集成的财务报表分析在供应链网绲挠呕杓圃谛枨蟮牟.doc

外文翻译学 院 宋体四号、加粗 专 业 宋体四号、加粗 届 别 宋体四号、加粗 学 号 宋体四号、加粗 姓 名 宋体四号、加粗 指导教师 宋体四号、加粗 200 年 月 日Integration of financial statement analysis in the optimal design of supply chain networks under demand uncertainty Pantelis Longinidis,Michael C. Georgiadis.Integration of financial statement analysis in the optimal design of supply chain networks under demand uncertaintyJ.International Journal of Production Economics,Volume 129, Issue 2,February 2011,Pages 262276 AbstractModels that aim to optimize the design of supply chain networks have become a mainstream in the supply chain literature. This paper aims to fill a gap in the literature by introducing a mathematical model that integrates financial considerations with supply chain design decisions under demand uncertainty. The proposed Mixed-Integer Linear Programming (MILP) problem enchases financial statement analysis through financial ratios and demand uncertainty through scenario analysis. The applicability of the model is illustrated by using a case study along with a sensitivity analysis on financial parameters expressing the business environment. The model could be used as an effective and convenient strategic decision tool by supply chain managers.KeywordsSupply chain optimization; Financial statements; Demand uncertainty; Distribution networks1. IntroductionDuring the last decade of the 20th century rapid changes occurred in the business environment. Competition among companies in all their operational functions， from raw material sourcing to customer service， has dramatically increased. Companies have extended their strategic focus in the global market. Cost and price benefits had been scattered across various countries and regions of the world and had pushed companies to seize upon these opportunities. Hence， companies were forced to manage their operations over the limited “unique enterprise” framework. Orientation to external environment is a medium that enables companies to obtain the necessary sources and abilities (Spekman et al.， 1998). These developments have driven to the evolution of “supply chain management” (SCM) as companies have realized that they cannot operate individually anymore， but only as parts of a complicated business operations chain (Tan et al.， 1998).Organizations， which constitute a supply chain network (SCN)， interact through continuous and two-sided connections that create added value in products (Mentzer et al.， 2001). These networks have an undefined number of echelons and stages while their main operations involve purchasing of raw materials from suppliers， production， transportation and storage of products， inventory management， and distribution of products to customers (Simchi-Levi et al.， 2000).Part of the planning process in SCM aims at finding the best possible supply chain configuration. These decisions are considered strategic because of their long time horizon and are tackled with facility location models. However， by considering certain aspects of the supply chain environment， these models are adequately capable to support the Supply Chain Network Design (SCND) phase (Melo et al.， 2009). Moreover， dynamic facility location models， where the decisions are spread out over a long-term planning horizon and the decision variables are time-dependent， are becoming more compatible to track the dynamics of complex supply chains (Thanh et al.， 2008).Since companies recognized the potential competitive advantages， gained through a holistic management of their supply chain， the academic community has been developing several models that describe their design and operation. These models support management staff in both strategic and tactical decisions regarding management of supply and distribution networks. Although numerous successful models have been developed for the design and operation of supply chains， their vast majority ignores decisions involving revenues， marketing campaigns， hedging against uncertainties， investment planning， and other corporate financial decisions (Shapiro， 2004). Financial factors are among the issues that have a strong impact on the configuration of global supply chains (Melo et al.