Mağaza Yerleşim Planının Müşterilerin Satın Alımlarına Etkisi: İki Mağazanın Karşılaştırılması

Year 2019, Volume: 8 Issue: EUREFE ’19, 395 - 401, 30.12.2019

Bahar Taşar , İpek Kazançoğlu

Abstract

Müşterilerin mağaza
içinde en kısa yolu izleyerek ihtiyaç duydukları ürünleri satın almak
istedikleri bilinmektedir. Müşterilerin yeni ve unutulmuş ihtiyaçlarını
tetikleyen fiziksel ürünlerin müşteri alışveriş yolu üzerinde olması onların
dürtüsel satın alımlarını arttırabilmektedir. Bu bağlamda çalışmanın amacı,
perakende mağazalardaki yerleşim düzenine bağlı uzun rotaların müşterilerin
alışveriş sepetlerinin büyümesine neden olup olmadığını belirlemektir.
Çalışmada bir perakendecinin benzer özelliklere sahip iki mağazasında adet
cinsinden ortalama sepet büyüklüğü kadar en sık satın alınan ürünler kümesinden
alışveriş listesi oluşturulmuştur. Her iki mağazada da listedeki ürünlerin
birbirlerine olan uzaklık matrisleri elde edilmiştir. Müşterilerin en kısa yolu
daha önceki tecrübelerine göre oluşturdukları ve en kısa yolu izledikleri
varsayımı altında rotalar gezgin satıcı problemi yöntemi ile ele alınmıştır.
Mağazalardaki müşteri alışveriş rotalarının alışveriş sepet büyüklüklerini
nasıl etkilediği incelenmiştir. Sonuçlara göre, satışları yüksek olan ürün
gruplarının mağaza içinde uzak noktalara yerleştirilmesinin satın alımları
arttırarak müşterilerin alışveriş sepetlerini büyüttüğü görülmüştür.   

Keywords

Alışveriş Yolu, Mağaza Yerleşim Düzeni, Gezgin Satıcı Problem, Tüketici Davranışları, Dürtüsel Satın Alma

The Effect of Store Layout Plan on Purchases of Customer: Comparison of Two Stores

Abstract

It
is known that customers want to choose the shortest path while buying products
they need in the store. The products that trigger the new and forgotten needs
of the customers is on the way of shopping path can increase their impulsive
purchases. The aim is to determine whether the long routes depending on the
layout of the retail stores that cause growing the shopping carts of the
customers. In this study, shopping lists from the most frequently purchased
products in average basket size were determined in two stores with similar
characteristics of a retailer. The distance matrices of the products listed in
both stores were obtained. Under the assumption that customers create the
shortest route according to their previous experience and follow the shortest
route, the routes are generated by using Traveling Salesman Problem. It was
examined how the distances between the products that are purchased frequently
in both stores affect the shopping routes and basket size of customers in the
store. According to the results, placing product groups with high sales in
distant locations within the store increased the purchases and basket sizes.

