| Summary: | 碩士 === 元智大學 === 工業工程與管理學系 === 93 === ABSTRACT
Recently, the science and technology in Taiwan grow up fast and our country officially joined the WTO (World Trade Organization) in year of 2002, this made a great impact to the automobile parts industries. In order to survive in the intensive competition, the automobile spare parts factory in our country which is highly customer-oriented can only improve their efficiency of their supply chain system to adjust themselves to the seller’s market.
Making a comprehensive survey of the annual sales of the parts in our company, the types of products are classified into various items with the following characteristics such as low quantity, shortened product life-cycle, fast delivery time, and globalization in both purchasing and manufacturing. In supply chain system, the upper layer manufacturer in behalf of dealing with the short terminal product life-cycle and fast-responding customer service, so that to cause lower layer’s vendors magnifying the manufactory’s stocks extrusion and demand, thereupon making excess end items and materials to run out of stock.
In this thesis, we use customer history transaction data to perform production value analysis. First, based on the RFM analysis model and extended with RFM individual difference as weighted value, we transform RFM attributes into a three-dimensional vector to compute the absolute distance as the production value index. Then, with the Neural Network technique, which is a Data Mining technique with learning ability, we use past customer transaction data as input and current customer value indices as learning goal to produce a Neural Network model to predict future customer value indices. Finally, we actually perform computation and prediction of production value indices with customer history transaction data of two different industries.
Key word: RFM Analysis model 、Data Mining、Neural Network
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