Machine Learning Application for Sales Forecasting and Inventory Optimization in Wholesale Trade

  • Authors

    • Pujan Shailesh Kakkad Senior Systems Development Engineer, Amazon Robotics, Business Applications and Solutions Engineering Team, Austin, Texas
    https://doi.org/10.14419/jnrqjz39

    Received date: July 28, 2025

    Accepted date: September 4, 2025

    Published date: September 20, 2025

  • Machine Learning; Inventory Optimization; Predictive Analytics; Linear Regression; ABC Analysis; Demand Forecasting

  • Abstract

    This work explores the use of machine learning in optimizing inventory management processes, using the case study of Vadodara, Gujarat, the largest wholesaler of plumbing and heating products in the Czech Republic and Slovakia. The work combines financial analysis with predictive analytics to identify opportunities for cost reduction and inventory optimization. A linear regression model was applied to historical sales data of corded and cordless power tools to forecast future demand. The findings suggest a clear trend of increasing sales of cordless tools and stagnating demand for corded tools. Based on this, inventory turnover was analyzed, and an ABC analysis was conducted to identify high-priority stock. Recommendations were made to rebalance inventory investments, enhance stock efficiency, and implement strategic purchasing decisions. The application of machine learning in this context demonstrated measurable benefits in predicting sales patterns and improving inventory planning, ultimately contributing to better cost management.

  • References

    1. Odimarha, A. C., Ayodeji, S. A., & Abaku, E. A. (2024). Machine learning’s influence on supply chain and logistics optimization in the oil and gas sector: A comprehensive analysis. Computer Science and IT Research Journal, 5, 725–740.
    2. Pasupuleti, V., Thuraka, B., Kodete, C. S., & Malisetty, S. (2024). Enhancing supply chain agility and sustainability through machine learning: Op-timization techniques for logistics and inventory management. Logistics, 8(3), 73. https://doi.org/10.3390/logistics8030073
    3. Akbari, M., & Do, T. N. A. (2021). A systematic review of machine learning in logistics and supply chain management: Current trends and future directions. Benchmarking: An International Journal, 28, 2977–3005.
    4. Yang, M., Lim, M. K., Qu, Y., Ni, D., & Xiao, Z. (2023). Supply chain risk management with machine learning technology: A literature review and future research directions. Computers & Industrial Engineering, 175, 108859.
    5. Daios, A., Kladovasilakis, N., Kelemis, A., & Kostavelis, I. (2025). AI applications in supply chain management: A survey. Applied Sciences, 15(5), 2775. https://doi.org/10.3390/app15052775
    6. Sayyad, J., Attarde, K., & Yilmaz, B. (2024). Improving machine learning predictive capacity for supply chain optimization through domain adver-sarial neural networks. Big Data and Cognitive Computing, 8(8), 81. https://doi.org/10.3390/bdcc8080081
    7. Dey, P. K., Chowdhury, S., Abadie, A., Vann Yaroson, E., & Sarkar, S. (2024). Artificial intelligence-driven supply chain resilience in Vietnamese manufacturing small-and medium-sized enterprises. International Journal of Production Research, 62, 5417–5456.
    8. Nimmagadda, V. S. P. (2020). AI-powered predictive analytics for retail supply chain risk management: Advanced techniques, applications, and real-world case studies. Distributed Learning and Broad Applications in Science Research, 6, 152–194.
    9. Pal, S. (2023). Integrating AI in sustainable supply chain management: A new paradigm for enhanced transparency and sustainability. International Journal of Research in Applied Science and Engineering Technology, 11, 2979–2984.
    10. Charles, V., Emrouznejad, A., & Gherman, T. (2023). A critical analysis of the integration of blockchain and artificial intelligence for supply chain. Annals of Operations Research, 327, 7–47.
    11. Curcio, D., & Longo, F. (2009). Inventory and internal logistics management as critical factors affecting the supply chain performances. Interna-tional Journal of Simulation and Process Modelling, 5, 278–288.
    12. Sharma, N., & Singhi, R. (2018). Logistics and supply chain management quality improvement of supply chain process through vendor managed inventory: A QFD approach. Journal of Supply Chain Management Systems, 7, 23–33.
    13. Mahraz, M. I., Benabbou, L., & Berrado, A. (2022). Machine learning in supply chain management: A systematic literature review. International Journal of Supply and Operations Management, 9, 398–416.
    14. Abolghasemi, M., Beh, E., Tarr, G., & Gerlach, R. (2020). Demand forecasting in supply chain: The impact of demand volatility in the presence of promotion. Computers & Industrial Engineering, 142, 106380.
    15. Salamai, A. A., El-Kenawy, E. S. M., & Abdelhameed, I. (2021). Dynamic voting classifier for risk identification in supply chain 4.0. Computers, Materials & Continua, 69(3).
    16. Suwignjo, P., Panjaitan, L., Baihaqy, A., & Rusdiansyah, A. (2023). Predictive analytics to improve inventory performance: A case study of an FMCG company. Operations and Supply Chain Management: An International Journal, 16, 293–310.
    17. Mahraz, M.-I., Benabbou, L., & Berrado, A. (2022). Machine learning in supply chain management: A systematic literature review. International Journal of Supply and Operations Management, 9(4), 398–416.
    18. Charles, V., Emrouznejad, A., & Gherman, T. (2023). A critical analysis of the integration of blockchain and artificial intelligence for supply chain. Annals of Operations Research, 327, 7–47.

  • Downloads

  • How to Cite

    Kakkad, P. S. . (2025). Machine Learning Application for Sales Forecasting and Inventory Optimization in Wholesale Trade. International Journal of Basic and Applied Sciences, 14(5), 753-764. https://doi.org/10.14419/jnrqjz39