Received 24.09.2025, Revised 30.01.2026, Accepted 26.03.2026 Published 06.04.2026

A multi-level AI-driven product policy model for retail chains as a conceptual framework for marketing consulting

Olena Bilovodska, Anastasiia Volevakha

The rapid development of omnichannel distribution systems and the increasing need to improve the speed and accuracy of marketing decision-making in product policy create a strong demand for the implementation of algorithmic data analysis methods and artificial intelligence technologies. This study aimed to develop the theoretical and methodological foundations of product policy management in retail enterprises through the design of a conceptual multi-level AI-driven product policy model. The research was based on a combination of general scientific and specialised methods, including analysis and synthesis, comparative analysis, systems analysis, structural-logical modelling, abstraction, and generalisation. The proposed model was conceptualised as an adaptive self-regulating system that integrates data, machine learning algorithms, and managerial decision-making processes and serves as a methodological platform for marketing consulting aimed at implementing intelligence-supported decisions in retail practice. The study provided a theoretical justification for the transition toward AI-driven product policy by identifying the system-forming role of data in marketing decision-making, analysing the capabilities of machine learning algorithms, and specifying directions for their application in demand forecasting, assortment optimisation, and automated inventory management. As a result, a conceptual multi-level model of AI-driven product policy was developed, integrating data infrastructure, machine learning analytics, managerial decision-making, and marketing consulting support into a unified adaptive management cycle. The model functions as an integrated closed-loop system with a feedback mechanism and includes the stages of data collection and integration, algorithmic forecasting and AI-based analytics, managerial decision-making for assortment optimisation and automated inventory management, and KPI-based performance monitoring. The practical significance of the research lies in the possibility of applying the proposed model as a methodological tool for marketing consulting aimed at increasing the adaptability of retail enterprises, reducing operational costs, accelerating inventory turnover, and strengthening competitive positions through data-driven management

marketing decision-making; marketing planning; data-driven management; digital analytics; product portfolio; market-product strategy; omnichannel retail
45-54
Bilovodska, O., & Volevakha, A. (2026). A multi-level AI-driven product policy model for retail chains as a conceptual framework for marketing consulting. Innovation and Sustainability, 6(1), 45-54. https://doi.org/10.31649/vis/1.2026.45

