincethen,many variants of the EOQ inventory model have appeared inthe inventory literature.Some of these variants correspond to EOQmodels for supply chains.A supply chain is comprised of vendors/suppliers,assemblers/manufacturers,distribution centers,retailersandﬁnal customers.It is important to mention that a supply chaincomposed of two stages(i.e.,one manufacturer and one retailer)isconsidered a complex system.The success of any supply chain system depends on its level ofcollaboration and integration.In this direction,the study of supplychains from collaborative and integrative perspectives has recentlyattracted the attention of industrial engineers.Previous researchefforts exist in the inventory literature that have shown that whenthe supply chains are vertically integrated their channels proﬁts aremaximized(Wu et al.,2009;Li et al.,2009;Ru and Wang,2010;Sana,2011a,2011b,2012,2013;Wu,2013).Björk(2008)has found ananalytical solution of an economic production quantity model whilethe demand is crisp and the cycle time is fuzzy in nature.In a subsequent paper,Björk(2012)has investigated a fuzzy multi-item economic production quantity model to obtain analytical solution(i.e.,optimal size of some production batches)under uncertain cycletimes which are handled with triangular fuzzy numbers.Also,thereare several research works that study the integration of supply chainsystems with multi-stages.These studies have shown that the costs ofthe channels in integrated supply chains are minimized(Cárdenas-Barrón,2007,2012a,2012b;Ben-Daya et al.,2013;Cárdenas-Barrónand Porter,2013;Cárdenas-Barrón and Treviño-Garza,2014).In an oligopolistic marketing environment,the managers ofbusiness organizations have a lot of pressure to sell products todownstream channel members.Generally speaking,vendors per-suade their customers with sales teams'initiatives or promotionalstrategies,i.e.,free gifts,delay in payments,discounts,displays,packaging,special services and advertising,among others.In thisdirection,Goyal and Gunasekaran(1995)have proposed an inte-grated production-inventory-marketing model to determine theoptimal economic production lot size and economic order quantityof raw materials in a multi-stage production system considering theeffect of different marketing policies such as the price per unitproduct and advertising frequency on the demand of a perishableproduct.On the other hand,Nair and Tarasewich(2003)haveobtained the optimal design of promotional efforts such as freegifts,discounts and special ter,Krishnan et al.(2004)have shown that pricing,displays,free goods and advertising areessential actions to achieve maximum revenues.Subsequently,Sun(2005)has found that there exists a relation between the customersand different types of promotions that results in a solid impact onstronger brands.Afterwards,Szmerekovsky and Zhang(2009)havedeveloped the pricing options and two-tier advertising actionsbetween one manufacturer and one retailer when customers'Contents lists available at ScienceDirectjournal homepage:/locate/ijpeInt.J.Production Economics/10.1016/j.ijpe.2014.03.0070925-5273/&2014Elsevier B.V.All rightsreserved.n Corresponding author.Tel.:+528183284235;fax:+528183284153.E-mail address:lecarden@itesm.mx(L.E.Cárdenas-Barrón).Int.J.Production Economics155(2014)249–258demand is dependent on the retail price and advertising by both players.Xie and Wei(2009)and Xie and Neyret(2009)have investigated the optimal collaborating advertising strategies and equilibrium pricing in a two-layer supply chain.Additionally, Ramanathan and Muyldermans(2010)have studied the effect of promotional efforts on the sales of soft drinks.Recently,Sana(2010) has presented a multi-item EOQ model for perishable and amelior-ating products when the time varying demand is dependent on the enterprise's initiatives such as advertising and salesmen's initiatives. Furthermore,Sana(2011a,2011b)has developed EOQ models for similar products when the demand of the end customers depends on the stock level,selling price and sales teams'initiatives.In this paper,we develop a production-inventory model for a two echelon supply chain consisting of one manufacturer and one retailer. In the proposed model,the procurement cost per unit is assumed to be a convex function of production rate and the demand ofﬁnal customers is an increasing function of sales teams'initiatives.The stockout situation is also considered at the retailer channel.The cost of initiatives by sales teams is an increasing nonlinear function of the sales teams'initiatives.This cost is shared by the manufacturer and the retailer.The proﬁts per unit quantity of the manufacturer and the retailer are formulated by trading off the cost and proﬁt parameters. Three strategies are discussed for a centralized supply chain system.The rest of the paper is organized as follows.Section2introduces the assumptions and notations that are considered in the develop-ment of the production-inventory model.In Section3,the production-inventory model is formulated and analyzed.Section4illustrates the production-inventory model with numerical examples and a sensitiv-ity analysis is provided in Section5.Finally,Section6provides some conclusions and future research directions.2.Assumptions and notationThe following assumptions and notation are adopted to develop a production-inventory model for a two-echelon supply chain when demand is dependent on sales teams'initiatives.2.1.Assumptions1.The production-inventory model is developed for a single product.2.A single supply chain between the manufacturer and theretailer is considered and it represents a collaborative system,i.e.,both the manufacturer and the retailer take optimalstrategies jointly to achieve maximum proﬁt or minimum cost of the supply chain.3.The procurement cost per unit is a convex function ofproduction rate.4.The rate of demand is an increasing function of sales teams'initiatives.5.The rate of production,production lot size and shortage levelare considered as continuous decision variables.6.The sales teams'initiatives is a discrete decision variable.7.Shortages at the manufacturer are not allowed.8.Shortages at the retailer are permitted and these are fullybacklogged.2.2.Notation2.2.1.Parametersc r–Material cost per unit($/unit).L–Labor/energy costs($/cycle).α–Tool/die costs($/unit)which is proportional to production rate.w m–Selling price per unit of the manufacturer($/unit).w r–Selling price per unit of the retailer($/unit).h f–Inventory holding cost per unit per unit time of theﬁnished product($/unit/time unit).h r–Inventory holding cost per unit per unit time of the rawmaterials($/unit/time unit).c3–Shortage cost per unit per unit of time($/unit/time unit).A m–Setup cost of the manufacture($/setup).A r–Setup cost of the retailer($/setup).d0–Theﬁrst part of the demand rate which is independent of the sales teams'initiativesðρÞ(units/time unit)k–Cost per unit effort of the sales teams'initiatives($/unit).m–An elasticity parameter.τ–A scale parameter of2nd part of the demand which varies with the sales teams'initiativesðρÞ(units/time unit).β–Sharing factorð0rβr1Þof the cost for initiatives of sales teams.2.2.2.Dependent variablesDðρÞ–Demand rate of the products(units/time unit).c pðpÞ–Procurement cost per unit that varies with the produc-tion rate($/unit).πM–Proﬁt per unit quantity of the manufacturer($/unit).πR–Proﬁt per unit quantity of the retailer($/unit).πj–Joint proﬁt per unit quantity($/unit).2.2.3.Decision variablesp–Production rate per unit time(units/time unit),a continuous variable.q–Production lot size(units),a continuous variable.ρ–Initiatives of the sales teams(measured by point scale such as1-point,2-points,etc.),a discrete variable.s–Shortage level(units),a continuous variable.3.The modelIn the production-inventory model,the rate of demand for theﬁnal customers is given by the following expression DðρÞ¼d0þτð1À1=ð1þρÞÞ.Theﬁrst part of the demandðd0Þis independent of the sales teams'initiativesðρÞ.On the other hand, the second part of the demand is a bounded increasing function of ρ;unlike the unbounded function ofρ.Here,D-ðd0þτÞwhen ρ-1;and D-d0whenρ-0.It is important to remark that the term d0tends to zero when new products are launched into the market.In this case,the demand of the product is zero whenρ-0;i.e.,the quality and prices are not familiar to the customers; whereas the demand of the familiar products is already increased by promotional efforts such as free gifts,better services,discounts, delay in payments,etc.The unit ofρis measured by the volume of the efforts made by the sales teams like as the number of efﬁcient salesmen and the above promotional efforts.It does not have any traditional units but it is measured by point scale such as1-point, 2-point,rger point scale includes more promotional effort that results in higher cost,in practice.The procurement cost per unit is determined as c pðpÞ¼c rþL=pþαp,where c r is theﬁxed cost of material per unit item.The term L is the energy/labor costs which are equally distributed over the production rate.This cost is reduced by the larger production rateðpÞ.The termαp represents tool/die costs which are proportional to the production rate.Here, c pðpÞis a convex function of p.Then c pðpÞis minimal at p¼ﬃﬃﬃﬃﬃﬃﬃﬃL=αp. The cost of sales teams'initiativesðρÞis given by kρm,where kðZ0ÞL.E.Cárdenas-Barrón,S.S.Sana/Int.J.Production Economics155(2014)249–258 250is a scale parameter and m ð40Þis the elasticity parameter.The formulation of the pro ﬁt functions of the manufacturer and the retailer are detailed in the next section and they consider the demand ðD ðρÞÞ,cost function of sales teams'initiatives ðk ρm Þand procurement cost function.3.1.Manufacturer 's individual pro ﬁtAt the manufacturer channel (see Fig.1),the production startswith raw materials-order lot size ðq Þ:In this process,two situations occur:the ﬁrst one is when the inventory decreases with produc-tion rate ðp Þand it reaches zero at time t 1.The second one is when the ﬁnished products,after production,pile up with rate p up to time t 1¼q =p .Then,the inventory cost for holding raw materials is given by ð1=2Þh r qt 1¼ð1=2p Þh r q 2and the inventory cost for holding ﬁnished products is determined as ð1=2p Þh f q 2.The revenue from selling items is w m q .The setup cost of the manufacturer is A m .The sharing cost of initiatives of sales teams'is k βρm .Therefore,the average pro ﬁt per unit quantity for the manufacturer is calculated as:πM ¼w m Àc r ÀL p Àαp À12p ðh f þh r Þq Àk βρm q ÀA mqð1Þ3.2.Retailer 's individual pro ﬁtIn the retailer channel,the inventory starts with shortages and continues up to time μ0at which the order size q is reached.After adjusting the shortages at time μ0,the remaining amount ðq Às Þsatis ﬁes the demand for the time span ðμ1Þ,as depicted in Fig.2.It is easy to see that μ0¼s =D and μ1¼ðq Às Þ=D .Therefore,the average pro ﬁt per unit quantity for the retailer is given by πR ¼w r Àw m À1h f ðq Às Þ2Àc 3s 2Àk ð1ÀβÞρm ÀA rð2Þ3.3.Centralized systemThree strategies