DETERMINING THE AREA SIZES OF EACH PRODUCT CATEGORY IN A DEPARTMENT STORE USING MULTI-CRITERIA DECISION MAKING METHODOLOGIES

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Apr 1, 2020
Gulcin Dinc Yalcin Zehra Kamisli Ozturk

Abstract

In a department store, customers have the opportunity to reach a wide range of consumer goods from different product categories within a single store area. Store layouts generally show the size and location of each department, any permanent structures, fixture locations, and customer traffic patterns. Determining the area sizes to be allocated to each product category and the layout of these areas in the department store is a strategic planning decision problem. The layout problem has been studied in the literature with different approaches where the sizes of the areas are known. The first purpose of this paper is to determine the area sizes of each product category.

 

Customers decide to go to a department store for several reasons including the quality of products, services, location, etc. These reasons have been studied in the literature. However, “for which product categories do customers decide to go to a department store” is an open question. The second purpose of this paper is to find the frequency of product categories from the viewpoint of the customers. Therefore, our aim is to obtain the required results in a systematic way with multi-criteria decision making methodologies. For this purpose, we perform the Analytic Network Process (ANP) and the Analytic Hierarchy Process (AHP) from the viewpoints of department managers and customers, respectively.

 

In the ANP model, several tangible and intangible criteria such as product costs, the demands of customers, sales history, overall inventory, floor space and relationship with suppliers are chosen, and the intersections between them are specified. Pairwise comparisons are made by department store managers. The ANP outcome is the weight of each product category, and these weights are considered the percentage of the area size within the store from the viewpoint of the department stores. In the AHP model, a simple model is constructed to define the customers’ preference for each product category. Pairwise comparisons between product categories are made by the customers. Therefore, the outcome of the AHP model is the weight of each product category, and this is the preference of each product category from the viewpoint of the customers. The outcomes show that these weights may be different. This is an expected situation since even if a product category is preferred by some as the driver to visit a department store, the footprint of that category in the actual store may be small. The outcome from customers provides feedback to department store managers on which product category should be diversified as well as the area sizes of those categories.

How to Cite

Dinc Yalcin, G., & Kamisli Ozturk, Z. (2020). DETERMINING THE AREA SIZES OF EACH PRODUCT CATEGORY IN A DEPARTMENT STORE USING MULTI-CRITERIA DECISION MAKING METHODOLOGIES. International Journal of the Analytic Hierarchy Process, 12(1). https://doi.org/10.13033/ijahp.v12i1.602

Downloads

Download data is not yet available.
Abstract 1318 | PDF Downloads 420

##plugins.themes.bootstrap3.article.details##

Keywords

Department store, Space allocation, Multi criteria decision making, Analytical Network Process, Analytical Hierarchy Process

References
Akalin, M., Turhan, G. & Sahin, A. (2012). The application of AHP approach for evaluating location selection elements for retail store: a case of clothing store. International Journal of Research in Business and Social Science, 2(4): 1-20
Bahng,Y. & Kincade, D.H. (2014). Retail buyer segmentation based on the use of assortment decision factors. Journal of Retailing and Consumer Services, 21(4):643-652
Ballester, N., Guthrie, B., Martens, S., Mowrey, C., Parikh, P. J. & Zhang, X. (2014). Effect of Retail Layout on Traffic Density and Travel Distance. In Proceedings of the 2014 Industrial and Systems Engineering Research Conference
Botsali, A. & Peters, B. A. (2005). A network based layout design model for retail stores. In Proceedings of the 2005 Industrial Engineering Research Conference, 1-6
Cheng, L., Li, B., Cheng, Q., Baldwin, A.N. & Shang, Y. (2017). Investigations of indoor air quality of large department store buildings in China based on field measurements. Building and Environment, 118:128-143
Chunk, M. & Park, H.C. (2012). Building energy demand patterns for department stores in Korea. Applied Energy, 90:241-249
Cil, I. (2012). Consumption universes based supermarket layout through association rule mining and multidimensional scaling. Expert Systems with Applications, 39(10):8611-8625
Doucet, M.J. (2003). The department store shuffle: a study of rationalization and location change on a large metropolitan market. Progress in Planning, 60:93-110
Duncan, R.B. (1972). Characteristics of organizational environments and perceived environmental uncertainty. Administrative Science Quarterly, 17:313–327
Eckert, A., He, Z. & West, D. S. (2015). An empirical analysis of tenant location patterns near department stores in planed regional shopping centers. Journal of Retailing and Consumer Services, 22:61-70
Eroglu, E. (2013). Factors affecting consumer preferences for retail ?ndustry and retailer selection using analytic hierarchy process. Kafkas University Journal of Economics and Administrative Sciences Faculty, 4(6):43-57