References

  1. Ahmed, R.S., Hasnain, M., Mahmood, M.H., & Mehmood, M.A. (2024). Comparison of deep learning algorithms for retail sales forecasting. ICCK Transactions on Intelligent Systematics, 1(3), 112-126. doi: 10.62762/TIS.2024.300700.
  2. AI-powered automated inventory replenishment software. (n.d.). Retrieved from https://www.leafio.ai/replenishmentsoftware/.
  3. Amosu, O.R., Kumar, P., Ogunsuji, Y.M., Oni, S., & Faworaja, O. (2024). AI-driven demand forecasting: Enhancing inventory management and customer satisfaction. World Journal of Advanced Research and Reviews, 23(2), 708-719. doi: 10.30574/wjarr.2024.23.2.2394.
  4. An, M.J., Jung, S.H., & Lee, D.H. (2025). Demand forecasting in micro-fulfillment centers using association rule-based machine learning. International Journal of Production Economics, 290, article number 109789. doi: 10.1016/j. ijpe.2025.109789.
  5. Ankam, S. (2025). AI-driven demand forecasting in enterprise retail systems: Leveraging predictive analytics for enhanced supply chain. International Journal on Science and Technology, 16(1). doi: 10.71097/ijsat.v16.i1.2644.
  6. Balusani, A., Chowdary, P.J.R., & Paruchuri, B.V.N.P. (2025). Enhancing retail demand forecasting with XGBoost: A comparative study with machine learning and deep learning models. SSRN. doi: 10.2139/ssrn.5274510.
  7. Chowdhury, A.R., Paul, R., & Rozony, F.Z. (2025). A systematic review of demand forecasting models for retail e-commerce enhancing accuracy in inventory and delivery planning. International Journal of Scientific Interdisciplinary Research, 6(1). doi: 10.63125/mbbfw637.
  8. Cillo, P., & Rubera, G. (2025). Generative AI in innovation and marketing processes: A roadmap of research opportunities. Journal of the Academy of Marketing Science, 53, 684-701. doi: 10.1007/s11747-024-01044-7.
  9. Douaioui, K., Oucheikh, R., Benmoussa, O., & Mabrouki, C. (2024). Machine learning and deep learning models for demand forecasting in supply chain management: A critical review. Applied System Innovations, 7(5), article number 93. doi: 10.3390/asi7050093.
  10. End stock guesswork with AI automated replenishment system. (n.d.). Retrieved from https://surl.lu/jazayo.
  11. Fatima, A., & Salam, M.A. (2026). A data-driven predictive framework for inventory optimisation using contextaugmented machine learning models. ArXiv. doi: 10.48550/arXiv.2601.05033.
  12. Fildes, R., Goodwin, P., & Önkal, D. (2019). Use and misuse of information in supply chain forecasting of promotion effects. International Journal of Forecasting, 35(1), 144-156. doi: 10.1016/j.ijforecast.2017.12.006.
  13. From manual work to a scalable and automated replenishment process. (n.d.). Retrieved from https://www.optiply. com/en/use-cases/automate-replenishment.
  14. Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmu, M., & Hussain, A. (2023). Interpreting blackbox models: A review on explainable artificial intelligence. Cognitive Computation, 16, 45-74. doi: 10.1007/s12559023101798.
  15. Hyndman, R.J., & Athanasopoulos, G. (2021). Forecasting: Principles and practice (3rd ed.). Melbourne: OTexts.
  16. Kaleva, H., & Småros, J. (n.d.). Complete guide to machine learning in retail demand forecasting. Retrieved from https:// www.relexsolutions.com/resources/machine-learning-in-retail-demand-forecasting/.
  17. Karim, M.R. (2025). Artificial intelligence-enhanced predictive analytics for demand forecasting in U.S. retail supply chains. American Scholarly Research Conference (ASRC), 1(1), 959-993. doi: 10.63125/gbkf5c16.
  18. Kinha, R. (2025). Big data predictive analytics in retail: Real ROI or hype? Retrieved from https://surl.li/litxtr.
  19. Kostromin, A. (2021). Key aspects of product range management in business organization. Young Scientist, 1(89), 163-166. doi: 10.32839/2304-5809/2021-1-89-34.
  20. Kubyshyna, N. (2019). Management of assortment policy of the enterprise. Economic Bulletin of National Technical University of Ukraine “Kyiv”, 16, 287-300. doi: 10.20535/2307-5651.16.2019.182730.
  21. Kuprenko, V. (2024). Predictive analytics in retail and e-commerce. Retrieved from https://surl.li/avqcfd.
  22. Lytiuha, Yu., & Kotyk, D. (2024). Automation of assortment management: Review of modern software solutions and their advantages. In О.I. Oleksiuk (Ed.), Modern technologies of commercial activity and logistics (pp. 59-61). Kyiv: Kyiv National Economic University named after Vadym Hetman.
  23. Muth, M., Lingenfelder, M., & Nufer, G. (2025). The application of machine learning for demand prediction under macroeconomic volatility: A systematic literature review. Management Review Quarterly, 75, 2759-2802. doi: 10.1007/ s11301-024-00447-8.
  24. Nasseri, M., Falatouri, T., Brandtner, P., & Darbanian, F. (2023). Applying machine learning in retail demand prediction – a comparison of treebased ensembles and LSTM. Applied Sciences, 13(19), article number 11112. doi: 10.3390/app131911112.
  25. Naumenko, M. (2024). Big data analysis and analytics in marketing and retail of competitive enterprise. Grail of Science, 40, 117-128. doi: 10.36074/grail-of-science.07.06.2024.013.
  26. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1, 206-215. doi: 10.1038/s42256-019-0048-x.
  27. Sahubar, S.M., Rosli, N.S., Faizal, D.R., & Azni, N.S. (2025). Rethinking inventory intelligence: A conceptual model in adopting AI-based demand forecasting within Malaysian retail supply chains. International Journal of Research and Innovation in Social Science, 9(7), article number 4667. doi: 10.47772/ijriss.2025.907000377.
  28. Smart merchandising & multichannel fulfillment: Platforms & services. (n.d.). Retrieved from https://www. increff.com/.
  29. Suganthi, D., Hema Sree, G., Monika, C., & Nithya Shree, K. (2025). From ARIMA to LSTM: Evaluating traditional and AI-based models for accurate retail sales forecasting. International Journal for Research in Applied Science and Engineering Technology, 13, 1756-1760.
  30. Tarallo, I., Akabane, G.K., Shimabukuro, C.I., Mello, J., & Amancio, D. (2019). Machine learning in predicting demand for fast-moving consumer goods: An exploratory research. IFAC-PapersOnLine, 52(13), 737-742. doi: 10.1016/j. ifacol.2019.11.203.
  31. Teixeira, A.R., Ferreira, J.V., & Ramos, A.L. (2025). Intelligent supply chain management: A systematic literature review on artificial intelligence contributions. Information, 16(5), article number 399. doi: 10.3390/info16050399.
  32. Wang, B., & Zain, A.B.M. (2025). A hybrid XGBoostLSTM framework for supply chain demand forecasting: Empirical evidence from retail multistore data. Journal of Cultural Analysis and Social Change, 10(4), 4056-4073. doi: 10.64753/ jcasc.v10i4.3736.
  33. Xu, L., Almahri, S., Mak, S., & Brintrup, A. (2023). Multi-agent systems and foundation models enable autonomous supply chains: Opportunities and challenges. IFAC-PapersOnLine, 58(19), 795-800. doi: 10.1016/j.ifacol.2024.09.200.
  34. Yu, Y., Wang, B., & Zheng, S. (2024). Data-driven product design and assortment optimisation. Transportation Research Part E: Logistics and Transportation Review, 182, article number 103413. doi: 10.1016/j.tre.2024.103413.
  35. Zulfia, A., Ilfa, T.N., Damia, Z., Sukiman, T.S.A., & Karima, A. (2025). AI decision support for demand forecasting and retail stock using Random Forest. Brilliance: Research of Artificial Intelligence, 5(2), 800-805. doi: 10.47709/ brilliance.v5i2.5901.