Keywords

Shopping Path, Store Layout, Traveling Salesman Problem, Impulsive Purchasing

References

• Aarts, E. H., Korst, J. H., & Michiels, W. (2014). Simulated Annealing, Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques. Springer Science & Business Media.
• Bitner M.J. (1992). Service Scapes: The Impact of Physical Surroundings on Customers and Employees. Journal of Marketing, 56, 57-71.
• Boros, P., Fehér, O., Lakner, Z., Niroomand, S., & Vizvári, B. (2015). Modeling Supermarket Re-Layout from The Owner’s Perspective. Annals of Operations Research, 238(1-2), 27–40.
• Botsali, A.R., & Peters, B.A. (2005). A Network Based Layout Design Model for Retail Stores. Proceedings of the Industrial Engineering Conference, Atlanta, GA.
• Burke, R. (2005). Retail Shoppability: A Measure of the World's Best Stores Future Retail Now: 40 of the World's Best Stores. Retail Industry Leaders Association, Washington, DC, 206-219.
• Chandon, P., Hutchinson, J., Joung, W., & Scott, H. (2002). Unseen Is Unsold: Visual Equity with Commercial Exe-Tracking Data. Fontainebleau: INSEAD.
• Chen, X., Li, Y., & Hu, T. (2015). Solving the Supermarket Shopping Route Planning Problem Based on Genetic Algorithm. 2015 IEEE/ACIS 14th International Conference on Computer and Information Science (ICIS), 529-533.
• Corstjens, M., & Doyle, P. (1981). A Model for Optimizing Retail Space Allocations. Management Science, 27(7), 822-833.
• Dantzig, G., Fulkerson, R., & Johnson, S. (1954). Solution of A Large-Scale Travelling Salesman Problem. Operations Research, 2(3), 393–410.
• Dorismond J.P. (2019). Data-Driven Models for Promoting Impulse Items in Supermarkets. PhD Thesis, The State University of New York.
• Eilon, S., Watson-Gandy, C. D. T., & Christofides, N. (1971). Distribution Management: Mathematical Modelling and Practical Analysis. London: Griffin.
• Flamand, T., Ghoniem, A., & Maddah, B. (2016). Promoting Impulse Buying by Allocating Retail Shelf Space to Grouped Product Categories. Journal of Operational Research Society, 67, 953-969.
• Fiechter, C. N. (1994). A Parallel Tabu Search Algorithm for Large Traveling Salesman Problems. Discrete Applied Mathematics, 51(3), 243-267.
• Garip, E., & Ünlü, A. (2011). Mağaza Yerleşim Düzeninin Tüketici Davranışına Etkileri: Bir Teknomarket Örneği. İTÜ Dergisi, 10(1).
• Gue, K.R., & Meller, R.D. (2009). Aisle Configurations for Unit-Load Warehouses. IIE Transactions, 41(3), 171-182.
• Harrel, G. D., Hutt, M. D., & Anderson, J. C. (1980). Path Analysis of Buyer Behaviour Under Conditions of Crowding. Journal of Marketing Research, 17(1), 45–51.
• Hui, S. K., Fader, P. S., & Bradlow, E. T. (2009). The Travelling Salesman Goes Shopping. The Systematic Deviations of Grocery Path from TSP Optimality, Marketing Science, 28(3), 566–572.
• Hui, S.K., Inman J.J., Huang Y., & Suher J. (2013). The Effect of in-Store Travel Distance on Unplanned Spending: Applications to Mobile Promotion Strategies. Journal of Marketing, 77(2), 1–16.
• 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, 122-136.
• Jianzhong, W., & Tang, H. (2011). A Fast Algorithm for Solving TSP problem. Journal of Microellectronics & Computer, 28(1), 7-10.
• Kaewyotha, J., & Songpan, W. (2018). A Study on the Optimization Algorithm for Solving The Supermarket Shopping Path Problem. 3rd International Conference on Computer and Communication Systems (ICCCS).
• Klabjan, D., & Pei, J. (2010). In-Store One-To-One Marketing. Journal of Retailing and Consumer Services, 18(1), 64–73.
• Knox, J. (1994). Tabu Search Performance on the Symmetric Traveling Salesman Problem. Computers & Operations Research, 21(8), 867-876.
• Kollat D.T., & Willett R.P. (1967). Customer Impulse Purchasing Behavior. Journal of Marketing Research, 21–31.
