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农产品冷链物流发展现状及对策研究,农产品,物流,发展,现状,对策,研究
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河北科技师范学院 本科毕业论文(设计)中期检查表 题 目农产品冷链物流发展现状及对策研究学生姓名宋国栋学 号9116100407专 业物流管理指导教师李丽杰职 称讲师主要研究内容及进展主要研究内容：首先介绍了冷链物流和农产品冷链物流的理论概念，总结了我国农产品冷链物流的发展现状,指出了农产品冷链物流存在问题，对问题进行了分析并给出了解决对策，最后以我国广东省荔枝冷链物流为案例探讨发展我国农产品冷链物流的问题和解决对策。课题进展情况：按照任务书的要求，已完成文献综述、外文翻译、开题报告、论文初稿，经审核和修改，论文的基本结构和内容已经确定。尚须完成的任务论文的格式需要修改，内容和细节还需要进一步完善；案例分析的不够彻底。存在的主要问题及解决措施问题：格式不够准确，案例分析部分不够详细，创新之处不够明显。解决措施：查阅相关资料，咨询导师，改善论文结构。指导教师审查意见 指导教师签名：年 月 日院（系、部）审查意见 签字盖章：年 月 日专业： 物流管理 学号： 9116100407 Hebei Normal University of Science & Technology本科毕业论文（设计）（人文科学）题 目：农产品冷链物流发展现状及对策研究 院（系、部）： 商务管理系 学 生 姓 名： 宋国栋 指 导 教 师： 李丽杰 职 称 讲师 2014年5月15日河北科技师范学院教务处制 河北科技师范学院2014届本科毕业论文资料目录 1.学术声明11页2.河北科技师范学院本科毕业论文113页3.河北科技师范学院本科毕业论文任务书11页4.河北科技师范学院本科毕业论文开题报告12页5.河北科技师范学院本科毕业论文中期检查表11页6.河北科技师范学院本科毕业论文答辩记录表11页7.河北科技师范学院本科毕业论文成绩评定汇总表12页8.河北科技师范学院本科毕业论文工作总结11页9.其他反映研究成果的资料（如公开发表的论文复印件、效益证明等） 页 河北科技师范学院本科毕业论文（设计）任务书农产品冷链物流发展现状及对策研究院（系、部）名 称 ： 商务管理系 专 业 名 称： 物流管理 学 生 姓 名： 宋国栋 学 生 学 号： 9116100407 指 导 教 师： 李丽杰 2013年11月30日河北科技师范学院教务处制 一、主要研究内容1. 冷链物流的相关理论；2. 冷链物流的国内发展现状；3. 冷链物流的国外发展现状；4. 我国发展冷链物流的对策研究；二、基本要求1. 根据论文选题方向，广泛查阅国内外相关文献，按时完成外文资料翻译和文献综述；2. 在充分理解该课题主要内涵和技术要素的基础上确定撰写结构和主要内容，写出开题报告；3. 毕业论文做到理论联系实际，利用专业知识分析问题，解决问题，达到论点明确，论据充分，结构严谨，层次清晰，具有理论价值、使用价值；4. 外文翻译、文献综述、开题报告、毕业论文等论文文档的写作规范必须符合河北科技师范学院本科毕业论文(设计)工作条例和欧美学院的相关要求。三、工作进度1、2013年12月15日，完成外文资料翻译及查阅资料；2、2013年12月30日，完成论文文献综述；3、2013-2014第一学期第19-20周，进行开题答辩。4、2013-2014第二学期第2周，完成毕业论文初稿；5、2013-2014第二学期第8周，完成论文，准备答辩；6、2013-2014第二学期第9-10周，毕业论文答辩。四、参考文献1 毋庆刚.我国冷链物流发展现状与对策研究J.中国流通经济,2011(02):24-28.2 金盛楠.冷链物流分析及其在食品中的应用现状J.现代食品科技, 2008(10):1031-1035.3 赵松岭.探讨冷链物流企业发展之路J.合作经济与科技,2012(09):57-58.4 Dr.K C Chaudhuri.Surveillance cold chain system em during intensified pulse polio programme-2006 in Chandigarh J.co-published by Springer India,20075 Tinne Van Looy, Eric Mathijs,Eric Tollens Underutilized agro forestry food products in Amazonas a market chain analysisJ.Springer Science-business Media B.V, 2008指导教师签名： 教学部主任审查签名： 河北科技师范学院本科毕业论文（设计）外文翻译商业智能在打造无缝冷链物流中的应用院（系、部）名 称 ： 商务管理系 流管理 11111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111专 业 名 称： 物流管理 学 生 姓 名： 宋国栋 学 生 学 号： 9116100407 指 导 教 师： 李丽杰 2013年12月15日河北科技师范学院教务处制河北科技师范学院2014届本科毕业论文外文翻译Applications of business intelligence in making cold chains seamlessAzimuddin KhanAbstractDue to intense competition, it has become inevitable for business organisations world over to become efficient and cost effective. Seamless cold chain infrastructure is critical for the success of cold chain business. Concurrent growth in demand for products such as fresh agricultural produce, frozen food, photographic films, chemicals and pharmaceutical drugs has lead to the necessity of managing cold chains intelligently. The managers of cold chains at strategic and functional level need actionable information for making their organisations agile. In the recent times, the concept of business intelligence has gained momentum and found application in diverse areas. In this paper, authors have made an effort to develop a framework of BI system for cold chains and highlighted the application areas of business intelligence for cold chains including a case study of cold chain business. Keywords: cold chains; business intelligence; BI; analytics; supply chain; 1. Introduction India falls short of 10 million tons cold storage facilities for storing agriculture-based produce, it has nearly 21.7 million tons of such storage facilities against required 31 million tons, and as a result 40% of produce is lost in fields after post-harvesting (KPMG-ASSOCHAM, 2009). India is a populous country and resource utilisation is always a major concern, to meet the demand. The demand for perishable food stuff, i.e., farm produce like fruits, vegetables and milk products, meat, fish, frozen food, photographic films, chemicals, and pharmaceutical drugs is increasing world over. This has also increased concerns regarding food hygiene and safety during storage and transportation to end point. The cold chains are providing integration of farm, processing, warehousing, distribution and retail business. Discrepancies in cold chain management can render products unfit for use so much so that consumption of these can even pose a threat to life. According to Bourlakis and Weightman (2004),the cold