， 2009). Financial globalization factors such as corporate income taxes， transfer prices， currency exchange rates， are some of the key components that a supply chain design model in the delocalization context should take into account (Hammami et al.， 2008). Integration of financial aspects in these models allows for the systematic assessment of the impact of production decisions in the financial operation and further selects their ideal combination thus providing a competitive advantage in the company (Guilln et al.， 2006). Inclusion of financial considerations in supply chain models is particularly advised for capital intensive activities (continuous processes， heavy industrial equipments， etc.).Financial operations are supplementary to production operations. They are essential and important activities because they ensure financing of production and distribution operations. Moreover， financing is necessary for investments in new production processes， in new production equipment， in new innovative products， and for expansion in new markets. The basic sources of financing are loans from financial institutions and funds from increases in equity stocks， with or without initial public offering (IPO). In order to attract capitals from these two investment groups， companies should have an unambiguously and satisfactory financial status. The evaluation of a companys investing prospect and credit standing is a process based on static and comparative analysis of financial statements (Horrigan， 1966 and Rushinek and Rushinek， 1987). In a similar vein， financial statement analysis enables financial institutions to benchmark companies in the same industry with relative measures instead of absolute measures (Cowen and Hoffer， 1982).Another important issue in SCM is uncertainty. Demand uncertainty has been early recognized in the SCM context as the essential cause of the “bullwhip effect”， which is characterized by excess volatility in demand (Davis， 1993). Uncertainty of customer demand is rarely a consideration in models for supply chain network design (Ballou， 2001) and especially for global logistics systems (Vidal and Goetschalckx， 1997). Systematic consideration of uncertainty can facilitate calculation of expected return and evaluation of associated risks based on current status and future predictions (Papageorgiou， 2009).This work first and foremost aims to enrich the literature in supply chain network design using mathematical programming techniques. Financial statement analysis and transient demand uncertainty are the salient features of the proposed approach.The rest of the paper is structured as follows. Section 2 reviews the literature in the field of financial aspects and demand uncertainty within supply chain modeling while Section 3 introduces the supply chain network design problem and presents the mathematical formulation. The applicability of the proposed model is illustrated through a case study in Section 4 and in Section 5 some computational experiments evaluate the model in larger testbeds. Concluding remarks are drawn in the last section along with further research directions.2. Literature review2.1. SCM models with financial aspectsDespite the fact that many researchers have mentioned the importance of financial considerations in the SCM context (Shapiro, 2004, Hammami et al., 2008, Melo et al., 2009 and Papageorgiou, 2009) very few research contributions can be found in the literature. SCM models with financial aspects could be divided into two groups. Those where financial