| Summary: | 碩士 === 元智大學 === 工業工程與管理學系 === 92 === ABSTRACT
The generic problem in book industry in Taiwan is uncertain sales of new released books. Making an order by relying on past sale records can easily result in over stock or shortage for book retailers. It also increases cost and depresses profit. No doubt it’s a severe challenge for low capital book-retail industry. Therefore, an effective forecast model is critical.
Because there are no past sales records of new released books, purchasers in book industry won’t be able to know the most likely amount of an order, which can satisfy future demands. This research analyzes the factors, which affect book sales, tests the factors similarity of past and new released books by using case retrieval of Case-Based Reasoning (CBR), and estimates the sales of new released books by using the sales records from past released books that have similar topics. In order to increase the effectiveness of the forecast from CBR, this research introduces the concept of clustering, which divides the gigantic sales database by utilizing SOM of neural network, S-CBR for short. This concept increases the effectiveness and efficiency of case retrieval in traditional CBR.
Finally, the result of the forecast of S-CBR is compared with the results of K-CBR, which is divided by K-mean, and traditional CBR. The conclusion is that S-CBR is more accurate in the forecast of the data than K-CBR or traditional CBR is.
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