یک سیستم ترکیبی با ترکیب نقشه های خود سازمان ده با استدلال مبتنی بر مورد در کتاب جدید الانتشار پیش بینی عمده فروش
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|21837||2005||10 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 29, Issue 1, July 2005, Pages 183–192
In this paper, we proposed a hybrid system to combine the self-organizing map (SOM) of neural network with case-based reasoning (CBR) method, for sales forecast of new released books. CBR systems have been successfully used in several domains of artificial intelligence. In order to enhance efficiency and capability of CBR systems, we connected the SOM method to deal with cluster problems of CBR systems, SOM/CBR for short. Case base is acquired from a book selling data of a wholesaler in Taiwan, and it is applied by SOM/CBR to forecast sales of new released books. We found the SOM/CBR method has excellent performance. The result of the prediction of SOM/CBR was compared with the results of K/CBR, which is divided by K-mean, and traditional CBR. We find out that the SOM/CBR is more accurate and efficient when being applied to the forecast of the data than K/CBR or traditional CBR.
Under the extremely competitive business environment, in order to face the complex market competitions, enterprises are trying their best to make the ultimate policy. The completeness of the information available to the decision maker is the key factor influencing the quality of the decisions. An enterprise could have better controls of the trend for the sales growth of a new product if a sales forecast is conducted for this new product. In business forecasting, managers often use the outcomes of past similar cases to predict the result of the current one. The methods to be used are nothing more than naive prediction, statistical methods, or artificial intelligent methods. Among these methods, artificial intelligent (AI) methods are mostly used in academic studies because of the ability to provide rapid solutions with high accuracy and to deal with diversified cases. Among AI methods, case-based reasoning (CBR) has been paid attention gradually. The earliest contributions to the area of CBR were from Schank and his colleagues at Yale University (Schank, 1982 and Schank and Abelson, 1977). During 1977–1993, CBR research was highly considered as a conceivable high-level model for cognitive processing. Aamodt and Plaza (1994) indicated that CBR systems have been successfully used in several domains such as diagnosis, prediction, control, and planning. Based on the survey conducted by Watson (1997), there were more than 130 enterprises using CBR systems to solve many kinds of problems in companies at the end of 1997. For the book industry in Taiwan, it is very difficult to predict sales volumes because the products have various classifications and different lengths of life-cycles. On an average, there are about 3412.6 new books being published every month in Taiwan, and the speed for new released books is really high. The returning rate of books is more than 30% in this industry according to the actual data collected from the wholesaler and from past studies (Council for Culture Affairs, 2000). The main reason of high book returning rate is caused by the insufficient information of book sales status in the book supply chain which brings up bullwhip effect and form up the unbalanced situation between supply and demand. High book returning rate is a very heavy burden for all companies in this industry. Hence, we propose a sales forecasting system for new released books to assist on decision making regarding book ordering. The system is a hybrid CBR method integrating a conventional CBR with SOM neural network-based clustering to conduct a high accurate and efficient book sales forecasting to reduce high book returning rate and increase profits. The remainder of this paper is organized as follows. Section 2 describes relevant literature review. Section 3 presents the hybrid method that integrates CBR with SOM neural network-based clustering. Section 4 shows problem description. Section 5 depicts experimental results. In Section 6, the conclusion is presented.