of the supply chain in a centralized system are considered,as follows:Case 1.The manufacturer and the retailer are both decision makers and they jointly optimize a pro ﬁt function.In this case,the joint pro ﬁt function is πj ¼πM þπR¼w r Àc r ðÞÀL Àαp À1h f þh r ÀÁq À1h f ðq Às Þ2Àc 3s 2Àk ρm ÀA m þA r ðÞð3ÞNow,differentiating πj with respect to q ;p and s ,while keepingρﬁxed,we obtain∂πj ∂q ¼À12p h r þh f ÀÁÀ12D h f þ12D h f þc 3ÀÁs q2þk ρm q2þ1q 2A m þA r ðÞð4Þ∂πj ∂p ¼L Àαþ1h f þh r ÀÁq ð5Þ∂πj ∂s ¼Àh f Dqðs Àq ÞÀc 3s Dq ð7Þ∂2πj ∂q2¼À2q 3k ρmþh f þc 3ÀÁ2D s 2þA m þA r !o 0;8q A R þð8Þ∂2πj ∂p 2¼À2L q 3À1p 3h r þh fÀÁq o 0;8p A R þand q A R þð9Þ∂2πj∂s 2¼Àh f þc 3ÀÁDq o 0;8q A R þð10Þ∂2πj ∂q ∂p ¼12p2h f þh r ÀÁq ð11Þ∂2πj ∂s ∂q ¼h f Dq s Àq ðÞþh f þc 3s Dq ð12Þ∂2πj¼0ð13ÞFor optimum values of πj ,∂πj =∂q ¼0,∂πj =∂p ¼0and ∂πj =∂s ¼0.Then ∂πj =∂p ¼0provides usp ¼ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ2L þh r þh f ÀÁq2αs :ð14ÞAnd ∂πj =∂s ¼0gives us s ¼h ff þc 3q :ð15ÞSubstituting the values of p and s in ∂πj =∂q ¼0,we have a function of q which can only be solved by numerical methods.For feasibility of our model,we will only consider the positive values of q at which the objective function is maximum,i.e.,the Hessian matrix∂2πj ∂q 2∂2πj∂q ∂p ∂2πj∂q ∂s∂2πj ∂q ∂p ∂2πj ∂p 2∂2πj ∂p ∂s ∂2πj ∂q ∂s∂2πj ∂p ∂s∂2πj ∂s 20B B B B @1C C C C Ais negative de ﬁnite at the solution ðp n ;q n ;s n Þ,i.e.,the eigen values of the Hessian matrix are negative.L e v e l o f I n v e n t o r yFig.1.Inventory versus time of the manufacturer.L e v e l o f I n v e n t o r yFig.2.Inventory versus time of the retailer.L.E.Cárdenas-Barrón,S.S.Sana /Int.J.Production Economics 155(2014)249–258251Case 2.The manufacturer determines the optimal production rate ðp Þand optimal lot size ðq Þto maximize the pro ﬁt function πM and the retailer sets the shortage level ðs Þto maximize his/her pro ﬁt function πR .Now,∂πM ∂q ¼À12p h r þh f ÀÁþ1q 2k βρm þA m ÀÁ¼0impliesq ¼ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ2p k βρm þA m ðÞh r þh fÀÁs ð16Þ∂πM ∂p ¼L p 2Àαþ12p 2h r þh f ÀÁq ¼0impliesﬃﬃﬃ2p L Àαp 2ÀÁþﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃp k βρm þA m ðÞh r þh f ÀÁq ¼0;ðusing q Þð17ÞSolving Eqs.(16)and (17),we have the optimal values of pand q .d πR ∂s ¼Àh f Dq ðs Àq ÞÀc 3sDq ¼0impliess ¼h ff þc 3qð18Þ∂2πM ∂q¼À2k βρm þA m ÀÁo 0;8q A R þð19Þ∂2πM ¼À2L p À1p ðh r þh f Þq o 0;8p A R þand q A R þð20Þ∂2πM ∂q ∂p ¼12p2ðh r þh f Þð21Þtherefore,πM ðp ;q Þattains its maximum value at ðp ;q Þif ½ð∂2πM =∂q 2Þð∂2πM =∂p 2ÞÀð∂2πM =∂q ∂p Þ2 is positive at ðp ;q Þ.Again,d 2πR =ds 2¼Àðh f þc 3Þ=Dq o 0;8q A R þ.Therefore,πR ðs Þattains itsmaximum value at ðs n Þ.Case 3.The manufacturer determines the optimal production rate (p )to maximize his pro ﬁt function πM ðp Þand the retailer deter-mines the optimal values of ðq ;s Þto maximize his/her pro ﬁt function πR ðq ;s Þ.Now,differentiating πR ðq ;s Þwith respect to q and s ,we have∂πR ∂q ¼À12D h f 1Às 2q 2þc 3s 22q 2D þk 1ÀβðÞρm q 2þA rq 2ð22Þ∂πR ∂s¼À12qD h f À2q þ2s ðÞÀc 3sqD ð23Þ∂2πR ∂q 2¼Às 2h f þc 3ÀÁDq 3À2k 1ÀβðÞρm q 3À2A r q 3o 0;8q A R þð24Þ∂2πR∂s 2¼Àh f þc 3ÀÁqD o 0;8q A R þð25Þ∂2πR ∂s ∂q ¼s q Dh f þc 3ÀÁð26ÞFor the maximum value of πR ðp ;q Þ,∂πR =∂q ¼0¼∂πR =∂p ,and solving these,we haveq ¼ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ2D k 1ÀβðÞρm þA r ÂÃh f þc 3ÀÁh f c 3s ð27Þs ¼ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ2Dh f k 1ÀβðÞρm þA rÂÃh f 3ÀÁc 3s ð28ÞAlso,∂πM =∂p ¼L =p 2Àαþð1=2p 2Þðh r þh f Þq ¼0implies p n ¼ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ2L þh r þh f ÀÁqs ð29Þand∂2πM ∂p¼À2L À1ðh r þh f Þq o 0;8p A R þand q A R þð30ÞThe optimal values of p from Eq.(14)for Stakelberg approach (i.e.,when manufacturer is the decision maker and retailer is the follower of the manufacturer)is same as the optimal value of p from Eq.(29)for the joint pro ﬁt of the chain.This incident occurs because the production is only controlled by the manufacturer which is obvious according to the formulation of the model.Similarly,Eqs.(20)and (30)are identical due to same reason.Therefore,πR ðs ;q Þattains its maximum value at ðs ;q Þif ½ð∂2πR =∂q 2Þð∂2πR =∂s 2ÞÀð∂2πR =∂q ∂s Þ2 is positive at ðs ;q Þ.Again,d 2πM =dp 2¼À2L =p 3Àð1=p 3Þðh r þh f Þq o 0;8p A R þand q A R þ.Therefore,πM ðp Þreaches its maximum value at ðp n Þ.The above strategies are maximized by the following algorithm:Step 1.Set the initiatives of the sales teams ρ¼0:Step 2.Maximize the pro ﬁt functions applying the strategydescribed in Case 1.Set πρM ;1¼πM ,πρR ;1¼πR ,πρj ;1¼½πM þπR ;q ρ1¼q ;p ρ1¼p ,and s ρ1¼s .Step 3.Set ρ¼ρþ1.Step 4.If πρÀ1j ;1q ρÀ11r πρj ;1q ρ1Z πρþ1j ;1q ρþ11holds,then go to Step 5.Otherwise,go to Step 2.Step 5.Set Max 1¼πρj ;1q ρ1,ρn ¼ρ;q n ¼q ρ1;p n ¼p ρ1,s n¼s ρ1.Step 6.Set ρ¼0:Step 7.Maximize the pro ﬁt functions applying the strategydescribed in Case 2.Set πρM ;2¼πM ,πρR ;2¼πR ,πρj ;2¼½πM þπR ,q ρ2¼q ;p ρ2¼p ,s ρ2¼s .Step 8.Set ρ¼ρþ1.Step 9.If πρÀ1j ;2q ρÀ12r πρj ;2q ρ2Z πρþ1j ;2q ρþ12holds,then go to Step 10.Otherwise,go to Step 7.Step 10.Set Max 2¼πρj ;2q ρ2,ρn ¼ρ;q n ¼q ρ2;p n ¼p ρ2,s n¼s ρ2.Step 11.Set ρ¼0:Step 12.Maximize the pro ﬁt functions applying the strategydescribed in Case 3.Set πρR ;3¼πR ,πρj ;3¼½πM þπR ,q ρ3¼q ;p ρ3¼p ,s ρ3¼s .Step 13.Set ρ¼ρþ1.Step 14.If πρÀ1j ;3q ρÀ13r πρj ;3q ρ3Z πρþ1j ;3q ρþ13holds,then go to Step 15.Otherwise,go to Step 12.Step 15.Set Max 3¼πρj ;3q ρ3,ρn ¼ρ;q n ¼q ρ3;p n ¼p ρ3,and s n¼s ρ3.Step 16.Obtain the optimal strategy among Max 1;Max 2and Max 3,i.e.,Max ¼Max 1;Max 2;Max 3f g .Step 17.Stop.In a supply chain management system,the pro ﬁt of the whole chain is controlled by the strategies taken by the members of the chain.In Case 2(Example 2),major cost factors (procurement cost and inventory cost)are minimized by optimal product rate and production lot size that results in maximum pro ﬁt of the manu-facturer,and the cost for penalty due to shortages at retailer isL.E.Cárdenas-Barrón,S.S.Sana /Int.J.Production Economics 155(2014)249–258252minimized by optimal shortage level decided by the retailer.In Case1(Example1),the whole costs of the chain are shared by the both parties so that the proﬁt of the manufacturer is reduced comparatively to Case2.Consequently,the proﬁt of the whole chain of Case1is lower than the proﬁt of the chain for Case2 (Example2).The same reason occurs in Case3also.From the results,it is observed that the cost factors(i.e.,contribution of the individual member)of the members of the chain determine the maximum proﬁt of the chain and sometimes Stakelberg approach provides maximum proﬁt of the chain if the decision maker (manufacturer)has more investment in the business than the follower(retailer).4.Numerical exampleIn this section,three numerical examples are solved to illus-trate the use of the proposed algorithm and to facilitate the sensitivity analysis presented in Section5.Example1.The values for the model parameters and their corre-sponding units(if applicable)are as follows:d0¼100units/year,τ¼50units/year,c r¼20:0$=unit,L¼2400:0$=production cycle,α¼0:01$=unit,w m¼40:0$=unit,w r¼50:0$=unit,h f¼2:5$= unit=year,h r¼3:0$=unit=year,c3¼25:0$=unit=year,A m¼200:0$= setup,A r¼100:0$=setup,k¼1500:0$=unit,m¼1,andβ¼50%. Then,the required optimal solution(see Table1)isðρn¼1point; q n¼360:999units;p n¼582:473units=year;s n¼32:8181unitsÞat which the maximum values of the proﬁt functions are ðπM¼5:71895$=unit;πR¼4:3636$=unit;πj¼$10:0826$=unit;πj q n¼$3639:80Þ.Example2.The same parameters values used in Example1are considered for Case2For this example,the optimal solution (see Table2)isðρn¼1point;q n¼457:061units;p n¼604:725 units=year;s n¼41:551unitsÞat which the maximum values of the proﬁt functions are given asπM¼5:82701$=unit;πR¼À3:98519$=unit;πj¼9:81220$=unit;πj q n¼$4484:78Þ.Example3.Again,the same parameters values used in Example1are considered for Case3For this example,the optimal solution (see Table3)isðρn¼1point;q n¼305:778units;p n¼569:288 units=year;s n¼27:798unitsÞat which the maximum values of the proﬁt functions areðπM¼5:50741$=unit;πR¼4:44041$=unit;πj¼9:94782$=unit;πj q n¼$3041:82Þ.The above numerical examples show that the optimal costs of procurement per unit that result from applying Cases1–3differ from the minimum cost c p¼29:798$=unit at the optimal production rate p n¼ﬃﬃﬃﬃﬃﬃﬃﬃL=αp¼489:898units=year:The required optimal strategy is Case2(among the three cases),because the total proﬁt is greater than that obtained with Case1or Case3.In such a situation,both parties shall negotiate their proﬁts according to their investment in their businesses.5.Sensitivity analysisTo analyze the effect of changes to the parameters on the optimal solution,a sensitivity analysis is performed for the numerical examples.The results of the sensitivity analysis are shown in Tables4–6.The changes to the optimal valuesðρ;p;q;sÞhave been done (see Tables4–6)by decreasing/increasing the values of the key parametersðh f;h r;c3;w m;w r;L;α;A m;A r;k;βÞusing the following magnitudes:ðÀ50%;À25%;þ25%;þ50%Þ.It is observed that the optimal values ofðρ;p;q;sÞare insensitive to changes inðA m;A rÞsince the formulas ofðp;q;sÞare independent ofðA m;A rÞ.However, the proﬁt functions change signiﬁcantly with changes inðA m;A rÞ. In Case2,the optimal values ofðp;q;sÞare insensitive to changes in A r because the optimal formulas ofðp;q;sÞare independent of A r; whereasπR,πj andðqÂπjÞdecrease with an increases in A r.The optimal values ofρincrease when k decreases.This is quite natural in practice.From the sensitivity analysis of key parameters for Cases1–3,the following realistic features are observed:Table1Optimal solution of Example1for Case1.ρp n;q n;s nðÞπnM ;πnR;πnjπnjÂq n d c npt n10p n¼526:29πnM¼7:986962113.3510029.82310.256q n¼134:470πnR¼7:7282s n¼12:2253πnj¼15:71521n p n¼582:473πnM¼5:718953639.8012529.94510.620q n¼360:999πnR¼4:3636s n¼32:8181πnj¼10:08262p n¼658:558πnM¼2:28571325.23133.3330.2299 1.070q n¼704:359πnR¼À0:40422s n¼64:0326πnj¼1:88153p n¼731:157πnM¼À1:1109À6811.63137.5030.5940 1.465q n¼1071:24πnR¼À5:24748s n¼97:3852πnj ¼À6:35864Table2Optimal solution of Example2for Case2.ρp n;q n;s nðÞπnM;πnR;πnjπnjÂq n d c npt n10p n¼542:807πnM¼8:137263055.0510029.8500.366q n¼198:688πnR¼7:23888s n¼18:0625πnj¼15:376141n p n¼604:725πnM¼5:827014484.7812530.0160.756q n¼457:061πnR¼3:98519s n¼41:551πnj¼9:812202p n¼698:521πnM¼2:480211224.28133.3330.421 1.291q n¼901:568πnR¼À1:12227s n¼81:9607πnj¼1:357943p n¼793:344πnM¼À0:7751À10358137.5030.959 1.785q n¼1415:98πnR¼À6:5399s n¼128:725πnj¼À7:3150Table3Optimal solution of Example3for Case3.ρp n;q n;s nðÞπnM;πnR;πnjπnjÂq n d c npt n10p n¼515:555πnM¼7:556891446.9810029.8110.182q n¼93:8083πnR¼7:86799s n¼8:52803πnj¼15:424881n p n¼569:288πnM¼5:507413041.8212529.9090.537q n¼305:778πnR¼4:44041s n¼27:7980πnj¼9:947822p n¼637:066πnM¼1:952781008.76133.3330.1380.947q n¼603:103πnR¼À0:28017s n¼54:8276πnj¼1:672613p n¼700:259πnM¼À1:6391À6088.1137.5030.430 1.300q n¼910:412πnR¼À5:04813s n¼82:7647πnj¼À6:68723L.E.Cárdenas-Barrón,S.S.Sana/Int.J.Production Economics155(2014)249–258253。

