Abstract
Despite the longstanding recognition of the importance of product assortment planning (PAP), existing literature has failed to provide satisfactory solutions to a great deal of problems that reside in this area of research. The issue of optimal assortment planning in the retail sector becomes even more important in periods of economic crisis, as retailers must adapt their product portfolios to new evolving patterns of consumer buying behaviour and reduced levels of consumer’s purchasing power. Private labels (PLs) typically experience significant growth in times of recession, due to their low prices, and the reduced disposable income of households. In this direction, the present paper introduces differential evolution to assist retailers in adapting their product portfolios in periods of economic recession and facilitate strategic PAP decisions, related to (a) optimal variety of PL product categories, (b) optimal service level of PL merchandise within a product category, and hence, (c) optimal balance between PLs and National Brands in a retailer’s product portfolio. The interrelated issue of assortment adaptation across different store formats is also considered. Economic recessions contribute to the prolonged upward evolution in PL share, and hence, our mechanism facilitates decisions that are nowadays more important than ever before. The proposed mechanism is illustrated through an implementation to an empirical dataset derived from a random sample of 1928 consumers who participated in a large-scale computer assisted telephone survey during the current economic crisis period.
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References
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.
Bai, R., & Kendall, G. (2005). An investigation of automated planograms using a simulated annealing based hyper-heuristics. In T. Ibaraki, K. Nonobe, & M. Yagiura (Eds.), Metaheuristics: progress as real problem solvers, operations research/computer science interfaces (Vol. 32, pp. 87–108). Berlin, Heidelberg, New York: Springer.
Boatwright, P., & Nunes, J. C. (2001). Reducing assortment: An attribute-based approach. Journal of Marketing, 65(3), 50–63.
Borin, N., & Farris, P. (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 Sciences, 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 Proceedings of the ACM Seventh International Conference on Knowledge Discovery and Data Mining (KDD-2000) (pp. 300–304), New York: ACM Press.
Brijs, T., Swinnen, G., Vanhoof, K., & Wets, G. (1999). Using association rules for product assortment decisions: a case study. In KDD-99 (pp. 254–260). San Diego, CA, USA.
Bultez, A., & Naert, P. (1988). SHARP: Shelf allocation for retailers’ profit. Marketing Science, 7(3), 211–231.
Bultez, A., Naert, P., Gijbrechts, E., & Abeele, P. V. (1989). Asymmetric cannibalism in retail assortment. Journal of Retailing, 65(2), 153–192.
Cachon, G. P., Terwiesch, C., & Xu, Y. (2005). Retail assortment planning in the presence of consumer search. Manufacturing & Service Operations Management, 7(4), 330–346.
Campo, K., Gijsbrechts, E., & Nisol, P. (2003). The impact of retailer stockouts on whether, how much, and what to buy. International Journal of Research in Marketing, 20(3), 273–286.
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.
Das, S., & Suganthan, P. N. (2011). Differential evolution: A survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation, 15(1), 4–31.
Dhar, S. K., Hoch, S. J., & Kumar, N. (2001). Effective category management depends on the role of the category. Journal of Retailing, 77(2), 165–184.
Engelbrecht, A. P. (2007). Computational intelligence: An introduction. New York: Wiley.
Fadılog̃lu, M. M., Karaşan, O. E., & Pınar, M. Ç. (2010). A model and case study for efficient shelf usage and assortment analysis. Annals of Operations Research, 180(1), 105–124.
Fitzsimons, G. J. (2000). Consumer response to stockouts. Journal of Consumer Research, 27(2), 249–266.
Gómez-Suárez, M. (2005). Shelf space assigned to store and national brands: A neural networks analysis. International Journal of Retail & Distribution Management, 33(11), 858–878.
Hansen, P., & Heinsbroek, H. (1979). Product selection and space allocation in supermarkets. European Journal of Operational Research, 3(6), 474–484.
Herstein, R., & Gamliel, E. (2006). The role of private branding in improving service quality. Managing Service Quality, 16(3), 306–319.
Hopp, W. J., & Xu, X. (2008). A static approximation for dynamic demand substitution with applications in a competitive market. Operations Research, 56(3), 630–645.
Hübner, A. H., & Kuhn, H. (2012). Retail category management: State-of-the-art review of quantitative research and software applications in assortment and shelf space management. Omega, 40(2), 199–209.
Islam, S. M., Das, S., Ghosh, S., Roy, S., & Suganthan, P. N. (2012). An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 42(2), 482–500.
Iyengar, S. S., & Lepper, M. R. (2000). When choice is demotivating: Can one desire too much of a good thing? Journal of Personality and Social Psychology, 79(6), 995.
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simmulated annealing. Science, 220(4598), 671–680.
Kök, A. G., & Fisher, M. L. (2007). Demand estimation and assortment optimization under substitution: Methodology and application. Operations Research, 55(6), 1001–1021.