chains are vital. Effective management of cold chains is crucial for maintaining the safety, sanctity of food, and profitability of business.Technology finds significant application in facilitating processes related to warehouse, inventory, transportation, and distribution. These processes generate huge volumes of data. Such large volumes of data need to be analysed, streamlined and channelised in an intelligent manner to make organisation agile (Khan and Saxena, 2010). The sensible extract of information aids in prudent decision-making and contributes to value proposition for business by making them more competitive with proper utilisation of business intelligence (BI). In this backdrop, an attempt has been made to understand the concept of BI with reference to cold chains, development of framework for implementation of BI solution, identifying the application areas of BI for cold chains. To date, there have been few academic studies on the use of BI solutionsin cold chains, yet, ractitioners and researchers are interested in understanding the concept for improving performance and profits using BI. An attempt has also been made to understand the cold chain business through a case study of cold chain company.2. Concept of BI Technology has empowered cold chain business to access huge volume of data, which plays a critical role in planning and controlling of cold chain activities like warehousing management, transportation, distribution operation, freight management and of course the compliance management. In the year 1989, Howard Dresner, a research scholar at Garter Group (an IT research and advisory firm in Connecticut) popularised the term BI with a set of methods and concepts to improve business decision-making by using data resources. Simon and Shaffer (2001) stated that the period from 1990 to 1999 was a remarkable decade in which many core computing and communication technologies and developments from the prior decade came together and transformed the method of business. In 1990s, technology led to the birth and widespread acceptance of applications such as enterprise resource planning (ERP) and customer relationship management (CRM). Due to failure of distributed database management system, organisations pursued data warehousing, where data would be consolidated from many distributed and heterogeneous stores of data. Organisations that built and deployed data warehouses typically focused their usage on the informational/analytical side to generate reports, analyse trends, and so on. McDonald et al. (2002) stated that “once the data warehouse has been constructed, the stage is set for effective business intelligence”. A data warehouse provides the support infrastructure for BI. BI is built on the foundation of data warehouse. Kalakota and Robinson (2001) define BI “as a group of applications that enable both the active and passive delivery of information or rather turn raw data into actionable intelligence. Data and information are collected from large databases to answer to mission-critical questions asked by its managers”. Gangadharan and Swami (2004) stated that BI is the result of in-depth analysis of detailed business data, including database and application technologies, as well as analysis practice. According to Moss and Hoberman (2004), the processes, technologies, and tools needed to turn data into information, information into knowledge and knowledge into plans, drive profitable business action. BI encompasses data warehousing, business analytics tools and content/knowledge management. Turban et al. (2007) infersthat “organizations are being compelled to capture, understand and harness their data to support decision making in order to improve business operations. Business cycle times are now extremely compressed; faster, more informed, and better decision-making is therefore a competitive imperative”. BI starts with day to day information that organisations need to run the business and assist to take correct decision based on facts at right time and at right place through out the life of business by doing analytics. This is a modern mantra for modern approaches to BI. As per Cody et al. (2002), BI and knowledge management technologies have been used in improving the quantitative and qualitative value of the knowledge available to decision-makers. BI has applied the