餐饮业控制管理PPT教学课件

库房控制
Handling Damaged Goods
保管易碎物品
Issuing Procedures
发货程序
Stock-Taking
库存盘点
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Objectives of
Storeroom Control
库房控制的目标
Maintain an adequate supply for immediate operational needs 维持足够的供应，达到紧急运营的需要
物品: 龙眼类型: 罐装总重量: 450 克纯重量: 400 克品牌: 派派价格: 8.00 人民币
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12
PPT精品课件
谢谢观看
Thank You For Watching
13
采购的方法
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Importance of Controls in the
Purchasing Functions
采购程序控制的重要功能
✓ Ensure the hotel buys the correct quality & quantity 酒店采买的物品在数量和质量上的保证
✓ From the right source 正当的货品来源
Control System
控制系统
Inventory Control 库存控制 Purchasing and Control 采购控制 Yield Test 净料测试 The Recipe 食品配方
2020/12/10
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Inventory Control
库存控制
Storeroom Control
重量损耗
Are resulting from: 导致损耗的原因

inventory control

Inventory control
• 1. Inventory control includes the function of inventory ordering, purchasing, receiving goods, storing and issuing, and controlling level of inventory
• 5525= 3925+unit on order-8450, Unit on order= 10050
• 2) recording inventory level a. bin-card: received and issued balance.
code No.
location
Bin No.
amount
Req. No.
quantity
price
amount
quantity
price
amount
receipt
issue
inventory balance
date
quantity
GRN No.
date
quantity
Req. No.
• b. store ledger account:
material
code
date
receipts
es
inventory
GRN
quantity
price
--delivery note: supplier provides, store keeper signs ---goods received note: store keeper—account department, matched with purchase order, and supplier invoice. ---material requisition note: material can only be issued against a material requisition note, Including quantity, cost center, job number...

INVENTORY CONTROL 库存控制作业全英版..

INVENTORY CONTROL
库存控制
Inventory Control
• The reasons for inventory control are: 1.Helps balance the stock as to value, size, color,style, and price line in proportion to demand or sales trends.
5.Helps maintain a business reputation for always having new, fresh merchandise in wanted sizes and colors.
Controlling Inventory
• STEP 1: Inventory Planning
Inventory control requires inventory planning. Inventory refers to more than the goods on hand in the retail operation, service business, or manufacturing facility. It also represents goods that must be in transit for arrival after the goods in the store or plant are sold or used. An ideal inventory control system would arrange for the arrival of new goods at the same moment the las item has been sold or used. The economic order quantity, or base orders, depends upon the amount of cash (or credit) available to invest in inventories, the number of units that qualify for a quantity discount from the manufacturer, and the amount of time goods spend in shipment.

外文翻译--- 供应链管理下的库存控制

外文翻译--- 供应链管理下的库存控制在供应链管理环境下，库存控制仍然存在一些问题，需要企业及时解决。

主要问题包括以下几个方面：1.信息不对称在供应链中，不同企业之间的信息不对称问题比较严重，导致企业难以准确预测市场需求，从而影响库存控制的效果。

2.订单不稳定供应链中的订单不稳定性也是影响库存控制的重要因素之一。

订单不稳定会导致企业难以确定库存水平，从而影响供应链整体绩效。

3.物流配送问题物流配送问题也是影响库存控制的重要因素之一。

物流配送不畅会导致库存积压，增加企业的库存成本。

4.缺乏协调供应链中各个企业之间缺乏协调也是影响库存控制的重要因素之一。

缺乏协调会导致企业之间的库存信息不同步，从而影响供应链整体绩效。

为了解决这些问题，企业需要采取一系列措施，如加强信息共享、优化订单管理、完善物流配送体系、建立协调机制等，以提高供应链整体绩效和库存控制的效果。

尽管从宏观角度来看，供应链管理环境下的库存控制比传统管理更具优势，但实际操作中，由于每个企业对供应链管理的理解存在差异，存在利益冲突等问题，导致实际运用时也会出现许多问题。

其中，主要存在以下几个方面的问题：1.各企业缺乏供应链管理的整体观念，导致各自为政的行为降低了供应链整体效率。

2.交货状态数据不准确，导致客户不满和供应链中某些企业增加库存量。

3.信息传递系统低效率，导致延迟和不准确的信息，影响库存量的精确度和短期生产计划的实施。

4.缺乏合作与协调性，组织障碍是库存增加的一个重要因素。

5.产品的过程设计没有考虑供应链上库存的影响，导致成本效益被库存成本抵消，引进新产品时也会遇到问题。

因此，在供应链管理环境下，需要制定合适的库存控制策略，包括建立整体观念，提高信息传递效率，加强合作与协调性，考虑库存影响的产品设计等措施，以提高供应链整体效率。

针对库存管理问题，我们推出以下策略：1.供应商管理库存策略：VMI（Vendor Managed Inventory）库存管理模式。

A multi-product multi-echelon inventory control model with joint replenishment strategy