• Kundu, A., & Dan, P. (2012). Metaheuristic in Facility Layout Problems: Current Trend and Future Direction. International Journal of Industrial and Systems Engineering, 10 (2), 238-253
• Larson, J.S., Bradlow, E.T., & Fader, P.S. (2005). An Exploratory Look at Supermarket Shopping Paths. International Journal of Research in Marketing, 22(4), 395–414.
• Lenstra, J.K., & RinnooyKan, A.H.G. (1975). Some Simple Applications of the Travelling Salesman Problem. Operations Research Quarterly, 26, 717–733.
• Levy, M., &Weitz, B. A. (1998). Retail Management. 3rd edition, McGraw-Hill.
• Lewison, D. (1994). Retailing, Upper saddle river. Prentice Hall, 277-278.
• Lu Y., & Seo H.-B. (2015). Developing Visibility Analysis for a Retail Store: A Pilot Study in a Bookstore Environment and Planning B. Planning and Design, 42, 95-109.
• Malek, M., Guruswamy, M., Pandya, M., & Owens, H. (1989). Serial and Parallel Simulated Annealing and Tabu Search Algorithms for the Traveling Salesman Problem. Annals of Operations Research, 21(1), 59e84.
• Mawdesley, M.S., Al-Jibouri, S.H., & Yang, H. (2002). Genetic Algo Rithms for Construction Site Layout in Project Planning. Journal of Construction Engineering and Management, 128(5), 418–426.
• Miao, Y., & Cheng, X. (2013). Improved Compact Genetic Algorithm for Solving Traveling Salesman Problem. Microellectronics & Computer, 30(8), 7-12.
• Meller, R.D., & Gau K.Y. (1996). The Facility Layout Problem: Recent and Emerging Trends and Perspectives. Journal of Manufacturing Systems, 15(5), 351-366.
• Mitchell, P. (2008). Discovery-Based Retail. Bascom Hill Publishing Group, Minneapolis, MN.
• Mowrey, C. H., Parikh, P. J., & Gue, K. R. (2018). A Model to Optimize Rack Layout in A Retail Store. European Journal of Operational Research, 271(3), 1100–1112.
• Mowrey, C. H., Parikh, P. J., & Gue, K. R. (2017). The İmpact of Rack Layout on Visual Experience in A Retail Store. INFOR: Information Systems and Operational Research, 1–24.
• Papadimitriou, C. H. (1977). The Euclidean Travelling Salesman Problem is NP-Complete, Theory. Computer Scence, 4(3), 237–244.
• Park, C. W., Iyer, E. S., & Smith, D. C. (1989). The Effects of Situational Factors on in-Store Grocery Shopping Behaviour. The Journal of Consumer Research, 15(4), 422–433.
• Peters, B. A., Klutke, G.A., & Botsali, A. R. (2004). Research Issues in Retail Facility Layout Design. Progress in Material Handling Research, 399–414.
• Shankar, V., Inman, J.J., Mantrala, M., Kelley, E., & Rizley, R. (2011). Innovations in Shopper Marketing: Current Insights and Future Research Issues. Journal of Retailing, 87(1), 29-42.
• Singh, S.P., & Sharma, R.K. (2006). A Review of Different Approaches to the Facility Layout Problems. International Journal of Advanced Manufacturing Technology, 30(5/6), 425-433.
• Sorensen, H. (2003). The Science of Shopping. Marketing Research, 15(3), 30–35.
• Sunderesh, S., & Heragu Kusiak, A. (1991). Efficient Models for the Facility Layout Problem. European Journal of Operational Research, 53(1), 1–13.
• Tompkins, J.A. (2003). Facilities Planning. 3rd edition, John Wiley & Sons, Inc.
• Turley, L.W., & Milliman, R.E. (2000). Atmospheric Effects on Shopping Behavior: A Review of the Experimental Evidence. Journal of Business Research, 49(2), 193-211.
• Yapicioglu, H., & Smith, A.E. (2012). Retail Space Design Considering Revenue and Adjacencies Using A Racetrack Aisle Network. IIE Transactions, 44(6), 446-458.
• Yingying, Y., Chen, Y., & Li, T. (2014). Improved Genetic Algorithm for Solving Traveling Salesman Problem. Control and Decision, 29(8), 1483-1488.
• Zhongwei, L. (2008). Improved Evolutionary Algorithm and its Application in Traveling Salesman Problem. Chongqing University.