DATA DRIVEN DECISION SUPPORT TO SUPERMARKET LAYOUT

Knowledge is the most valuable asset in today's dynamic business environment. In many organizations, decisions are made based on a combination of judgment and knowledge extracted from databases. Successful business organization to be able to react rapidly to the changing market demands both locally and globally, by utilizing the latest data mining techniques of extracting previously unknown and potentially useful knowledge from vast resources of raw data. We propose a methodological framework for the use of the knowledge discovery process to improve store layout. In this paper, we propose a data driven decision support for store layout and present an empirical study. This paper develops a relational database and uses Apriori algorithm and multidimensional scaling techniques as methodologies for the store layout issue. As the empirical study, a supermarket analysis has done for Migros Turk A.S, a leading Turkish retailing company.