A multi-product multi-echelon inventory control model with joint replenishment strategyWei-Qi Zhou ⇑,Long Chen,Hui-Ming GeSchool of Automobile and Trafﬁc Engineering,Jiangsu University,Zhenjiang 212013,Chinaa r t i c l e i n f o Article history:Received 23January 2011Received in revised form 11April 2012Accepted 21April 2012Available online xxxx Keywords:InventoryMulti-product Multi-echelonGenetic Algorithm (GA)Joint replenishment strategya b s t r a c tOn the basis of analyzing the shortages of present studies on multi-echelon inventory con-trol,and considering some restrictions,this paper applies the joint replenishment strategy into the inventory system and builds a multi-product multi-echelon inventory control model.Then,an algorithm designed by Genetic Algorithm (GA)is used for solving the model.Finally,we respectively simulate the model under three different ordering strate-gies.The simulation result shows that the established model and the algorithm designed by GA have obvious superiority on reducing the total cost of the multi-product multi-echelon inventory system.Moreover,it illustrates the feasibility and the effectiveness of the model and the GA method.Crown Copyright Ó2012Published by Elsevier Inc.All rights reserved.1.IntroductionA supply chain is a network of nodes cooperating to satisfy customers’demands,and the nodes are arranged in echelons.In the network,each node’s position is corresponding to its relative position in reality.The nodes are interconnected through supply–demand relationships.These nodes serve external demand which generates orders to the downstream echelon,and they are served by external supply which responds to the orders of the upstream echelon.The problem of multi-echelon inventory control has been investigated as early as the 1950s by researchers such as Arrow et al.[1]and Love [2].The main challenge in these problems is to control the inventory levels by determining the size of the orders for each echelon during each period so as to optimize a given objective function.Many researchers have studied how to reduce the inventory cost of either suppliers or distributors,or have considered either the distribution system or the production system.Burns and Sivazlian [3]investigated the dynamic response of a multi-echelon supply chain to various demands placed upon the system by a ﬁnal consumer.Van Beek [4]carried out a model in order to compare several alternatives for the way in which goods are forwarded from factory,via stores to the cus-tomers.Zijm [5]presented a framework for the planning and control of the materials ﬂow in a multi-item production system.The prime objective was to meet a presanctiﬁed customer service level at minimum overall costs.Van der Heijden [6]deter-mined a simple inventory control rule for multi-echelon distribution systems under periodic review without lot sizing.Yoo et al.[7]proposed an improved DRP method to schedule multi-echelon distribution network.Diks and Kok [8]considered a divergent multi-echelon inventory system,such as a distribution system or a production system.Andersson and Melchiors [9]considered a one warehouse several retailers’inventory system,assuming lost sales at the retailers.Huang et al.[10]0307-904X/$-see front matter Crown Copyright Ó2012Published by Elsevier Inc.All rights reserved./10.1016/j.apm.2012.04.054⇑Corresponding author.Tel.:+8651188780074;fax:+8651188791900.E-mail address:zwqsky@ (W.-Q.Zhou).2W.-Q.Zhou et al./Applied Mathematical Modelling xxx(2012)xxx–xxxconsidered a one-warehouse multi-retailer system under constant and deterministic demand,which is subjected to transpor-tation capacity for every delivery godimos and Koukoumialos[11]developed closed-form customer service models.And many researchers have modeled an inventory system of only two-echelon or two-layer.Gupta and Albright[12] modeled a two-echelon multi-indentured repairable-item inventory system.Axsäter and Zhang[13]considered a two-level inventory system with a central warehouse and a number of identical retailers.Axsäter[14]considered a two-echelon distri-bution inventory system with stochastic demand.Chen et al.[15]considered a two-level inventory system in which there are one supplier and multiple retailers.Tee and Rossetti[16]developed a simulation model to explore the model’s ability to pre-dict system performance for a two-echelon one-warehouse,multiple retailer system.Seferlis and Giannelos[17]developed a new two-layered optimization-based control approach for multi-product,multi-echelon supply chain networks.Hill et al.[18]considered a single-item,two-echelon,continuous-review inventory model.Al-Rifai and Rossetti[19]presented a two-echelon non-repairable spare parts inventory system.Mitra[20]considered a two echelon system with returns under more generalized conditions,and developed a deterministic model as well as a stochastic model under continuous review for the system.There are also many researches on multi-echelon inventory control,considering either the distribution system or the sup-ply system.Choi et al.[21]evaluated conventional lot-sizing rules in a multi-echelon coalescence MRP system.Chikán and Vastag[22]described a multi-echelon production inventory system and developed a heuristic suggestion.Bregman et al.[23]introduced a heuristic algorithm for managing inventory in a multi-echelon environment.Van der Vorst et al.[24]pre-sented a method for modeling the dynamic behavior of multi-echelon food supply chains and evaluating alternative designs of the supply chain by applying discrete-event simulation.The model considered a producer,a distribution center and2re-tailer outlets.Iida[25]studied a dynamic multi-echelon inventory problem with nonstationary u and Lau[26] applied different demand-curve functions to a simple inventory/pricing model.Routroy and Kodali[27]developed a three-echelon inventory model for single product,which consists of single manufacturer,single warehouse and single retailer. Dong and Lee[28]considered a multi-echelon serial periodic review inventory system and3echelons for numerical exam-ple.The system extended the approximation to the time correlated demand process of Clark and Scarf[29],and studied in particular for an auto-regressive demand model the impact of leadtimes and auto-correlation on the performance of the se-rial inventory system.Gumus and Guneri[30]structured an inventory management framework and deterministic/stochas-tic-neurofuzzy cost models within the context of this framework for effective multi-echelon supply chains under stochastic and fuzzy environments.Caggiano et al.[31]described and validated a practical method for computing channelﬁll rates in a multi-item,multi-echelon service parts distribution system.Yang and Lin[32]provided a serial multi-echelon integrated just-in-time(JIT)model based on uncertain delivery lead time and quality unreliability considerations.Gumus et al.[33] structured an inventory management framework and deterministic/stochastic-neuro-fuzzy cost models within the context of the framework.Then,a numerical application in a three-echelon tree-structure chain is presented to show the applicabil-ity and performance of proposed framework.The model only handled one product type.Only one other paper we are aware of addresses a problem similar to ours and consideres inventory optimization in a multi-echelon system,considering both the distribution system and the supply system.Rau et al.[34]developed a multi-echelon inventory model for a deteriorating item and to derive an optimal joint total cost from an integrated perspective among the supplier,the producer,and the buyer.The model considered the single supplier,single producer and single buyer. The basic difference between our model and Rau et al.[34]is that our model considers multiple suppliers,one producer,and multiple distributors and buyers.Additionally,an algorithm designed by Genetic Algorithm(GA)is used for solving the mod-el,and we apply the joint replenishment strategy into the model.The remainder of this paper is organized as follows:In Section2,the various assumptions are made and the multi-product multi-echelon inventory control model is developed.In Section3,GA is used for solving the model and the algorithm based on GA is designed.Then,we simulate the model under three different ordering strategies,respectively.In Section4,conclu-sions and limitations in this research are presented.2.Mathematical model2.1.The multi-product multi-echelon inventory control model descriptionIn this model,the raw materials,accessories or products can be supplied from the nodal enterprise of layer k to the nodal enterprise of layer k +1,but there is no logistics between nodal enterprises of the same layer or the non-adjacent layers.And also there is no reverse logistics from the nodal enterprise of high-layer to the nodal enterprise of low-layer.The multi-prod-uct multi-echelon inventory system is divided into three subsystems (supply network,core enterprise and distribution net-work)by the core enterprise as a dividing line (Fig.1).The key issue to the multi-product multi-echelon inventory system is to determine the optimal order quantity and the optimal order cycle for each nodal enterprise in order to minimize the inventory cost of the whole system.In this paper,the (T ,S )inventory control strategy based on multi-product joint replenishment is used.The multi-product joint replenishment strategy is an ordering strategy that to order varieties of products in one order cycle.Each nodal enter-prise determines a minimum order cycle as the basic order cycle,and the order cycle of the same enterprise to order each product is an integral multiple of the basic order cycle.2.2.Assumptions(1)In this supply chain,there is only one core enterprise.(2)Allow a variety of products,but the price of each product is ﬁxed.And also allow a variety of raw materials or acces-sories,but one supplier only provides one raw material or accessory.(3)The demand of each nodal enterprise per day is random,but it obeys Poisson distribution.(4)Lead time of each nodal enterprise is ﬁxed.(5)Storage cost per product per unit time is constant.And the storage cost of different nodal enterprises is allowed to bedifferent.2.3.Notations P w price of product w (there are W kind of products,and w =1,2,...,W )P g k Àl price of raw material or accessory provided by the nodal enterprise g of layer k Àl (g =1,2,...,m k Àl ;l =1,2,...,k À1;m k Àl is the number of nodal enterprises of layer k Àl)T h k basic order cycle of the nodal enterprise h of layer k to order products from the nodal enterprises of layer k À1T ðg ;h Þk order cycle of the nodal enterprise h of layer k to order products from the nodal enterprise g of layer k À1Z ðg ;h Þkratio of T ðg ;h Þkand T h k ,which is a positive integer,so T ðg ;h Þk¼Z ðg ;h ÞkT hkA h k public ordering cost of the nodal enterprise h of layer k to order products from the nodal enterprises of layer k À1ineach order cycle,which is independent of the order quantity and the order varietiesA ðg ;h Þkindividual ordering cost of the nodal enterprise h of layer k to order products from the nodal enterprise g of layer k À1in each order cycle,which is dependent of the order quantity and the order varietiesA ðh ;i ;w Þk þl individual ordering cost of the nodal enterprise i of layer k +l to order the product w from the nodal enterprise h of layer k +l À1in each order cycle,in the distribution networkT i k þl basic order cycle of the nodal enterprise i of layer k +l to order products from the nodal enterprises of layer k +l À1,in the distribution networkT ði ;w Þk þl order cycle of the nodal enterprise i of layer k +l to order product w from the nodal enterprises of layer k À1,in the distribution networkZ ði ;w Þk þlratio of T ði ;w Þk þl and T i k þl ,which is a positive integer,so T ði ;w Þk þl ¼Z ði ;w Þk þl T i k þlS ðg ;h Þk maximum inventory level of the nodal enterprise h of layer k to order products from the nodal enterprise g of layer k À1E D ðg ;h Þk average demand of the nodal enterprise h of layer k to order products from the nodal enterprise g of layer k À1perdayL ðg ;h Þk lead time of the nodal enterprise h of layer k to order products from the nodal enterprise g of layer k À1L ði ;w Þk þl the average lead time of the nodal enterprise i of layer k +l to order the product w from the nodal enterprise of layer k +l À1H ðg ;h Þk storage cost of the nodal enterprise h of layer k per product per yearY ðg ;h Þk quantity demand of the nodal enterprise h of layer k to order products from the nodal enterprise g of layer k À1peryear,so Y ðg ;h Þk ¼365E D ðg ;h Þkn ðg ;h Þkthe number of trips from the nodal enterprise g of layer k À1to the nodal enterprise h of layer k per year,which isinversely proportional to order cycle,so n ðg ;h Þk ¼Z ðg ;h Þk T h kÀ1W.