# Applying ANN and TM to Build a Prediction Model for the Site Selection of a Convenience Store
## 2 Literature Review  
Site selection is a crucial decision for most businesses, including those in retailing, the service industry, and manufacturing [12]. It has a consequential impact on profitability and is a valuable strategic tool for chain store operations. Site selection involves more than merely selecting a site with good visibility and accessibility [13]; accordingly, it requires a thorough evaluation process. Competition, demographics (i.e., prospective customers in the area), and market demands must also be considered [14]. For example, a site requires an adequate number of vehicles to pass by each day. Additionally, the availability of convenient parking is often an essential consideration, well as the definition and evaluation of competition near the proposed site. In fact, the number of competitors near a site can be a double-edged sword: competitors may attract crowds to the area yet also offer severe competition [15]. There may also be neighborhood changes, traffic changes, natural barriers, and human-made obstacles at the site [16]. Therefore, a robust and systematic design system for a store's, site selection is essential, especially because of the dynamism in the store and business environments. In addition to the above issues, the impact of site location on a firm's financial metrics is important. [PERSON] et al. [17] and [PERSON] et al. [18] investigated the variance in store performance with respect to customer expenditure, as well as store operating costs, earnings, profits, and legal/fiscal factors. They found that these data significantly reflected the different commercial sectors of retail stores. [PERSON] et al. [6] proposed three traditional assessment methods for determining and selecting store location: the checklist method, the simulation method, and multiple regression analysis. These methods can provide systematic steps for screening store locations. However, they fail to consider the relationship among all the influential factors involved in the decision. Recently, analytic solutions through the use of big data have been prevalent in solving business problems, including retail store site selection. Focusing on data-driven decisions--due to the advent of ICT (Information and Communication Technology)--enables decision makers to overcome the traditional challenges of brick-and-mortar retail store site selection. Moreover, data on customer behaviors sourced from social media, customer surveillance data within stores, and customer mobile data through use of retailers' mobile applications, as well as street varieties, should also be considered in site selection, yet it tends to receive less attention [19; 20; 21]. [PERSON] and [PERSON] [22] emphasized that the most important decision a retailer makes is the selection of an appropriate location to ensure retailing success. However, [PERSON] and [PERSON] [5] argued that, although techniques for locational analysis have been available for over 50 years, regrettably, most retailers traditionally do not apply these techniques in practice. Instead, they chiefly rely on intuition and judgment guided by experience and \"common sense\". Compounding the problem are the fact that the operation of a retail store is heavily affected by site factors and that the quality of decision-making skills in choosing the trade area are related to the extent of their development [23]. The above discussion suggests that the determination of site-selection criteria for CVSs is not definite. Rather, it is dependent on store structure, store environment, business district, choice of inventory, corporate culture, and business model [4; 13; 24]. As noted above, the conventional approaches in this field include managers' judgment--based simply on knowledge and experience--the checklist approach, the analog method, regression models, and simulation [16; 25; 26]. These alternatives only provide a set of systematic steps for problem-solving but do not adequately deal with the relationship among the decision factors. Their weaknesses are described below.