یک روش داده کاوی برای طبقه بندی محصولات و اختصاص فضای قفسه
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|21408||2007||11 صفحه PDF||سفارش دهید||6093 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 32, Issue 4, May 2007, Pages 976–986
In retailing, a variety of products compete to be displayed in the limited shelf space since it has a significant effect on demands. To affect customers’ purchasing decisions, retailers properly make decisions about which products to display (product assortment) and how much shelf space to allocate the stocked products (shelf space allocation). In the previous studies, researchers usually employed the space elasticity to optimize product assortment and space allocation models. The space elasticity is usually used to construct the relationship between shelf space and product demand. However, the large number of parameters requiring to estimate and the he non-linear nature of space elasticity can reduce the efficacy of the space elasticity based models. This paper utilizes a popular data mining approach, association rule mining, instead of space elasticity to resolve the product assortment and allocation problems in retailing. In this paper, the multi-level association rule mining is applied to explore the relationships between products as well as between product categories. Because association rules are obtained by directly analyzing the transaction database, they can generate more reliable information to shelf space management.
Most retailers nowadays face challenges such as how to respond consumer’s ever-changing demands and how to adapt themselves to keen competition in dynamic market. Retail management is to develop a retail mix to satisfy customers’ demands and to affect customers’ purchasing decisions. The factors in retail mix include store location, product assortment, pricing, advertising and promotion, store design and display, services and personal selling (Levy & Weitz, 1995). Shelf space is an important resource for retail stores since a great quantity of products compete the limited shelf space for display. Retailers need frequently make decisions about which products to display (assortment) and how much shelf space to allocate these products (allocation) (Borin and Farris, 1995 and Borin et al., 1994). Product assortment and shelf space allocation are two important issues in retailing which can affect the customers’ purchasing decisions. Through the proficient shelf space management, retailers can improve return on inventory and consumer’s satisfaction, and therefore increase sales and margin profit (Yang, 1999). In the past two decades, numerous models and solution approaches have been developed to deal with product assortment and/or shelf space allocation problems (Anderson and Amato, 1974, Borin and Farris, 1995, Borin et al., 1994, Brijs et al., 2000, Brijs et al., 1999, Bultez and Naert, 1988, Bultez et al., 1989, Corstjens and Doyle, 1981, Corstjens and Doyle, 1983, Hansen and Heinsbroek, 1979, Urban, 1998 and Yang, 1999). In these previous studies, the individual space elasticity and the cross-elasticity between products are usually applied to estimate the relationship between shelf space and demands. Traditionally, researchers apply the space elasticities to determine which products to stock and how much shelf space to display these products. However, there are two major limitations that reduce the effectiveness of the space elasticity (Borin and Farris, 1995 and Borin et al., 1994). First, due to the non-linear nature of space elasticity, the space elasticity based models are very complicated, and the specific solution approach is developed for each model. Additionally, it is necessary to estimate a large number of parameters by using the space elasticity. Recently, the progress of information technology makes retailers easily collect daily transaction data at very low cost. Through the point of sale (POS) system, a retail store can collect a large volume of transaction data. From the huge transaction database, a great quantity of useful information can be extracted to support the retail management. Data mining is frequently adopted to discover the valuable information from the huge database. In data mining, association rule mining is widely applied to market basket analysis or transaction data analysis (Agrawal et al., 1993 and Srikant and Agrawal, 1997). This study proposes a data mining approach to make decisions about which products to stock, how much shelf space allocated to the stocked products and where to display them. Association rules are generated by directly analyzing the transaction database, and these rules can be used to effectively resolve the product assortment and shelf space allocation problems. This study applies the association instead of the space elasticity to formulate the mathematical model for product assortment. In this paper, multi-level association rules are generated to express the relationships between products and product categories to allocate the products selected in the assortment stage.
نتیجه گیری انگلیسی
To face the keen competition in retail market, retailers need to accurately and quickly respond the dynamic customers’ requirements. Shelf space management is an important issue to keep the competitive advantage in retailing sector. Retailers can try to satisfy the diverse customers’ demands and to affect customers’ purchasing decisions by using the systematic approach for product assortment and allocation. With the rapid development of information technology, retailers have put a huge amount of transaction data in storage, and they potentially can be used to support shelf space management. This paper develops a data mining based approach to simultaneously make decisions about which products to stock, how much shelf space allocated to the stocked products and where to display them. There exist some advantages in the proposed product assortment and space allocation approach. Firstly, because association rules are obtained by directly analyzing the transaction database, therefore they are reliable for shelf space management. Secondly, the massive estimation of parameters in space elasticity can be eliminated, and the estimation error and costly experiment can thus be reduced. Thirdly, association rules can quickly respond to market changes since the transaction data are timely collected by retailer’s POS system. Forth, the assortment model ensures to include the basic products for expressing the store’s image, and the added products are determined by using the associations between product items. Fifth, by mining the multi-level association rules, retailers can allocate the product categories, subcategories and items with respect to their associations and profits.