functionality, scalability, and reliability of modern database management systems to build ever-larger data warehouses, and to utilise BI tools to extract business analytics from the vast amount of available enterprise data. BI systems facilitate the decision-makers to correct their intuition by taking advantage of analytical tools, which can test and verify intuition before applying it to the decision-making process. Decision-maker can also use predictive models to improve their decision-making. The current state of decision-making is forcing companies, to reap the real benefits of BI. BI solution can turn dynamic, detail data into information, and make it available in real-time to the decision-makers. Actionable information must be accessible on-demand when it is required. It provides trends and patterns that might otherwise go undetected and unseen by decision-makers.3. Framework for implementation of BI in cold chains The BI can be built with the use of technology through BI system, refining the processes and BI tools for analytics. BI can be applied at all three levels in the cold chains, i.e., strategic, tactical and operational. A framework has been proposed in Figure 1. The framework includes four basic components which are existing IT setup, Transformation tools, data warehousing and various BI tools for analytics.3.1 Existing IT setup for data collection In an organisation, online transaction processing system and other enterprise application generate huge volume of data. These data are stored in databases. These databases along with application software, present the business information to the business user through IT infrastructure including PC, Notebook, Tablet, Smart Phone and networks. All the applications for running cold chain business including warehouse management, logistic management, network planning, RFID tracking and monitoring, CRM, inventory management, quality assurance, HR application, order management and data management generate huge volume of data with different database at different location. There are certain external information, and data about the financial and market information which are taken from research organisations, government, regulatory bodies and companys websites, audio, video, spatial and supplier data. Data can also come from e-mail, voice application, images, spatial data taken from satellite and regulatory compliance (Hazard Analysis and Critical Control Points (HACCP)/ISO 22000 norms) (Keener, 2007). Cold chain business has to heavily rely on radio frequency identification (RFID) tags attached to items, cases or pallets, monitor and log the environment temperature at predefined intervals duringtransportation or product lifecycle. The recorded data can be read and analysedin real time for better analytics. 3.2 Data transformation tools The objective of this stage is to define and design data management strategy to ensure that organisation has right information and uses it properly. The greatest challenge is to collect the clean data, that too from various sources so that BI solution delivers the correct actionable information to management at different levels. The organisation should concentrate on quality of data, and investment must be made to ensure high levels of data quality. The duplicate data should be unified as it comes from various sources. The data coming from transaction system is atomic level data and should be recorded in detailed form. It is necessary to first clean and validate it using business rules through data cleansing tools. Transformation procedure defines business logic which maps data from its source to destination. Extract, transfer and load (ETL) tools are very helpful to reduce the development time, manage the flow of data from source to destination and upload data to tables of data warehouse. 