Research On The Method And Application Of Spare Parts Demand Forecasting And Inventory Control For Complex Equipment

With the further development of science and technology, more and more complex equipment are used in industry. Complex equipment is normally a large-scale system composed of numerous components, some of key parts malfunction may render the equipment inoperable, which seriously affects normal production. Therefore, spare parts management has a crucial role in guaranteeing the normal operation of the manufacturing system. Based on an overview of the literature on the models of the complex equipment information and inventory management, this thesis conducts a study on classification methods, demand forecasting and inventory control strategy for spare parts. Applying to a steam turbine of an industry corporation, the method and the model are verified. A set of spare parts inventory management information software is finally developed at the end of this thesis.Chapter one illustrates the research situation at home and abroad of classification methods, demand forecasting and inventory control strategy for spare parts, analyses the deficiency of the basic spare parts inventory management system applying in complex equipment, and further defines the research significance of objective, presents the main content and the structure of dissertation.In the second chapter, the dissertation proposes a spare parts classification method which based on multi-index combined with fuzzy evaluation, determines the importance of the different spare parts which synthesizes multi-index such as storage, economics and significance. The thesis evaluates the type-needs and consumption characteristic under the velocity-index of spare parts by the theories and method of fuzzy mathematics, and completes spare parts classification for a steam turbine of an industry corporation.In the third chapter, the dissertation studies demand forecasting methods which are suitable for different spare parts type-needs. Considering the influences of trend and seasonal factors may be on A spare parts which has a demand of continuous period, this thesis chooses winters method to predict the forecast and gets the smooth parameter optimization through matlab iteration. Before the demand forecasting of A spare parts has an uncontinuous period using bayesian statistics, the spare parts curve-like failure rate model should be analyzed first, then calculates the conjugated prior distribution of bayesian theory and safety inventory which meets a certain service level.The forth chapter is based on the result of spare parts classification and demand forecasting, faces the different importance of spare parts and consumption character-istics, develops appropriate inventory control strategy for class A fast moving spare parts, class B and class C fast moving spare parts, class A slow moving spare parts, class B and class C fast moving spare parts separately, which helps inventory managers to make decisions for the problems of "Cycle time of inventory review", "order point of spare parts" and "order quantity of spare parts". A case study conducts to verify the correctness and validity of the theory and methods presented in the thesis.In the fifth chapter, a three-tier architecture of browser-server-database is made up based on theoretical research. The dissertation develops a set of complex equipment spare parts inventory management system. The functions and characteristics of the main modules of this system are also introduced combined with the project in the application of an industrial steam turbine.In the last chapter, this dissertation summarizes the main conclusions and prospects the further research work.