Kök, A. G., Fisher, M. L., & Vaidyanathan, R. (2005). Assortment planning: Review of literature and industry practice. In N. Agrawal & S. Smith (Eds.), Retail supply chain management. Amsterdam: Kluwer.
Krink, T., Mittnik, S., & Paterlini, S. (2009). Differential evolution and combinatorial search for constrained index-tracking. Annals of Operations Research, 172(1), 153–176.
Lamey, L., Deleersnyder, B., Dekimpe, M. G., & Steenkamp, J. B. E. (2007). How business cycles contribute to private-label success: Evidence from the United States and Europe. Journal of Marketing, 71(1), 1–15.
Lampinen, J., & Storn, R. (2004). Differential evolution. In G. C., Onwubolu et al. (Eds.), New optimization techniques in engineering (pp. 123–166). Springer: Berlin, Heidelberg.
Lampinen, J., & Zelinka, I. (1999). Mixed integer-discrete-continuous optimization by differential evolution, Part 1: the optimization method. In Pavel Ošmera (Ed.), Proceedings of MENDEL’99, 5th international mendel conference on soft computing, June 9–12 (pp. 71–76). Brno, Czech Republic.
Li, Z. (2007). A single-period assortment optimization model. Production and Operations Management, 16(3), 369–380.
Lieckens, K., & Vandaele, N. (2015). Differential evolution to solve the lot size problem in stochastic supply chain management systems. Annals of Operations Research, 224(1), 1–25.
Mahajan, S., & van Ryzin, G. (2001). Stocking retail assortments under dynamic consumer substitution. Operations Research, 49(3), 334–351.
Mahajan, S., & van Ryzin, G. J. (1998). Retail inventories and consumer choice. In S. Tayur, R. Ganeshan, & M. Magazine (Eds.), Quantitative methods in supply chain management. Amsterdam: Kluwer.
Mantrala, M. K., Levy, M., Kahn, B. E., Fox, E. J., Gaidarev, P., Dankworth, B., et al. (2009). Why is assortment planning so difficult for retailers? A framework and research agenda. Journal of Retailing, 85(1), 71–83.
Mezura-Montes, E., Velázquez-Reyes, J., & Coello Coello, C. A. (2006). A comparative study of differential evolution variants for global optimization. In Maarten Keijzer, et al. (Eds.), Genetic and evolutionary computation conference. New York: ACM Press.
Mohamed, A. W., & Sabry, H. Z. (2012). Constrained optimization based on modified differential evolution algorithm. Information Sciences, 194, 171–208.
Nandan, S., & Dickinson, R. (1994). Private brands: Major brand perspective. Journal of Consumer Marketing, 11, 18–28.
Nogales, A. F., & Gómez-Suárez, M. (2005). Shelf space management of private labels: A case study in Spanish retailing. Journal of Retailing and Consumer Services, 12(3), 205–216.
O’Connell, V. (2008). Reversing field, Macy’s goes local, Wall Street Journal, (April 21), B1.
Price, K., Storn, R., & Lampinen, J. (2005). Differential evolution: A practical approach to global optimization., Natural Computing Series New York, Secaucus, NJ: Springer.
Quelch, J. A., & Harding, D. (1996). Brands versus private labels: Fighting to win. Harvard Business Review, 37, 99–109.
Rajaram, K., & Tang, C. S. (2001). The impact of product substitution on retail merchandising. European Journal of Operational Research, 135(3), 582–601.
Russell, R. A., & Urban, T. L. (2010). The location and allocation of products and product families on retail shelves. Annals of Operations Research, 179(1), 131–147.
Ryzin, G. V., & Mahajan, S. (1999). On the relationship between inventory costs and variety benefits in retail assortments. Management Science, 45(11), 1496–1509.
Shah, J., & Avittathur, B. (2007). The retailer multi-item inventory problem with demand cannibalization and substitution. International Journal of Production Economics, 106(1), 104–114.
Sloot, L. M., & Verhoef, P. C. (2008). The impact of brand delisting on store switching and brand switching intentions. Journal of Retailing, 84(3), 281–296.
Smith, S. A., & Agrawal, N. (2000). Management of multi-item retail inventory systems with demand substitution. Operations Research, 48(1), 50–64.
Storn, R., & Price, K. (1997). Differential evolution: A simple and efficient adaptive scheme for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359.
Urban, T. L. (1998). An inventory-theoretic approach to product assortment and shelf space allocation. Journal of Retailing, 74(1), 15–35.
Xin, G., Messinger, P. R., & Li, J. (2009). Influence of soldout products on consumer choice. Journal of Retailing, 85(3), 274–287.
Yang, M. H. (2001). An efficient algorithm to allocate shelf space. European Journal of Operational Research, 131(1), 107–118.
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.
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Tsafarakis, S., Saridakis, C., Matsatsinis, N. et al. Private labels and retail assortment planning: a differential evolution approach. Ann Oper Res 247, 677–692 (2016). https://doi.org/10.1007/s10479-015-1978-2
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DOI: https://doi.org/10.1007/s10479-015-1978-2