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Design of Product Placement Layout in Retail Shop Using Market Basket Analysis

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Design of Product Placement Layout in Retail Shop Using Market Basket Analysis
MAKARA, TEKNOLOGI, VOL. 9, NO. 2, NOPEMBER 2005: 43-47

43

DESIGN OF PRODUCT PLACEMENT LAYOUT IN RETAIL SHOP USING MARKET BASKET ANALYSIS
Isti Surjandari dan Annury Citra Seruni
Industrial Engineering Department, Faculty of Engineering, University of Indonesia, Depok 16424, Indonesia E-mail: surjandari.2@osu.edu, a_citra_s@yahoo.com

Abstract
Retailing is an industry with high level of competition. It is a customer-based industry which depends on how it could be aware of what the customers’ needs and requirements are. One technique most used in supermarkets is the mix merchandise. The purpose of this paper is to identify associated products, which then grouped in mix merchandise with the use of market basket analysis. This association between products then will be applied in the design layout of the product in the supermarket. The process of identifying the related products bought together in one transaction is done by using data mining technique. Apriori algorithm is chosen as a method in the data mining process. Using WEKA (Waikato Environment for Knowledge Analysis) software, the association rule between products is calculated. The results found five category association rules and fourteen sub-category association rules. These associations then will be interpreted as confidence and support to become consideration for the product layout. Keywords: layout, market basket analysis, retail

1. Introduction
Retail industry is a kind of business with high level of competition. The success of retail business is influenced by its fast response and its ability in understanding consumers’ behaviors. Retail business must focus to its consumer since retail business plays its role at the end of distribution channel. Consumer buying behaviors can be comprehended by observing how someone interacts and reacts to the marketing mix. According to Cohen (1991), company determines the decisions related to the 4P (Product, Place, Promotion, and Price) by focusing to its



References: [1] R. Agrawal, T. Imielinski, A. Swami, Proceeding of the ACM SIGMOD Conference on Management of Data, 1993. [2] R. Agrawal, R. Srikant. The International Conference on Very Large Databases, 1994. [3] I. H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, chapter 8, http://www.cs.waikato.ac.nz, 2000. [4] A. Roberts, Guide to WEKA, http://www.comp.leeds.ac.uk/andyr, 2005. [5] M. Levy, B. Weitz, Retailing Management, McGraw-Hill, New York, 2001.

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