AI-integrated order consolidation for enhancing same-day delivery efficiency

The rapid growth of e-commerce has increased the demand for efficient and timely order fulfilment. Same-day delivery has become essential for meeting customer expectations and maintaining profitability. Traditional methods often struggle with balancing fast delivery and cost-effectiveness, especially when handling diverse product assortments and fluctuating demand patterns. Our approach leverages AI-based models to predict order patterns, enabling dynamic and real-time order consolidation strategies that reduce overall operational costs. The AI-based decision-making model dynamically consolidates orders based on proximity and delivery windows by analyzing historical data and real-time customer behaviour. The experimental analysis demonstrates that the proposed approach not only reduces the operational costs but also decreases the number of delivery trips required by consolidating orders, and contributes to a more sustainable and cost-effective supply chain. The online grocery retailing data set used to validate our approach shows that the proposed model can capture almost 90% of multi-orders and decrease order fulfilment costs by 4.29% against the company's existing threshold policy, which transforms to over 15 million USD annually. This study provides a comprehensive analysis of the system's performance, highlighting its potential to revolutionise same-day delivery logistics in the online sector.