Representing a Model Using Data Mining Approach for Maximizing Profit with Considering Product Assortment and Space Allocation Decisions

Document Type: Research Paper


1 Associate Professor/ University of Tehran

2 Assistant Professor/ University of Tehran

3 None


The choice of which products to stock among numerous competing products and how much space to allocate to those products are central decisions for retailers. This study aimed to apply data mining approach so that, we got needed information from large datasets of sale transactions to find the relations between products and to make product assortments. Thus, we represented a model for product assortment and space allocation. Research population was transactional data of a store, the sample included transactional data of one-month period in the time series. Data were collected in October and November, 2015 from Shaghayegh store. 525 transactions with regard to 79 different products were analyzed. Based on the result 10 product assortments formed although some products were allocated to more than 1 product category. By solving profit equation and finding volume increase indices we allocated spaces for each product assortment.


Main Subjects

Anderson, E. E. & Amato, H. N. (1974). A mathematical model for simultaneously determine the optimal brand-collection and display area allocation. Operations Research, 22(1), 13–21.

Azizi, Sh., Qareche, M., Tavangar, M. H. & Jamali Kaapek, Sh. (2013). Identifying and Prioritizing the Key Factors Influencing Customer Decision Making in Buying Organizational Software (A survey about HAMKARAN Co.). Quarterly Journal of Information technology management, 5 (2): 117-134. (in Persian)

Borin, N. & Farris, P. W. (1995). A sensitivity analysis of retailer shelf management models. Journal of Retailing, 71(2), 153–171.

Borin, N., Farris, P. W. & Freeland, J. R. (1994). A model for determining retail product category assortment and shelf space allocation. Decision Science, 25(3), 359–384.

Brijs, T., Goethals, B., Swinnen, G., Vanhoof, K. & Wets, G. (2000). A data mining framework for optimal product selection in retail supermarket data: the generalized PROFSET model. In KDD-2000, Boston, MA, USA.

Brijs, T., Swinnen, G., Vanhoof, K. & Wets, G. (1999). Using association rules for product assortment decisions: a case study. In KDD-99, San Diego, CA, USA.

Chen M.C., Lin C. P. (2007). A data mining approach to product assortment and shelf space allocation. Expert Systems with Applications, 32(4), 976–986.

Corstjens, M. & Doyle, P. (1981). A model for optimizing retail space allocations. Management Science, 27(7), 822–833.

Corstjens, M. & Doyle, P. (1983). A dynamic model for strategically allocating retail space. Journal of Operational Research Society, 34(10), 943-951.

Ghazavi, E. & Lotfi, M. (2016). Formulation of customers’ shopping path in shelf space planning: A simulation-optimization approach. Expert Systems with Applications, 55, 243-254.

Hansen, J.M., Raut, S. & Swami, S. (2010). Retail Shelf Allocation: A Comparative Analysis of Heuristic and Meta-Heuristic Approaches. Journal of Retailing, 86(1), 94–105.

Hariga, M.A., Al-Ahmari, A. & Mohamed, A.A. (2007). A joint optimization model for inventory replenishment, product assortment, shelf space and display area allocation decisions. European Journal of Operational Research, 181(1), 239–251.

Hashem, T., Ahmed, C., Samiullah, M., Akther, S., Jeong, B. & Jeon, S. (2014). An efficient approach for mining cross-level closed itemsets and minimal association rules using closed itemset lattices. Expert Systems with Applications, 41(6), 2914-2938.

Hosseini, S.Y., Bahreynizadeh, M. & Ziaei-Bideh, A. (2012). Importance-Performance Analysis of Service Attributes based on Customers Segmentation with a Data Mining Approach: A Study in the Mobile Telecommunication Market in Yazd Province. Quarterly Journal of Information technology management, 4 (13), 45-70. (in Persian)

Hwang, H., Choi, B. & Lee, M.J. (2005). A model for shelf space allocation and inventory control considering location and inventory level effects on demand. International Journal of Production Economics, 97(2), 185–195.

Irion, J., Lu, J.C., Al-Khayyal, F. & Tsao, Y.C. (2012). A piecewise linearization framework for retail shelf space management models. European Journal of Operational Research, 222(1), 122–136.

Katsifou, A., Seifert, R.W. & Tancrez J.S. (2014). Joint product assortment, inventory and price optimization to attract loyal and non-loyal customers. Omega, 46, 36–50.

Mohammadi, A., Sahrakar, M. &Yazdani, H.R. (2011). Investigating the Effects of Information Technology on the Capabilities and Performance of the Supply Chain of Dairy Companies in Fars Province: A Multiple Case Study. Quarterly Journal of Information technology management, 3 (8), 151-170. (in Persian)

Radfar, R., Nezafati, N. & Yousefi Asli, S. (2014). Classification of Internet banking customers using data mining algorithms. Quarterly Journal of Information technology management, 6 (1), 71-90. (in Persian)

Yücel, E., Karaesmen, F., Salman, F.S. & Türkay, M. (2009). Optimizing product assortment under customer-driven demand substitution. European Journal of Operational Research, 199(3), 759–768.

Zufryden, F. S. (1986). A dynamic programming approach for product selection and supermarket shelf-space allocation. Journal of Operational Research Society, 37(4), 413-422.