-Q.Zhou et al./Applied Mathematical Modelling xxx (2012)xxx–xxx3f ðg ;h Þkﬁxed transportation cost from the nodal enterprise g of layer k À1to the nodal enterprise h of layer k in each trans-portation (such as driver’s wage)t ðg ;h Þkvariable transportation cost to transport the unit product from the nodal enterprise g of layer k À1to the nodal enter-prise h of layer k (such as cost of fuels),which is the function of transport efﬁciency and order quantity in the case of ﬁxed transportation distanceX ðh ;i ;w Þkthe expected value of the produce w of the nodal enterprise h of layer k relative to order quantity of the nodal enter-prise i of layer k +11ðg ;h ;w Þk conversion rate of product w produced by the nodal enterprise h of layer k relative to raw materials or accessories supplied by the nodal enterprise g of layer k À1g ðh ;i ;w Þksupply coefﬁcient of product w supplied from the nodal enterprise h of layer k to the nodal enterprise i of layer k +1,and P m k þ1i ¼1g ðh ;i ;w Þk ¼1b ðg ;h ;w Þkproportionality coefﬁcient of raw materials or accessories used to produce product w ,which are supplied from thenodal enterprise g of layer k À1to the nodal enterprise h of layer k ,and P W w ¼1b ðg ;h ;w Þk¼1B ðh ;i ;w Þkshortage penalty per produce w per order cycle from the nodal enterprise i of layer k +1to the nodal enterprise h of layer k2.4.Multi-product multi-echelon inventory control modelWe divide the inventory cost into ordering cost,holding cost,transportation cost and shortage cost.(1)Ordering costThe total ordering cost of the core enterprise per year is deﬁned as follows:C Order C¼X m kh ¼1A h kT hkþXm k À1g ¼1X m k h ¼1A ðg ;h ÞkZ ðg ;h ÞkT hk:ð1ÞThe total ordering cost of the supply network per year is deﬁned as follows:C Order S¼X k À2l ¼1X m k Àl g ¼1Ag k ÀlT g k ÀlþX k À2l ¼1X m k Àl À1f ¼1X m k Àl g ¼1A ðf ;g Þk Àl Z ðf ;g Þk Àl T g k Àl:ð2ÞThe total ordering cost of the distribution network per year is deﬁned as follows:C Order D¼X N Àk l ¼1X m k þl i ¼1Ai k þl T ik þlþX N Àk l ¼1X m k þl À1h ¼1X m k þl i ¼1X W w ¼1A ðh ;i ;w Þk þl Z ði ;w Þk þl T ik þl:ð3ÞTherefore,the total ordering cost of the multi-product multi-echelon inventory system per year is deﬁned as follows:TC Order ¼C Order C þC Order S þC Order D:ð4Þ(2)Holding costThe inventory level of the nodal enterprise h of layer k when it has received the order quantity from the nodal enter-prise of layer k À1is:S ðg ;h ÞkÀE D ðg ;h Þk L ðg ;h Þk ;ð5Þand the inventory level of the nodal enterprise h of layer k before it receives the order quantity next order cycle is:S ðg ;h ÞkÀE D ðg ;h Þk L ðg ;h Þk ÀE D ðg ;h Þk Z ðg ;h Þk T h k :ð6ÞTherefore,the average inventory level in one order cycle is:12S ðg ;h Þk ÀE D ðg ;h Þk L ðg ;h Þk þS ðg ;h Þk ÀE D ðg ;h Þk L ðg ;h Þk ÀE D ðg ;h Þk Z ðg ;h Þk T h k hi ¼S ðg ;h Þk ÀE D ðg ;h Þk L ðg ;h Þk ÀE D ðg ;h ÞkZ ðg ;h Þk T h k 2:ð7ÞThe total holding cost of the core enterprise per year is deﬁned as follows:C Hold C¼Xm k À1g ¼1X m k h ¼1S ðg ;h Þk ÀE D ðg ;h Þk L ðg ;h Þk ÀE D ðg ;h Þk Z ðg ;h Þk T h k22435H ðg ;h Þk:ð8Þ4W.-Q.Zhou et al./Applied Mathematical Modelling xxx (2012)xxx–xxxAs a practical matter,we must ensure that the average inventory level is greater than zero,as shown in Eq.(9):Sðg;hÞk ÀE Dðg;hÞkLðg;hÞkÀE Dðg;hÞkZðg;hÞkT hk2>0:ð9ÞThe total holding cost of the supply network per year is deﬁned as follows:C Hold S ¼X kÀ2l¼1Xm kÀlÀ1f¼1X m kÀlg¼1Sðf;gÞkÀlÀE Dðf;gÞkÀlLðf;gÞkÀlÀE Dðf;gÞkÀlZðf;gÞkÀlT gkÀl22435Hðf;gÞkÀl;ð10Þunder the following constraint:Sðf;gÞkÀl ÀE Dðf;gÞkÀlLðf;gÞkÀlÀE Dðf;gÞkÀlZðf;gÞkÀlT gkÀl2>0:ð11ÞThe total holding cost of the distribution network per year is deﬁned as follows:C HoldD ¼X NÀkl¼1X m kþli¼1X Ww¼1Sði;wÞkþlÀE Dði;wÞkþlLði;wÞkþlÀE Dði;wÞkþlZði;wÞkþlT ikþl22435Hði;wÞkþl;ð12Þunder the following constraint:Sði;wÞkþl ÀE Dði;wÞkþlLði;wÞkþlÀE Dði;wÞkþlZði;wÞkþlT ikþl2>0:ð13ÞTherefore,the total holding cost of the multi-product multi-echelon inventory system per year is deﬁned as follows:TC Hold¼C HoldC þC HoldSþC HoldD:ð14Þ(3)Transportation costThe total transportation cost of the core enterprise per year is deﬁned as follows:C Trans C ¼Xm kÀ1g¼1X m kh¼1nðg;hÞkfðg;hÞkþtðg;hÞkYðg;hÞkh i:ð15ÞThe total transportation cost of the supply network per year is deﬁned as follows:C Trans S ¼X kÀ2l¼1Xm kÀlÀ1f¼1X m kÀlg¼1nðf;gÞkÀlfðf;gÞkÀlþtðf;gÞkÀl Yðf;gÞkÀlh i;ð16Þwhere nðf;gÞkÀl ¼Zðf;gÞkÀlT gkÀlÀ1;Yðf;gÞkÀl¼365E Dðf;gÞkÀlThe total transportation cost of the distribution network per year is deﬁned as follows:C TransD ¼X Ww¼1X NÀkl¼1Xm kþlÀ1h¼1X m kþli¼1nði;wÞkþlfðh;iÞkþlþtðh;iÞkþl Yðh;i;wÞkþlh i;ð17Þwhere nði;wÞkþl ¼Zði;wÞkþlT ikþlÀ1;Yðh;i;wÞkþl¼365E Dðh;i;wÞkþlTherefore,the total transportation cost of the multi-product multi-echelon inventory system per year is deﬁned as follows:TC Trans¼C TransC þC TransSþC TransD:ð18Þ(4)Shortage costAssuming Xðh;i;wÞk obeys Poisson distribution p kðh;i;wÞkZðg;hÞkT hkþLðg;hÞkh iduring the period Zðg;hÞkT hkþLðg;hÞk,so:Xðh;i;wÞk ¼X1u¼AuÀgðh;i;wÞk1ðg;h;wÞkbðg;h;wÞkSðg;hÞkp kðh;i;wÞkZðg;hÞkT hkþLðg;hÞkh i:ð19ÞThe total shortage cost of the core enterprise per year is deﬁned as follows:C Shortage C ¼X m kh¼1Xm kþ1i¼1X Ww¼1Bðh;i;wÞkXðh;i;wÞkZðg;hÞkT hk:ð20ÞW.-Q.Zhou et al./Applied Mathematical Modelling xxx(2012)xxx–xxx5The total shortage cost of the supply network per year is deﬁned as follows:C Shortage S¼X k À2l ¼1X m k Àl g ¼1X m k Àl þ1h ¼1B ðg ;h Þk Àl X ðg ;h Þk ÀlZ k Àl T g k Àl;ð21ÞwhereX ðg ;h Þk Àl¼P 1u ¼Au Àgðg ;h Þk Àl 1ðf ;g Þk Àl S ðf ;g Þk Àlp k ðg ;h Þk Àl Z ðf ;g Þk Àl T g k Àl þL ðf ;g Þk Àl h i ;g ðg ;h Þk Àl ¼E D ðg ;h Þk Àl þ1ÀÁP m k Àl þ1h ¼1E Dðg ;h Þk Àl þ1ÀÁ,and P m k Àl þ1h ¼1g ðg ;h Þk Àl ¼1.The total shortage cost of the distribution network per year is deﬁned as follows:C Shortage D¼X N Àk l ¼1X m k þl i ¼1X m k þl þ1j ¼1X W w ¼1B ði ;j ;w Þk þl X ði ;j ;w Þk þl Z ði ;w Þk þl T i k þl;ð22ÞwhereX ði ;j ;w Þk þl¼X 1u ¼Au Àg ði ;j ;w Þk þl S ði ;w Þk þl p k ði ;j ;w Þk þl Z ði ;w Þk þl T i k þl þL ði ;w Þk þl h i;gði ;j ;w Þk þl¼E D ði ;j ;w Þk þlk þl þ1j ¼1E D ði ;j ;w Þk þl;andX m k þl þ1j ¼1g ði ;j ;w Þk¼1:Therefore,the total shortage cost of the multi-product multi-echelon inventory system per year is deﬁned as follows:TC Shortage ¼C Shortage C þC Shortage SþC Shortage D :ð23ÞIn conclusion,we develop the multi-product multi-echelon inventory control model as follows:minTC ¼TC Order þTC Hold þTC Trans þTC Shortage ;ð24Þs :t :E D ðg ;h ÞkL ðg ;h Þk þE D ðg ;h Þk Z ðg ;h Þk T h k 2ÀS ðg ;h Þk <0;ð25ÞE D ðf ;g Þk Àl L ðf ;g Þk Àl þE D ðf ;g Þk Àl Z ðf ;g Þk Àl T g k Àl 2ÀS ðf ;g Þk Àl <0;l ¼1;2;...;k À2;f ¼1;2;...;m k Àl À1;g ¼1;2;...;m k Àl ;ð26ÞE D ði ;w Þk þl L ði ;w Þk þl þE D ði ;w Þk þl Z ði ;w Þk þl T i k þl2ÀS ði ;w Þk þl <0;l ¼1;2;...;N Àk ;i ¼1;2;...;m k þl ;w ¼1;2;...;W ;ð27Þmin Z g ¼1;h ðÞk ;Z g ¼2;h ðÞk ;...;Z g ¼m k À1;h ðÞk h i¼1;ð28Þmin Z ðf ¼1;g Þk Àl ;Z ðf ¼2;g Þk Àl ;...;Z ðf ¼m k Àl À1;g Þk Àl h i¼1;l ¼1;2;3;...;k À2;ð29Þmin Z ði ;w ¼1Þk þl ;Z ði ;w ¼2Þk þl ;...;Z ði ;w ¼W Þk þl h i ¼1;l ¼1;2;3;...;N Àk :ð30Þ(28)–(30)can ensure that at least one product’s order cycle is the basic order cycle.The decision variables in the model are allintegers greater than or equal to zero.3.Simulation and analysis 3.1.Simulation model based on GAThe objective function of this optimization model is minimization,and the objective function of GA is maximization,so the objective function of this optimization model cannot be taken as the ﬁtness function of GA.We must convert the objec-tive function to the ﬁtness function of GA as follows:F ðX Þ¼TC max ÀTC ;TC <TC max ;0;TC P TC max ;&ð31Þwhere F (X )is the individual ﬁtness.TC max is a relatively large number,and in this simulation model,we may put TC max as the largest objective function value during evolution.The multi-product multi-echelon inventory control model can be reduced to a nonlinear programming problem as follows:6W.-Q.Zhou et al./Applied Mathematical Modelling xxx (2012)xxx–xxxmin f ðX Þ;ð32Þs :t :g i ðX Þ60ði ¼1;2;3;...;m Þ:In this paper,penalty function is used as constraint.So,we construct the penalty function as follows:/ðX ;c kÞ¼X m i ¼1c k i min g i ðX Þ;0ðÞ2;ð33Þwhere k is iteration times of GA.c k i is penalty factor,which is a monotone increasing sequence and positive value.Andc k þ1i ¼e i Ác ki .The experience in computation shows that if c k i ¼1and e i =5À10,we can achieve satisfactory results.So,we change (31)to the function as follows:F ðX Þ¼TC max ÀTC À/ðX ;c k Þ;TC <TC max ;0;TC P TC max :(ð34ÞMoreover,we use the ﬂoating point number coding (the chromosome’s length equals the number of decision variables),the roulette wheels selection mechanism as the selection operator,the arithmetic cross technique as the crossover operator,the Gauss mutation operator as the mutation operator,and algebra (its values range from 100to 500)as the termination criteria.3.2.SimulationAs an illustration,we develop a multi-product multi-echelon inventory control model which has four suppliers (the four suppliers are divided into two levels and each level has two suppliers),one core enterprise and two distributors,and has two products (Fig.2).The average demand of the customers to order product 1and product 2from the distributor 1of layer 4per day is 6units and 3units.The average demand of the customers to order product 1and product 2from the distributor 2of layer 4per day is 4units and 7units.The values of other parameters are shown in Tables 1–3.Table 2The values of the parameters of the supply network.Parameters A 12A ð1;1Þ2A ð2;1Þ2A 22A ð1;2Þ2A ð2;2Þ2L ð1;1Þ2L ð2;1Þ2L ð1;2Þ2Values $70$200$180$60$250$250666Parameters L ð2;2Þ2H ð1;1Þ2H ð2;1Þ2H ð1;2Þ2H ð2;2Þ2f ð1;1Þ2f ð2;1Þ2f ð1;2Þ2f ð2;2Þ2Values 6$3$6$3$15$200$140$250$150Parameters t ð1;1Þ2t ð2;1Þ2t ð1;2Þ2t ð2;2Þ2g ð1;1Þ2g ð2;1Þ2g ð1;2Þ2g ð2;2Þ21ð1;1Þ2Values $4$6$5$811111Parameters 1ð2;1Þ21ð1;2Þ21ð2;2Þ2B ð1;1Þ2B ð2;1Þ2B ð1;2Þ2B ð2;2Þ2Values0.510.5$150$120$160$140Table 1The values of the parameters of the core enterprise.Parameters A 13A ð1;1Þ3A ð2;1Þ3L ð1;1Þ3L ð2;1Þ3H ð1;1Þ3H ð2;1Þ3f ð1;1Þ3f ð2;1Þ3Values $100$240$32055$5$40$300$350Parameters t ð1;1Þ3t ð2;1Þ3g ð1;1;1Þ3g ð1;2;1Þ3g ð1;1;2Þ3g ð1;2;2Þ31ð1;1;1Þ31ð2;1;1Þ31ð1;1;2Þ3Values $15$100.60.40.30.70.510Parameters 1ð2;1;2Þ3b ð1;1;1Þ3b ð1;1;2Þ3b ð2;1;1Þ3b ð2;1;2Þ3B ð1;2;1Þ3B ð1;1;2Þ3B ð1;2;2Þ3B ð1;1;1Þ3Values110.50.5$150$120$180$200W.-Q.Zhouet al./Applied Mathematical Modelling xxx (2012)xxx–xxx 7。

