A machine learning-based intelligent decision supporting model for E-commerce operations

In today’s dynamic E-commerce environment, efficient inventory management and streamlined logistics pose significant hurdles for online retailers. Online retailers face new daily challenges with transportation, pricing, product recommendations, and inventory management. Making the best judgments to optimize revenue and consumer delight is not straightforward due to the dynamic, complex nature of e-commerce data. Using real-world data, this research compares SDMA-ML to traditional techniques, demonstrating how its intelligent, data-driven tactics can increase revenue, reduce costs, and improve customer satisfaction. The research proposes Smart Decision-Making Aids based on Machine Learning (SDMA-ML) to support data-driven decisions for e-commerce operations. Using cutting-edge machine learning methods, such as reinforcement learning and deep neural networks, this strategy enables data-driven decision-making across key e-commerce areas. The study compares the performance of the SDMA-ML model with baseline methods using real-world e-commerce datasets. Some essential elements include recurrent neural networks for inventory management, deep reinforcement learning for logistics optimization, collaborative filtering for product suggestions, and genetic algorithm pricing optimization. The product recommendations system offers tailored recommendations via collaborative filtering to foster customer loyalty and cross-selling. It uses optimization components that employ genetic algorithms to search for the field’s most lucrative dynamic pricing schemes. The suggested approach outperforms conventional techniques across several decision domains, as demonstrated by tests on real e-commerce datasets. The method efficiently boosts income by making tailored recommendations, improving inventory levels, reducing operating expenses, and increasing profitability through astute pricing. A decision-support system helps online retailers make data-driven decisions and provides a combined AI-powered solution to gain a competitive edge. Not applicable.