Gardner, N.J, Huh, J. & Chung, L. (2002). Lessons from the Sampoong department store collapse. Cement&Concrete Composites, 24:523-529

Hjelmgren, D. (2016). Creating a compelling brand meaning by orchestrating stories: the case of Scandinavia’s largest department store. Journal of Retailing and Consumer Services, 32:210-217
Kannan, V.R. & Tan, K.C. (2006). Buyer-supplier relationships: the impact of supplier selection and buyer-supplier engagement on relationship and firm performance. International Journal of Physical Distribution and Logistics Management, 36:755–775
Kernsom, T. & Sahachaisaeree, N. (2010). Determining of design elements and compositional setting of window display on the corporate strategic merchandising of large scale department store: a case of central world department store. Procedia Social and Behavioral Sciences, 5:1351-1356
Kernsom, T. & Sahachaisaeree, N. (2012). Strategic merchandising and effective composition design of windows display: a case of large scale department store in Bangkok. Procedia Social and Behavioral Sciences, 42:422-428
Kumar, V., Anand, A. & Song, H. (2017). Future of retailer profitability: an organizing framework. Journal of Retailing, 93(1):96-119
Mantrala, M.K., Levy,M., Kahn, B.E., Fox, E.J., Gaidarev, P., Dankworth, B. & Shah, D. (2009). Why is assortment planning so difficult for retailers? A framework and research agenda. Journal of Retailing, 85:71–83
Miller, D. (2006). Strategic human resource management in department stores: An historical perspective. Journal of retailing and consumer services, 13:99-109
Ozcan, T., & Esnaf, S. (2013). A Discrete Constrained Optimization Using Genetic Algorithms for A Bookstore Layout. International Journal of Computational Intelligence Systems, 6(2):261-278
Park, T.W. (2012). Inspection of collapse cause of Sampoong department store. Forensic Science International, 217:119-126
Ratanatamskul, C. & Siritiewsri, T. (2015). A compact on-site UASB-EGSB system for organic and suspended solid digestion and biogas recovery from department store wastewater. International Biodeterioration & Biodegradation, 102:24-30
Sagir, M. & Ozturk, Z. K. (2010). Exam scheduling: Mathematical modelling and parameter estimation with the Analytic Network Process approach. Mathematical and Computer Modelling, 52:930-941
Saaty, T.L. (1980). The Analytic Hierarchy Process. McGraw-Hill, New York
Saaty, T.L. (2001). Decision making with dependence and feedback the Analytic Network Process. RWS Publications, Pittsburg, PA
Saaty, T. L. (2004a). Decsion making-the analiytic hierarchy and network process (AHP/ANP). Journal of Systems Science and Systems Engineering, 13(2):129-157
Saaty, T. L. (2004b) Fundamentals of the analytic network process-dependence and feedback in decision-making with a single network. Journal of Systems Science and Systems Engineering, 13(1):1-35
Saaty, T.L. & Vargas, L. G. (2006). Decision making with the Analytic Network Process: Economic, Political, Social and Technological Applications with Benefits, Opportunities, Costs and Risks. Springer Science+Business Media, LLC
Silver, E.A., Pyke, D.F. & Peterson, R. (1998). Inventory Management and Production Planning and Scheduling. Third ed. JohnWiley&Sons, NewYork
Silva, D. R., Davies, G. & Naude, P. (2002). Assessing the influence of retail buyer variables on the buying decision-making process. European Journal of Marketing, 36:1327–1343
Su, X., Chen, y., Hipel, K. W. & Kilgour, D. M. (2005). Comparison of the analytical network process and the graph model for conflict resolution. Journal of Systems Science and Systems Engineering, 14(3):308-325
Yapicioglu, H., & Smith, A. E. (2012a). Retail space design considering revenue and adjacencies using a racetrack aisle network. IIE Transactions, 44(6):446-458
Yapicioglu, H., & Smith, A. E. (2012b). A bi-objective model for the retail spatial design problem. Engineering Optimization, 44(3):243-266
Wagner, J., Etteson, R. & Parrish, J. (1989). Vendor selection among retail buyers: an analysis by merchandise division. Journal of Retailing, 65:58–79
Walsh, E.J & Jeacle, I. (2003). The taming of the buyer: the retail inventory method and the early twentieth century department store. Accounting, Organizations and Society, 28:73-791
Wargocki, P, Fanger, P.O., Krupicz, P & Szczecinski, A. (2004). Sensory pollution loads in six office buildings and a department store. Energy and Buildings, 36(10):995-1001
Section
Articles