3.3 Data warehousing and data mart Inmon (1995) defined a “data warehouse as a centralized repository (collection of resources that can be accessed to retrieve information) of an organizations electronically stored data, designed to facilitate reporting and analysis”. Kimball and Ross (2002) have given another approach where data marts are first created to provide reporting and analytical capabilities for specific business processes. Data marts contain, primarily, dimensions and facts. Facts cancontain either atomic data and, if necessary, summarised data. The single data mart can be build for specific business area such as sales or production. These data marts can eventually beintegrated to create a comprehensive data warehouse. The data warehouse is a play ground for analytics and it provides retrieval of data without slowing down operational systems.3.4 BI tools for analytics There are many categories of tools available in the BI market. BI vendors are now also consolidating tools in every category to provide complete BI solution to companies. However, some organisations still prefer to have best of breed strategy in which they select BI tools in each category from different vendors. The various categories are query and reporting, online analytical processing, dashboards and scorecards, performance management, predictive analytics and data mining and advanced visualisation. BI can derive better return on investment (ROI) from complex integrated cold chain management software and other operational systems implemented by unlocking the wealth of information stored in these systems. 4.Application areas of BI for cold chains The cold chain business is constantly searching for cost effective methods to remain competitive in fast changing world where margins are thin, customer expectation is very high, regulatory compliances are mandatory, and product life is very short. Companies in cold chain are working hard to adopt the information technology to get the rich insight into the hidden trends through cold chain analytics. The BI system provides reports, analyses, and monitors the vast corporate data. It also helps companies to reduce supply chain production cost, improve efficiencies, accuracy, increase revenue and performance. The cold chain analytics also provides the details to reduce waste, produce fresher, higher quality products, and enhance the economic value generated from perishable food industry by giving 360-degree overview of financial and operational results. As per suggested framework, various BI tools can be utilised to generate various analytics in following areas of cold chain business. 4.1 Supply chain intelligence Supply chain intelligence allows cold chains to evaluate supplier performance to negotiate prices, ensure timely deliveries and maintain high standard of quality by analysing the demand patterns, supply networks, operations and customer service requirements. Wal-Mart has set the standard of supply chain analytics. With the analytics driven intelligence, supply chain disruption can be reduced to better manage suppliers. Commodity classification provides information regarding procurement data from various sources within or outside company and classify the spend information into meaningful categories to understand true volumes per commodity. This can be used to develop the sourcing strategies. Spend analysis provides a dynamic ranking system for identifying and prioritising the most valuable suppliers. Demand driven forecasting allows planning of future requirements and management of supply chain by using statistical models. Scenario planning and what if analysis reduces the finished goods inventory and stockouts. The complete process of optimisation of plans and procedures creates an everlasting and sustainable competitive advantage for the organisation throughout a supply chain despite the risks associatedso commonly with unbounded challenges.4.2 Transportation analytics Increasing fuel costs, international expansion, and global competition has forced to use BI to streamline operations, distribution, and fleet management. BI optimises service and ensures consistent on-time performance for cold chains. Customers are demanding more services at lower prices, making operational efficiency improvements a requirement for maintaining acceptable profitability. The process of getting products delivered from one place to another on time, efficiently, and at the lowest costwithout losing life are main objectives of cold chains. The temperature conditions at origin and destination, seasonal temperature, load configurations, transport routes and modes, total duration of transit, duration and location of handling and stopover points are very important factors for temperature sensitive transportation. In thisextremely competitive business, one late delivery or losing quality of products can miss revenue opportunities and a lost customer forever. BI tools can help gain insight into the complex process of transportation by providing carrier performance evaluation, mode-cost analysis, supplier compliance analysis, carrier relationship management, capacity planning, cycle time analysis, routing and scheduling, truck and driver performance analysis, and root cause and claims analysis. 