Decision support algorithm development for assortment optimization in the retail chain
Abstract
As the consumer market changes rapidly, retail networks require a system to optimize the quantity and assortment of goods. The authors develop and test theoretical and practical assortment optimization and distribution principles. The study aims to improve retail assortment management by creating a decision support system for optimizing commodity composition, quantity, and location. The system’s primary objective is to enhance the trading margin obtained from the sale while considering constraints related to commodity resources and shelf space. This entails optimizing the procurement and inventory management processes to maximize the profit margin. By generating freight invoices, distributing, and redistributing commodities within the network under inbound logistics orders, the system optimizes the allocation of commodities using information from the company’s existing software. The authors present an optimization method for commodities that relies on mathematical modeling and the calculation of the consolidated profitability ratio. It ensures the necessary accuracy and provides assortment management within time and cost limits, without substantial investments in equipment and updating qualifications of employees. The research outcomes are applicable to apparel retail. The practical outcome is maximizing the return on investment for goods sold per day. The algorithm’s benefits and effectiveness were calculated based on real data after implementation.
Keyword : retail chain, assortment optimization, assortment management, commodity management, decision support system, apparel retail, sales analytics, business processing
How to Cite
Iurasova, O. (2025). Decision support algorithm development for assortment optimization in the retail chain. Journal of Business Economics and Management, 26(1), 127–144. https://doi.org/10.3846/jbem.2025.22952
This work is licensed under a Creative Commons Attribution 4.0 International License.
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Fisher, M., & Vaidyanathan, R. (2014). A demand estimation procedure for retail assortment optimization with results from implementations. Management Science, 60(10), 2401–2415. https://doi.org/10.1287/mnsc.2014.1904.
Fildes, R., Kolassa, S., & Ma, Sh. (2021). Post-script – Retail forecasting: Research and practice. International Journal of Forecasting, 38(4), 1319–1324. https://doi.org/10.1016/j.ijforecast.2021.09.012
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Hense, J., Hübner, A., & Schäfer, F. (2022). An analytical assessment of demand effects in omni-channel assortment planning. Omega, 115, Article 102749. https://doi.org/10.1016/j.omega.2022.102749
Holmström, J. (1998). Handling product range complexity: A case study on re‐engineering demand forecasting. Business Process Management Journal, 4(3), 241–258. https://doi.org/10.1108/14637159810231027
Hong, J., Liao, Y., Zhang, Y., & Yu, Z. (2019). The effect of supply chain quality management practices and capabilities on operational and innovation performance: Evidence from Chinese manufacturers. International Journal of Production Economics, 212, 227–235. https://doi.org/10.1016/j.ijpe.2019.01.036
Honhon, D., & Seshadri, S. (2013). Fixed vs. Random proportions demand models for the assortment planning problem under stockout-based substitution. Manufacturing & Service Operations Management, 15(3), 378–386. https://doi.org/10.1287/msom.1120.0425
Huang, M., Hao, Y., Wang, Y., Hu, X., & Li, L. (2023). Split-order consolidation optimization for online supermarkets: Process analysis and optimization models. Frontiers of Engineering Management, 10, 499–516. https://doi.org/10.1007/s42524-022-0221-5
Hübner, A. (2017). A decision support system for retail assortment planning. International Journal of Retail & Distribution Management, 45(7–8), 808–825. https://doi.org/10.1108/IJRDM-09-2016-0166
Hübner, A., & Kuhn, H. (2012). Retail category management: a state-of-the-art review of quantitative research and software applications in assortment and shelf space management. Omega, 40(2), 199–209. https://doi.org/10.1016/j.omega.2011.05.008
Hübner, A., Kuhn, H., & Sternbeck, M. (2013). Demand and supply chain planning in grocery retail: An operations planning framework. International Journal of Retail & Distribution Management, 41(7), 512–530. https://doi.org/10.1108/IJRDM-05-2013-0104
Hübner, A., Kuhn, H., & Wollenburg, J. (2016a). Last mile fulfilment and distribution in omni-channel grocery retailing: A strategic planning framework. International Journal of Retail & Distribution Management, 44(3), 228–247. https://doi.org/10.1108/IJRDM-11-2014-0154
Hübner, A., Kühn, S., & Kuhn, H. (2016b). An efficient algorithm for capacitated assortment planning with stochastic demand and substitution. European Journal of Operational Research, 250(2), 505–520. https://doi.org/10.1016/j.ejor.2015.11.007
Hübner, A., & Schaal, K. (2017). Effect of replenishment and backroom on retail shelf-space planning. Business Research, 10, 123–156. https://doi.org/10.1007/s40685-016-0043-6
Hunter, A., King, R., & Nuttle, H. L. W. (1996). Evaluation of traditional and quick-response retailing procedures by using a stochastic simulation model. Journal of the Textile Institute, 87(1), 42–55. https://doi.org/10.1080/00405009608659101
Iurasov, A. (1998). Adaption of logistics merchandise management systems to the conditions of the consumer market [Doctoral dissertation]. Sankt Petersburg State University of Economics and Finance, Sankt-Petersburg, Russia. https://www.dissercat.com/content/adaptatsiya-logisticheskikh-sistem-upravleniya-tovarodvizheniem-k-konyunkture-potrebitelskog
Iurasov, A., Ivashko, L., & Maksymov, O. (2021). Development of decision support algorithms for management. Financial and Credit Activity: Problems of Theory and Practice, 1(36), 260–269. https://doi.org/10.18371/fcaptp.v1i36.227782
Jasińska-Biliczak, A. (2022). E-commerce from the customer panel: The phenomenon of the pandemic increase and future challenge. Business, Management and Economics Engineering, 20(1), 139–151. https://doi.org/10.3846/bmee.2022.16752
Javed, A., Basit, A., Ejaz, F., Hameed, A., Fodor, Z. J., & Hossain, Md B. (2024). The role of advanced technologies and supply chain collaboration: during COVID-19 on sustainable supply chain performance. Discover Sustainability, 5(1), Article 46. https://doi.org/10.1007/s43621-024-00228-z
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Kasprzak, E. (2020). Coronavirus: Supermarket shoppers ‘keep calm’ and queue. BBC News. https://tinyurl.com/fkf3xeby
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