INVENTORY CONTROL 库存控制作业全英版

STEP 3: Balance Inventory Levels
•Efficient or inefficient management of merchandise inventory by a firm is a major factor between healthy profits and operating at a loss. There are both market-related and budget-related issues that must be dealt with in terms of coming up with an ideal inventory balance:
STEP 2: Establish order cycles
• If the business experiences seasonal cycles, it is important to recognize the demands tha will be placed on suppliers as well as other sellers.
STEP 2: Establish order cycles
• If demand can be predicted for the product or if demand can be measured on a regular basis, regular ordering quantities can be setup that take into consideration the most economic relationships among the costs of preparing an order, the aggregate shipping costs, and the economic order cost. When demand is regular, it is possible to program regular ordering levels so that stock-outs will be avoided and costs will be minimized. If it is known that every so many weeks or months a certain quantity of goods will be sold at a steady pace, then replacements should be scheduled to arrive with equal regularity. Time should be spent developing a system tailored to the needs of each business. It is useful to focus on items whose costs justify such control, recognizing that in some cases control efforts may cost more the items worth. At the same time, it is also necessary to include low return items that are critical to the overall sales effort.

INVENTORYCONTROL

----------------- 我们为您所做的 … 永远是 … 最基础的 … 最关键的 … ------------------ 地址：北京经济技术开发区宏达北路10# 万源商务中心415室电话： +86 10 6786 5628 传真：+86 10 6788 26541程晓华先生Mr. John Cheng谈制造业库存问题 Inventory Control & Risks Management－库存水平的高低与资金压力问题－库存周转率的提高与现金周转－库存风险的控制－库存与“人” … 与ERP …----------------- 我们为您所做的…永远是…最基础的…最关键的… ------------------ 地址：北京经济技术开发区宏达北路10# 万源商务中心415室电话： +86 10 6786 5628 传真：+86 10 6788 2654 2作者前言本人自2004年离开制造业进入咨询行业以来，一直想把自己自2003年以来发表在e-works、《中国管理传播网》、《物流技术与应用》、《机械工业信息与网络》、《机电商报》、《国际商报》等媒体上关于“制造业库存控制”相关的文章、言论编辑成册，以奉献给广大制造业的客户与朋友。

直到现在，在朋友的帮助下，终于将其整理编印。

编辑此资料的目的关键在于：第一：感谢那些长期接受我培训、咨询的制造业客户、朋友，没有他们的帮助，是很难写出这些文章的。

第二：便于与更多的制造业朋友，特别是与那些非常关注制造业库存问题的朋友们交流。

相信我的很多观点可能是不成熟的，但考虑到自己自95年以来，一直从事这方面的工作，尤其是对于库存这个制造业的两大“死点”之一问题的研究，自己认为还是有些想法的，所以尽管有些东西不够成熟，但还是决定拿出来与大家共同探讨，以共同提高。

第三：鼓励更多的朋友加入到制造业库存控制这个课题的研究中来。

直到现在，我们很多人一提到“库存控制”，就首先想到“仓库”的管理。

物流英语unit (6)

Table 6-1 Determination of the Average Cost of a Stock-out Alternative Loss Probability Average costs 1. L oyal customers $0 0.10 $0 2. Comes back $37 0.65 $24.05 3. Lost $1,200 0.25 $300 Average cost of a stock-out 1 $324.05 Note: These are hypothetical figures for illustration.
The Tradeoff in Inventory Functions
• Costs of Holding Inventory
– Incur an opportunity cost represented by the return on investment the firm would have realized if money had been better invested – Incur warehousing costs – Cause significant tax expenses – Cause holding costs
• Opportunity Cost
– Firms engaging in speculative inventory
• Taxes
– The inventory on hand on a particular date
• Storage Costs
– Handling costs and insurance
Communication Convenience
Ine