4.3 Warehouse analytics Warehouse management provides the ability to know the location of stock, time of requirement, and transporting it correctly in the shortest time. BI provides inventory analysis, warehouse performance analysis based on picking accuracy, shipping accuracy, lines per hour, overtime hours and on time shipments, picking analysis to improve warehouse efficiency and layout design, and warehouse space utilisation analysis for getting cost per unit of space over a period of time. 4.4 Inventory analysis Inventory optimisation analysis enables to reduce the over capacity and ensure sufficient supplies, monitor carrying cost for obsolete and slow moving items and usage across location and time. These analyses provides inventory carrying costs, inventory turns, order fulfilment lead time, percentage of backorders, average item inventory, finished goods on hands, etc. The intelligent analytics provides improved quality, reduces spoilage, and lowers rejections to make the customer or retailer more satisfied.4.5 Quality life cycle analysis4.6 Asset maintenance analytics4.7 Customer intelligence4.8 Financial analytics4.9 Customer profitability analysis6 Conclusions5.Conclusions Like every other business, cold chain business has also become fiercely competitive. In order to stay ahead, and remain competitive, the cold chains should implement BI solution. Huge volume of data generation from existing applications like warehouse management, logistics management, inventory management, RFID tracking and monitoring, order management, quality assurance, CRM, and supply chain management has given an opportunity to manager to take smart decision based on analytics rather than intuition. The suggested framework will guide cold chains to streamline their operation. BI solution implementation requires basic operational system in place. With rapid development of information technology, communication system and reduction of cost of smart phones has opened the new doors for cold chains to provide the mobile BI to their executives. There are some new trends like BI search, BI gadgets, and query as web service will make a lot of difference in future BI and those who implement BI solutions intelligently will definite have an edge over others.摘 要随着全球竞争的加剧，企业如何控制成本，使企业运营变得更加高效越来越成为世界上所有企业不得不面对的课题。而在冷链物流方面，相关的冷链基础设施对于冷链物流能否良好运行起到了至关重要的基础性作用。随着世界经济的不断发展，人们对于新鲜的农产品的需求也飞速增长。冷冻食品，胶片，化学品和医药用品对于冷链物流的大量需求，也要求冷链管理智能化程度的跟进，商业智能在冷链中的应用显得越来越必要。信息化的应用有利于冷链物流从业人员对企业的战略和功能做出及时调整，提高了企业对市场反应的敏捷性。近几年，商业智能的发展在不同的领域中得到重视，商业智能的应用发展势头迅猛。在本文中，作者为冷链物流努力构建了一个商业智能的结构，并且强调了包括冷链业务案例研究在内的冷链商业领域的应用。关键词：冷链物流、商业智能、商业智能构架、供应链一、 简介印度农产品冷链仓储设施不足1000万吨，对于印度3100万吨的存储设施需求量来讲，还有将近2170万吨的缺口。这造成的结果就是农产品收获后在运输过程中有高达40%的损耗率。印度是一个人口大国，为满足如此庞大的需求，如何提高资源的利用率一直是一个需要重点关注的问题。全世界范围内对于易腐食品，即农产品如：水果、蔬菜、和乳制品、肉制品、鱼、冷冻食品、胶片、化学品和药物等产品的需求量越来越大，在此类产品运输过程中，如何保证在途产品的卫生安全越来越受到重视。在农场生产、加工、仓储到企业的分销和零售，都需要集成冷链的功能。冷链管理过程中的差异对产品带来损害，甚至威胁到消费者人身安全。根据bourlakis Weightman（2004）的观点，冷链的有效管理关系到维护食品安全和企业盈利能力，因此，科学有效的冷链管理是至关重要的。现代技术的发明及应用在提高仓库存储、运输、配送能力的过程中十分重要。在这些过程中，产生了大量的数据，如此大量的数据需要进行智能化的处理和分析，以提高仓储企业的组织敏捷度。合理的运用商业智能，借助辅助设备能够智能的提取产品相关信息，并且做出合理决策。因此，商业智能的应用有助于企业价值和企业竞争力的提高。在这样的背景下，冷链物流从业人员应该尝试去理解并引进商业智能的概念，使商业智能识别在冷链物流领域得以应用和发展。到目前为止，出于提高冷链物流企业的效率和利润率，冷链物流从业人员和研究人员尝试通过研究相关的冷链物流公司案例，已经有一些组织机构提出了冷链物流商业智能的解决方案。二、 冷链物流商业智能的概念随着冷链业务的发展，其数据量也越来越大，数据处理技术在冷链的规划、冷链活动如仓储管理、运输和配送作业的控制中起着关键的作用。1989年，嘉德集团（美国康涅狄格州一家IT研究咨询公司）的研究学者Howard Dresner 推广了商业智能的概念和方法，并通过商业智能结合数据资源改善企业决策。Simon和Shaffer (2001)指出，从1990年到1999年是了不起的十年，许多核心的计算方法和通信技术在这十年里实现了有机结合，从而改变了商业模式。在20世纪90年代，企业资源规划（ERP）和客户关系管理（CRM）像LED技术一样诞生并且得到广泛应用。由于分布式数据库管理系统的故障，组织运行的数据仓库将由许多分布式和异构数据的商店合并。组织将通过建立和部署数据库来集中使用信息、分析生成报告、并分析整体趋势等等。McDonald（2002）表示，“数据仓库一旦建成，将成为商业智能有效表现的舞台”。商业智能是建立在数据仓库基础上的，因此数据库将作为基础设施为商业智能起到支持作用。Kalakota和Robinson (2001)将商业智能定义为一组能够主动和被动传递信息或把原始数据转化为可操作情报的应用程序，数据和信息是从大型数据库中收集而来用来回答管理者问题的。Gangadharan和Swami (2004)表示，商业智能是详细的业务数据进行深入分析后得出的结果，它包括数据库和应用技术，以及分析实践。Moss和Hoberman (2004)认为，其工艺、技术和工具
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