# Applying ANN and TM to Build a Prediction Model for the Site Selection of a Convenience Store
## 4 Case Study
### Data Collection  
First, the determinate factors for a new chain of convenience stores in this real case included nine input data items: trade area characteristics (\(A\)) (\(A1\)--residential area and \(A2\)--commercial area), competitiveness of other CVSs (\(B\)), competitiveness of supermarkets (C), people flow (\(D\)), major customer type (\(E\)), number of households within a radius of 100 m (\(F\)), store location (\(G\)), visibility (\(H\)), and usable area (\(I\)) (as defined in Table 1). We then collected the data of the nine factors from 96 new stores of a well-known CVS chain store in Taiwan (see Table 2). \begin{table}
\begin{tabular}{c c c c} \hline
**No** & **Factor** & \multicolumn{2}{c}{**Definition**} \\ \hline \multirow{3}{*}{\(A\)} & \multirow{3}{*}{Customer source} & \(A1\) & Residential area (dominated by the residential population surrounding the site, with fixed consumption and higher turnover on holidays and non-work-days compared with weekdays) \\ \cline{3-4}  & & & Business area (dominated by transient populations surrounding the site engaging in commercial activities in the area, with a higher number of customers on weekdays) \\ \hline \multirow{2}{*}{\(B\)} & \multirow{2}{*}{Competitiveness of other CVSs} & The number of other chain CVSs within a radius of 200 M + self-owned CVSs within a radius of 100 M \\ \hline \multirow{2}{*}{C} & \multirow{2}{*}{Competitiveness of supermarkets} & The number of supermarkets and other types of retail stores within a radius of 200 M \\ \hline \multirow{2}{*}{\(D\)} & \multirow{2}{*}{Customer flow} & Pedestrian flow through the site + the number of motorcycles passing through the site \\ \hline \multirow{3}{*}{\(E\)} & \multirow{3}{*}{Major customer type} & (1) Workers \\  & & (2) Students \\ \cline{3-4}  & & (3) Pedestrian flow \\ \cline{3-4}  & & (4) Nearby residents \\ \hline \multirow{2}{*}{\(F\)} & \multirow{2}{*}{The number of households} & The number of effective households within a radius of 100M \\ \cline{3-4}  & & (1) Intersection of two parkways (between main streets) \\ \hline \multirow{2}{*}{\(G\)} & \multirow{2}{*}{Store location} & (2) Intersection of one parkway and byway (between minor streets) \\ \cline{3-4}  & & (3) Off intersection \\ \hline \(H\) & Visibility & The signboard is visible from 100 m away \\ \hline \multirow{2}{*}{\(I\)} & \multirow{2}{*}{Usable area} & The area of the store (greater than 75 m\({}^{2}\) or not) \\ \end{tabular}
\end{table} 
Table 1: Definitions of site selection assessment indicators. The existing evaluated model was based on the idea that the initial prediction turnover of a new store is obtained based on the sum of each of the actual data factors of the nine determinants times the given weight of each determinant. Whether a new store is a success is based the actual turnover. If the value of the predicted turnover/average actual turnover per day over an operational period of six months is higher than 90% (0.9), then the new store is defined as a success; otherwise, it is a failure. For instance, the prediction score form of Store 1 is shown in Table 2. The scores for factors \(B\), \(C\), \(D\), \(E\), \(F\), \(G\), and \(H\) are 100, 60 \(\times\) (20 + 35 + 5), 11.7 \(\times\) (6.6 + 5.1), 100, 17.2, 80 \(\times\) (30 + 50), and 50 respectively (Table 3), and we can estimate the number of visitors at 914.5--based on the weightings of the residential area (Factor \(A\)) (Table 4). Therefore, the predicted turnover/per day is 914.5 \(\times\) 70 (customer price of residential area) \(\times\) 1.1 (Factor \(I\): the area of store 1 is greater than 100 square meter) = 70,400. Finally, we can predict the daily turnover of each store and collect the average actual daily turnover for six months from the 96 new stores, as summarized in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c c} \hline \hline
**No** & **\(A1\)** & **\(A2