Study On The Purchasing Model Of Farm Products Based On CPFR

One of the main important features of farm products is their biotic energy, and the period of their quality guarantee is very short, which means they are easily perishable in the course of transportation and storage. These are the important factors of reducing management profits of farm products, and will result in serious waste of social resources. Considering of these facts, and in order to cut down the waste in farm product logistics and storage, purchasing strategies should be considered seriously. Therefore, it is valuable of researching in the project of purchasing of farm products. Because of the fact that there are many non-determinated factors effecting farm product purchasing forecasting, it is very difficulty in judgement with empirical experience or improving forecasting precision by a simple model. In this thesis, to resolve this problem, an intelligent forecasting system for farm product demands is put forward, which is mainly based on the theory of Support Vector Machine (SVM), and integrates many other theories and methods. At the same time, many non-determinated factors affecting the future demands for farm products are considered in dynamic forecasting. Because the supply and demands for farm products are all in dynamic status, therefore, by integrated with collaborative forecasting method, a CPFR(Collaborative Planning, Forecasting and Replenishment) management method is used to increase the forecasting precision of supply-demand in farm product supply chain. The purpose of using this model is also to construct the scientific strategical foundations of dealing with the inventory replenishment problem. The significance of this research is both to theoretically provide new thoughts for farm product purchasing study and to practically improve the rationality of inventory replenishment by accurately forecasting the demands for farm products in supply chain, which will lead the purchasing decision making of farm products to a more intelligent and scientific stage. At first, this thesis gives a comprehensive description of the meaning, the features and implement framework of CPFR, analyses the applicability of applying CPFR management strategies to farm product purchasing, and introduces briefly the main aspects of present purchasing management. Based on such work, the goal and significance of this research work are discussed specifically, and the research status of farm product purchasing both at home and abroad is summarized and commented in detail. Then the characteristics of farm products and market rules are given in this thesis, and the classification of farm products with regard to its expiration is also discussed. Traditional CPFR process is improved in virtue of farm product purchasing, and a new CPFR purchasing process is put forward with the consideration of retailer and supplier in supply chain. Furthermore, by extending two-echelon supply chain to multiple-echelon supply chain and affiliating with up-streaming supplier in farm product supply chain, a n-layer CPFR model is constructed, which configuration model is consequently investigated. Joined with CTM(Collaborative Transportation Management) management thoughts, the application of n-layer CPFR model in agriculture product supply chain is also discussed in detail. Thirdly, investment game model is established within the CPFR cooperation framework, and two classifications of equilibrium, that are interior equilibrium and boundary equilibrium, are defined, and their existence are proved accurately. In view of multi-equilibrium solutions, a general way is to gain Pareto optimal solution by means of refining equilibrium. While in this thesis, the notion of equilibrium stability is put forward, and the stability solution of invest game model is also given. Based on the study on the relationship between parameters and the effect of main parameters to the equilibrium stability, a conclusion is drawn that theoretical research work will help to construct and implement CPFR model in farm product supply chain. Fourthly, a dynamic forecasting system of demands for farm products in consume market is studied by means of applying the intelligent forecast method SVM. In order to achieve higher forecast precision of farm product sale quantities, such factors as weather, climate conditions and demands on holidays etc., are introduced to the forecasting model, and the Fuzzy Theory is also applied to cope with the problem of fuzzification. Furthermore, an intelligent forecasting system is constructed, which is mainly based on SVM theory and combined with many other techniques. Using this system to dynamically forecast the demands for farm products, the practical application shows that the precision is better then by using any other forecasting methods. Fifthly, a collaborative forecasting model between a retailer and a supplier isconstructed. In this model, the evolution of demand and forecast for farm product is analyzed, and the forecasting adjustment variable ψnm ,i, the correlation ρbetween adjustment variables ψnr ,i and ψns ,i, and the forecasting capability ηare all in consideration. The relationship between the forecasting adjustment correlation and forecasting capability is also discussed. At last, an inventory replenishment model of farm product purchasing is put forward by introducing collaborative forecasting model to inventory replenishment strategies of retailer and supplier. The practical simulation of such model shows that, when both of the supplier and retailer follow the collaborative forecasting rules, the inventory level is close to the actual demands and the cost of supply chain will reduce consequently. Another inventory replenishment model is also studied in detail that aims at achieving the largest whole profit in supply chain, while the demands for farm products and the lead time of order are all stochastic, the price discount is included, and the shortage of the supplier or retailer is allowed.