# Applying ANN and TM to Build a Prediction Model for the Site Selection of a Convenience Store
## 5 Conclusions  
The structure of the parameters of a neural network determines the network's predictive reliability and validity. This study employed a robust experimental design, the TM, to BPN training to efficiently derive the optimal CVS site-selection decision model for the case company. Site-selection methods developed solely using an ANN methodology require time- and cost-intensive effort. In this study, combined with the TM's experimental design and the S/N ratios to evaluate performance, the BPN decision model confirmed that valid results can be obtained with fewer experiments. The TM can reduce the number of times required for the determination of the BPN for improved convergence and fewer network errors. We assert that with systematic assessment, site selection for a new chain of CVSs can be performed with reduced human bias. The actual operational data of a well-known chain of CVSs was used here to validate the site selection prediction model built in this paper. The results indicated that the predicted accuracy rate and the decision quality of the proposed prediction model were higher than those obtained from the existing decision method of using experienced managers. The combined application of ANN and TM to a retail business in this study represented a pilot in both the TM application and site selection literature. This study adopted the TM to identify important criteria for the BPN site selection prediction model. By using data from a well-known Taiwanese CVS chain, we provided a valuable and practical alternative and thus contributed knowledge about chain CVS site-selection decision-making. The deviation of the case company's actual sales from its sales forecast implied an unsuccessful site selection decision. For chain corporations in Taiwan, site selection criteria are usually employed using similar principles for each criterion--such as types of patron, business area, and location environment. Since the advent of analytical techniques, Taiwanese retailers have tended to apply contemporary advanced data analysis methodologies in their practices; ANNs, for example, are popular. However, with limited knowledge regarding such analytic solutions and a keen desire to achieve efficiency and effectiveness in retail businesses, this study proposed an approach that can enable Taiwanese chain CVSs to appropriately solve site-selection problems. The managerial implication of applying retail site selection studies is that companies should make efforts to review the feasibility and applicability of the requisite factors for building ANN prediction models. This study demonstrated this in the case company. It is necessary to debrief and re-examine current CVS site location evaluation procedures, including the effective criteria and their scales, based on the predictive accuracy of the ANN procedure. Nonetheless, the definition of successful and unsuccessful CVSs is not unique within the case company. Generally, a successful CVS is defined as one that achieves its revenue and/or other goals. Some unsuccessful CVSs may be considered successes when their actual sales are much higher than the original forecast. Hence, classifying successful and unsuccessful retail stores for BPN analysis is crucial. The approach presented in this study assists in building a systematic design of the BPN predictive model to reduce the cost, time, and personnel involved in the site selection process. The purpose of this study was not to determine the \"definite\" predictive model for CVS site selection across all CVSs. Other criteria may be more salient in site selection in other business areas or cities. In addition, as shown in Table 5, there were major differences between the actual turnover and the predictions. This demonstrates the limitation that the source data were collected or encoded by subjective judgements, as the data fields were still treated as important and indicative of store site-location determinants.