aspects are considered as endogenous variables which model the financial operation and are optimized along with the other SCND variables and those where financial aspects are considered as known parameters used in constraints and in the objective function.Regarding the first group, the work of Puigjaner and co-workers represents a key contribution in the area.Romero et al. (2003) build a deterministic multi-period mathematical model for the batch chemical process industry (CPI) that combined scheduling and planning with cash flow and budget management. In the same vein, Badell et al. (2004) proposed an unequal multi-period deterministic Mixed-Integer Linear Programming (MILP) model for the batch process industries that integrates advanced planning and scheduling at plant level with cash flow and budgeting. Guilln et al., 2006 and Guilln et al., 2007 introduced a deterministic MILP model, for a multiproduct, multi-echelon chemical supply chain, which optimizes planning/scheduling and cash flow/budgeting decisions simultaneously. The model is multi-period and its objective function is the change in companys equity, a novel feature against previous models. Recently, Puigjaner and Guilln (2008)developed a holistic agent-based system that was able to use a number of different tools such as if-then analysis rules and mathematical programming algorithms in order to capture all processes in a batch chemical supply chain. A budgeting model was among these features and its connection to the agent-based system was made through payments of raw materials, production and transport utilities, and the sale of products. Yi and Reklaitis (2004) presented a two level parametric optimization model at plant level for the optimal design of batch storage networks that integrated production decisions with financial transactions through cash flow assignment in each production activity. Lanez et al. (2007) proposed a deterministic MILP model for the optimal design of a chemical supply chain based on holistic models that covered both the process operations and the finances of the company and aimed at maximizing the corporate value of the firm.集成的财务报表分析在供应链网络的优化设计在需求的不确定性摘要旨在优化设计的供应链网络的模型已经成为一个供应链网络的主流。本文旨在通过引入集成了金融的考虑与供应链设计下的决策需求的不确定性的数学模型来填补这一空白的文献。提出的混合整数线性规划(MILP)问题通过财务比率和情景分析需求的不确定性来进行财务报表分析。通过使用一个案例研究以及灵敏度分析在金融参数表达的商业环境很好地说明了模型的适用性。该模型可以作为一个有效的和方便的战略决策工具被供应链经理来使用。关键词：供应链优化；财务报表；需求的不确定性；分销网络1.介绍在20世纪的最后10年里商业环境发生了快速的变化。企业之间的竞争在他们所有的领域里，从原材料采购到客户服务都大幅增加了。公司已经开始扩展他们在全球市场的战略重点。成本和价格利益被分散在不同国家和世界的不同地区，这推动着公司去抓住这些机会。因此，公司被迫在有限的“独特的企业”框架内管理他们的业务。定向到外部环境的是一个媒介，使得公司获得必要的资源和技能( Spekman et al.,1998 )。这些进展驱动了“供应链管理”(SCM)的演变，公司意识到他们不能单独操作了，但只是仅限于部分复杂的业务操作链( Tan et al.,1998 )。组织，通过连续和双边关系互动构成一个供应链网络(SCN)，在产品上创造附加价值( Mentzer et al，2001 )。这些网络有一个未定义的数量的阶层和阶段，而他们的主要业务涉及采购的原料供应商，生产、运输和存储产品、库存管理和分布的产品给客户( Simchi-Levi et al.,2000 )。在SCM中，规划过程的一部分是旨在找到最好的供应链配置。这些决定由于具备长远的眼光和解决与设施选址模型从而被视为战略。然而，考虑到某些方面的供应链环境下，这些模型能够充分支持供应链网络设计(SCND)阶段(Melo et al.,2009 )。此外，动态设施选址模型，决策是分散在一个长期的规划周期，决策变量是时间依赖，变得更兼容跟踪动态复杂的供应链( Thanh et al.,2008 )。因为公司通过他们的供应链整体管理认识到了潜在的竞争优势，学术界一直在开发几个模型，描述他们的设计和操作。这些模型支持管理人员在这两个战略和战术决策管理的供应和分销网络。虽然许多被用来设计及运行供应链的模型已经成功开发，但他们绝大多数都忽视了决策涉及收入、营销活动、不确定性、投资规划对冲，和其他企业的财务决策( Shapiro，2004 )。金融是对全球供应链的配置有很大影响的因素之一( Melo et al.,2009 )。金融全球化因素，诸如企业所得税、转让价格、汇率，是一些关键的组件，一个供应链设计模型应当在非局部化考虑( Hammami et al.,2008 )。在这些模型中，金融方面的集成允许系统影响的评价生产决策在金融运行和进一步选择理想的组合从而为公司提供一个竞争优势( Guillen et al.,2006 )。尤其建议为资本密集型活动提供一个包含金融因素在内的供应链模型(连续生产，重型工业设备等)。金融操作补充生产操作。它们是必要的和重要的活动，因为他们保证生产和分销业务融资。此外，融资对于投资新的生产流程是必要，包括在新的生产设备，在新的创新的产品，在新市场的扩张。最基本的资金来源是向金融机构贷款和基金增加股本的股票，有或没有首次公开发行(IPO)。为了从这两个投资集团吸引资本，公司应该有一个明确和令人满意的财务状况。评估一个公司的投资前景和信誉是一个基于静态和比较分析财务报表的过程( Horrigan, 1966 and Rushinek and Rushinek, 1987)。同样，财务报表分析使金融机构基准公司在同一行业相对措施，而不是绝对的措施( Cowen and Hoffer, 1982 )。另一个重要的问题是不确定的供应链。因为“牛鞭效应”，在SCM环境中，需求的不确定性是早期已经公认的，它的特点是需求的过度波动( Davis, 1993 )。在对供应链网络的模型的设计中，客户需求的不确定性