库存的控制

订购数量(单位)
0 - 499 500 - 999 1,000 及以上
价格/单位($) $5 $4.50 $3.9
25
批量折扣模型例题解答 (Part 2)
首先，将数据以及每个订购批量区间的C值代入公式
年需求量 (D)= 10,000 units 订货成本 (S)= $20
运货成本占总成本的百分比(i)= 20% 单价 (C) = $5, $4.5, $3.9
组件、原材料等)
8
ABC 分类法
进行精确库存管理的成本是非常巨大的利用ABC分类法：决定对哪些物料运用精确且
成本高的控制措施；对哪些物料运用相对简单的控制措施。 A类：全部物料中的20%，占用每年资金额的 80%（精确的控制措施/短间隔） B类：全部物料中的30%，占用每年资金额的 15%（精确的控制措施/较长间隔） C类：全部物料中的50%，占用每年资金额的 5%（简单的控制措施/长间隔）
成本函数
TC DC D S p d QH
Q
2p
EOQ
2DS H
p
p
d
30
Fixed Order Quantity Model with Usage during Production Time
Exhibit 16.4
31
报童模型
单周期库存模型易过期产品：此类产品仅在特定时期内销售。对于这种产品的需求量具有很高的可变性，但是服从一种已知的概率分布。该类产品的残余价值或者特定时期过去之后产品的价值低于产品最初的成本价。
位购买价格的一部分(f)，因此可得H=C*f）
14
基本的定量订购模型以及再订购点行为
1. 收到一份数量为Q的定单

ACCAPAPERF2MANAG...

ACCAPAPERF2MANAG...CH6 MATERIAL COSTS1. WHAT IS INVENTORY CONTROL1.1 inventory control systemThis chapter will concentrate on a inventory control system for materials. Controls should cover the following functions.The ordering of stockThe purchase of stockThe receipt of goods into storeStorageThe issue of stock and maintenance of stock at the most appropriate level1.2 The objective of inventory controlThe overall objective of stock control is, therefore, to maintain stock levels so that the total of the following costs is minimized:Holding costsOrdering costsStockout costs1.3 Advantages and disadvantages of holding stockThe basis of the theoretical calculations of an EOQ and an optimal ROL is that there are advantages and disadvantages of holding stock （of buying stock in large or small quantities）. The advantages include:the need to meet customer demandtaking advantage of bulk discountsreducing total annual re-ordering costThe disadvantages include:storage costsinsurance costs of stock and warehouserent of warehouserates of ware housecost of capital tied up in stockdeterioration, obsolescence, and theft.The aim behind the calculations of EOQ and ROL is to weigh up these, and other advantages and disadvantages and to find a suitable compromise level.2.EOQ2.1 calculation of EOQWhen determining how much to order at a time, an organisation will recognise that:as order quantity rises, average stock rises and the total annual cost of holding stock risesas order quantity rises, the number of orders decreases and the total annualre-order costs decreaseThe total of annual holding and re-order costs first decreases, then increases. The point at which cost is minimised is the EOQ. This cost behaviour is illustrated by the graph in Figure 1.The way in which this EOQ is calculated is based on certain assumptions, including:constant purchase priceconstant demand and constant lead-timeholding-cost dependent on average stockorder costs independent of order quantityThe assumptions result in a pattern of stock that can be illustrated graphically as shown in Figure 2.The FormulaUsing the standard ACCA notation in which:CHcost of holding a unit of stock for a yearCO=cost of placing an orderD=annual demandalso:TOC=total annual re-ordering costTHC=total annual holding costx=order quantitythen:average stock=x/2THC=x/2×CHand:number of orders in a year=D/xTO C=D/x×COThe total annual cost （affected by order quantity）is:C=THC+TOC=x/2×CH+D/x×COThis formula is not supplied in exams –it needs to be understood （and remembered）.The value of x, order quantity that minimizes this total cost is the EOQ, given by an easily remembered formula:Use of EOQ FormulaYou need to take care over which figures you put into the formula, particularly in multiple-choice questions. The areas to beware of fall into two categories: Relevant costs – only include those costs affected by order quantity. Only include those holding costs which （in total in a year）will double if you order twice as much at a time. Only include those order costs which （in total in a year）will double if you order twice as often. （Thus, fixed salaries to storekeepers or buying department staff will beexcluded.）Consistent units–ensure that figures inserted have consistent units. Annual demand and cost of holding a unit for a year. Both holding costs and re-ordering costs should be in ￡, or both in pence.2.2 Bulk DiscountsA common twist to exam questions is to ask students to evaluate whether bulk discounts are worth taking. While prices reduce, total annual holding costs will increase if more stock is ordered at a time, so the matter needs a little thought. The common approach is one of trial and error. This involves finding the total annual cost （holding cost, re-ordering cost and purchasing cost）at the level indicated by the EOQ and at the level（s）where discount first becomes available.Figure 3 shows total costs （now including cost of purchasing the stock）plotted against order quantity with discount incorporated.Point A represents the cost at the order quantity indicated by the EOQ. If stock is ordered in larger quantities, total costs will increase to point B1, at which stage bulk discounts are available, bringing the costs down to point B. Any calculations will involve finding which cost out of A, B or C is the lowest, as Example 1 will show.Example 1Moore Limited uses 5,000 units of its main raw material per month. The material costs ￡4 per unit to buy, supplier’s delivery costs are ￡25 per order and internal ordering costs are ￡2 per order. Total annual holding costs are ￡1 per unit. The supplier has offered a discount of 1% if 4,000 units of the material are bought at a time.Required:a.Establish the economic order quantity （EOQ）ignoring the discount opportunities.Determine if the discount offer should be accepted.Example 1 solutionsPurchase （no discount）60000 units×4240000Holding costs （1×1800/2）900Ordering costs （60000/1800 ×27）900Total cost241800Purchase （with 1% discount）240000×99%237600Holding cost （1×4000/2）2000Ordering costs （60000/4000×27）405Total cost240005Should accept the discount offer.3.RE-ORDER LEVELSAs important as how much to order at a time is the question of when to order more stock. If an order is placed too late, when stocks have been allowed to run too low, a ‘stock-out’ will occur, resulting in either a loss of production or loss of sales, or possibly both.If orders are placed too soon, when there are still substantial supplies in stock, then stock levels and holding costs will be unnecessarily high. The re-order level as explained below should not be confused with the stock control levels referred to in textbooks. When it comes to calculating re-order levels, three sets of circumstances can be envisaged.3.1 Lead-time is zero‘Lead-time’ is the interval between placing an order with a supplier and that order arriving. It is unlikely that this could be reduced to zero –it would require astonishingly co-operative andefficient suppliers. If it were possible, a re-order level of zero could be adopted. An organisation could simply wait until it ran out of stock, click its corporate fingers, and stock would arrive instantaneously.3.2 Constant demand, fixed finite lead-timeThe assumption of constant demand is consistent with the assumptions underlying the EOQ formula. If suppliers take some time to provide goods, orders need to be placed in advance of running out. Figure 4 illustrates the problem and itssolution.If the lead-time is, say, 5 days, an order has to be placed before stocks have been exhausted. Specifically, the order should be placed when there is still sufficient stock to last 5 days, i.e:3.3 Re-order level （ROL）Demand in lead-timeSo, if lead-time for a particular stock item is 5 days and daily demand is 30 units, the re-order level would be 5 days at 30 units per day, 150 units.Variable demand in the lead-timeIf demand in lead-time varied, it could be described by means of some form of probability distribution. Taking the previous example of the demand in lead-time being 150 units, we’re considering the possibility of demand being more than 150 or less than that. See Figure 5.Note: This aspect of stock control produces a few problems. The EOQ formula requires that demand （and lead-time）for a stock item be constant. Here the possibility of demand varying or lead-time varying or both varying is introduced. Setting that problem aside, most ACCA syllabuses at the lower levels avoid any discussion of uncertainty or probability distributions. However, uncertainty in lead-time demand in stock control hasfeatured in exams.In these circumstances, a firm could place an order with a supplier when the stock fell to 150 units （the average demand in the lead-time）. However, there’s a 33% chance （0.23+0.08+0.02 0.33）that demand would exceed this re-order level, and the organisation would be left with a problem. It is therefore advisable to increase the re-order level by an amount of ‘buffer stock’ （safety stock）.3.4 Buffer stockBuffer stock is simply the amount by which ROL exceeds average demand in lead-time. It is needed when there is uncertainty in lead-time demand to reduce the chance of running out of stock and reduce the cost of such shortages.If a ROL of 160 units was adopted, this would correspond to a buffer stock of 10 units （and reduce the chance of running out of stock to 0.08+0.02 0.1, or 10%）. A ROL of 170 is equivalent to a buffer stock of 20 and reduces the chance of running out to 2%, and a ROL of 180 implies 30 units of buffer stock （and no chance of running short）.3.5 Optimal Re-order LevelsThis leaves the problem of how to calculate the optimal ROL. There are two common ways in which one could determine a suitable re-order level （if theinformation was available）:A tabular approach –Calculate, for each possible ROL （each level of buffer stock）the cost of holding different levels of buffer stock and the cost incurred if the buffer is inadequate （‘stock-out’ costs）. The optimal re-order level is that level at which the total of holding and stock-out costs are a minimum.A ‘service level’ approach –An organization has todetermine a suitable level of service （an acceptably small probability that it would run out of stock）, and would need to know the nature of the probability distribution for lead-time demand. These two would be used to find a suitable ROL.（1）Reorder levelWhen stocks reach this level, an order should be placed to replenish stocks. The reorder level is determined by consideration of the following:The maximum rate of consumptionThe maximum lead time （the maximum lead time is the time between placing an order with a supplier, and the stock becoming available for use）Reorder level maximum usage X maximum lead time（2）Minimum levelThis is a warning level to draw management attention to the fact that stocks are approaching a dangerously low level and hat stockouts are possible.Minimum level=reorder level–（average usage×average lead time）（3）Maximum levelThis also acts as a warning level to signal to management that stocks are reaching a potentially wasteful level.Maximum level reorder level+reorder quantity –（minimum usage X minimum lead time）（4）Reorder quantityThis is the quantity of stock which is to be ordered when stock reaches the reorder level. If it is set so as to minimize the total costs associated with holding and ordering stock, then it is known as the economic order quantity.（5）Average stockThe formula for the average stock level assumes that stock levels fluctuate evenly between the minimum （or safety）stock level and the highest possible stock level （the amount of stock immediately after an order is received, i.e. safety stock+reorder quantity）.Average stock safety stock+1/2 reorder quantity4.THE STORAGE OF RAW MATERIALS4.1 Periodic stocktakingPeriodic stocktakin is a ‘process whereby all stock items are physically counted and valued at a set point in time, usually at the end of an accounting period.Continuous stocktaking is ‘the process of counting and valuing selected items at different times on a rotating basis’. This involves a specialist team counting and checking a number of stock items each day, so that each item is checked at least once a year. Valuable items or items with a high turnover could be checked morefrequently.4.2 Perpetual inventory systemA perpetual inventory system involves recording every receipt and issue of stock as it occurs on bin cards and stores ledger accounts.Section III Stock valuationFIFO （First In, First Out）LIFO （Last In, First Out）FIFO assumes that materials are issued out of stock in the order in which they were deliver ed into stock:issues are priced at the cost of the earliest deliveiy remaining in stock.Using FIFO,the cost of issues and the closing stock value in the example would be as follows.Date ofissue Quantity issued ValueUnits￡￡4May200100 o/s at￡2200100 at ￡2.10 210410 11May400300 at￡2.10630100at￡2.12212842 20May100100at￡2.12212Cost of issues 1,464Closing stock value 200100at￡2.12212100at￡2.402404521,916*5.CUMULATIVE WEIGHTED AVERAGE PRICINGLIFO assumes that materials are issued out of stock in the reverse order to which they were delivered:the ost recent deliveries are issued before earlier ones,and are priced accordingly.Using LIFO,the cost of issues and the closing stock value in the example above would be as follows.Date of issue Quantity issued ValuationUnits￡￡4May200 200at￡2.1042011May400300at￡2.12636100at￡2.10210846 20May100100 at￡2.40240Cost of issues1,506Closing stock value200100at￡2.002004101,916 The cumulative weighted average pricing method calculates a weighted average price for all units in stock. Issues are priced at this average cost,and the balance of stock remaining would have the same unit valuation. The average price isdetermined by dividing the total cost by the total number of units.A new weighted average price is calculated whenever a new delivery of materials into store is received. This is the key feature of cumulative weighted average pricing.In our example,issue costs and closing stock values would be as follows.Date Received Issued Balance stock value Unit costUnits Units Units￡￡￡Opening stock100 200 2.003May400840 2.10*5001,040 2.084May200（416） 2.08416300624 2.089May300636 2.12*6001,260 2.1011May400（840） 2.10840200420 2.1018May100240 2.40*300660 2.2020May100（220） 2.20220Closing stock value200440 2.204401，916 ProfitabilityFIFO > weighted average > LIFODifferent stock valuation methods produced different costs of sale and hence different profits. As opening stock values and purchase costs are the same for each method, the different costs of sale are due to different closing stock valuations. The differences in profits therefore equal the differences in closing stock valuations.The profit differences are only temporary.。

存货的相关英语表达

存货的相关英语表达Inventories.Inventories are assets that are held for sale in the ordinary course of business or in the process of production for such sale. The term "inventory" encompasses a wide range of items, including raw materials, work in progress, finished goods, and supplies. Inventories are an essential part of many businesses, as they allow companies to meet customer demand and generate revenue.Types of Inventories.There are three main types of inventories:Raw materials are materials that have not yet been used in the production process. Examples of raw materials include lumber, steel, and fabric.Work in progress is inventory that is partiallycomplete but not yet ready for sale. Examples of work in progress include unfinished products, subassemblies, and components.Finished goods are inventory that is complete and ready for sale. Examples of finished goods include automobiles, computers, and clothing.Inventory Management.Inventory management is the process of planning, organizing, and controlling inventories. The goal of inventory management is to ensure that a company has the right amount of inventory on hand to meet customer demand without incurring excessive costs.There are a number of different inventory management techniques, including:Just-in-time (JIT) inventory management is a technique that involves producing inventory only when it is needed. JIT inventory management can help companies reduce theirinventory costs and improve their efficiency.Economic order quantity (EOQ) is a technique that helps companies determine the optimal quantity of inventory to order. EOQ can help companies minimize their inventory costs.Safety stock is a buffer of inventory that is held to protect against unexpected increases in demand or disruptions in the supply chain. Safety stock can help companies avoid stockouts and lost sales.Inventory Costs.There are a number of different costs associated with inventory, including:Ordering costs are the costs of placing an order for inventory. Ordering costs include the cost of preparing the order, the cost of shipping the order, and the cost of receiving the order.Holding costs are the costs of storing inventory. Holding costs include the cost of rent, utilities, and insurance.Shortage costs are the costs incurred when a company does not have enough inventory to meet customer demand. Shortage costs include the cost of lost sales, the cost of backorders, and the cost of expedited shipping.Inventory Valuation.Inventory is valued on a company's balance sheet at the lower of cost or market. The cost of inventory is the costof acquiring the inventory, including the cost of materials, labor, and overhead. The market value of inventory is the price at which the inventory could be sold.There are a number of different methods for valuing inventory, including:First-in, first-out (FIFO) is a method of valuing inventory that assumes that the oldest inventory is soldfirst. FIFO can result in higher inventory values during periods of rising prices.Last-in, first-out (LIFO) is a method of valuing inventory that assumes that the newest inventory is sold first. LIFO can result in lower inventory values during periods of rising prices.Weighted average cost is a method of valuing inventory that uses the average cost of all inventory on hand. Weighted average cost can result in more stable inventory values over time.Inventory Turnover.Inventory turnover is a measure of how quickly a company is selling its inventory. Inventory turnover is calculated by dividing the cost of goods sold by the average inventory balance. A high inventory turnover rate indicates that a company is selling its inventory quickly and efficiently. A low inventory turnover rate indicates that a company is holding on to its inventory for too long.Inventory Control.Inventory control is the process of tracking and managing inventory levels. Inventory control systems can help companies avoid stockouts, reduce inventory costs, and improve their efficiency.There are a number of different inventory control techniques, including:Periodic inventory is a technique that involves counting inventory on a regular basis. Periodic inventory can help companies track inventory levels and identify any discrepancies.Perpetual inventory is a technique that involves tracking inventory levels in real time. Perpetual inventory can help companies avoid stockouts and improve their efficiency.Cycle counting is a technique that involves countinginventory on a regular basis, but only a portion of the inventory is counted each time. Cycle counting can help companies identify any discrepancies in inventory levels and improve their accuracy.Inventory Management Software.Inventory management software can help companies track and manage their inventory levels. Inventory management software can help companies automate inventory processes, reduce inventory costs, and improve their efficiency.There are a number of different inventory management software solutions available, including both on-premise and cloud-based solutions. The best inventory management software solution for a particular company will depend on its specific needs and requirements.。

存货管理英文小作文

存货管理英文小作文英文：Inventory management is a crucial aspect of running a business. As someone who has experience in managing inventory, I can say that there are a few key things to keep in mind. Firstly, it's important to have a system in place for tracking inventory levels. This can be done manually or through software, but it's important to have a way to know when it's time to reorder items.Another important aspect of inventory management is forecasting. By analyzing sales data and trends, you can predict which items will sell well and which may not. This can help you make informed decisions about how much inventory to keep on hand.It's also important to regularly conduct physical inventory counts to ensure that your records match what's actually in stock. This can help prevent overstocking orunderstocking, which can both be detrimental to a business.Finally, it's important to have a plan in place for dealing with slow-moving or obsolete inventory. This could involve discounting the items to move them out of stock or finding alternative uses for them.Overall, effective inventory management requires attention to detail and the ability to adapt to changing circumstances. By staying on top of inventory levels and making informed decisions, businesses can ensure that they have the products they need to meet customer demand.中文：存货管理是经营企业的关键方面。

英语作文-图书批发行业的生产效率研究

英语作文-图书批发行业的生产效率研究The book wholesale industry is a critical component of the global publishing ecosystem, serving as a bridge between publishers and retailers, and ultimately, readers. The efficiency of production within this sector is paramount to ensuring that books are distributed in a timely and cost-effective manner. This essay delves into the various aspects of production efficiency in the book wholesale industry, exploring the methods and technologies that have revolutionized this field.Efficiency in the Book Wholesale Industry。

Efficiency in the book wholesale industry is measured by how quickly and economically books can be distributed from publishers to retailers. This involves several key processes, including inventory management, order processing, logistics, and customer service. Each of these components must work seamlessly to ensure that books reach their destinations without delay or excessive cost.Inventory Management。

助理物流师辅导物流英语常见语句：库存控制

助理物流师辅导物流英语常见语句：库存控制助理物流师辅导物流英语常见语句：库存控制导语：库存控制是对制造业或服务业生产、经营全过程的各种物品、产成品以及其他资源进行管理和控制，使其储备保持在经济合理的水平上。

在助理物流师考试中常见的英语语句有哪些呢?大家一起来看看吧。

1. Inventory control is the method to keep the best inventory level and position with the minimum cost to satisfy the demand.库存控制是保持最佳库存水平和位置的方法，以最低成本满足需求。

2. When the inventory is reduced to a specific level, purchase for new parts and material will start. It is called the Order Point System.当库存减少到一个特定水平，新零部件和原材料采购将启动。

这就是所谓的订货点制度。

3. Zero stock is means zero inventory.零库存是指零存货。

4. Inspection is the operation to check the quantity, quality and package of the goods according to the contract and specific standards.检验是按合同和具体标准，检查货物的数量、质量和包装。

5. Goods that are stored in warehouses for distribution and sales are called inventory.存放在仓库待配送和销售的货物被称为库存。

6. Warehouse rental represent a very significant proportion of total warehouse cost.仓库租金占总仓储成本的一个非常重要的.比例。

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