AI-Based Mathematical Modelling for Sustainable Business and Inventory Management

The paper focuses on the elaboration and application of AI-based mathematical models for sustainable inventory management, which have the full potential to improve both operational efficiency and environmental performance. The integration of machine learning algorithms, optimization techniques, and real-time data in the proposed models will help improve accuracy in demand forecasting, diminish waste in inventory, and reduce carbon emissions within supply chains. Indeed, a 20-25% improvement in demand forecasting, a reduction of inventory costs by 15%, and waste generation as high as 30%. Furthermore, AI-driven sustainability metrics, such as the efficiency of transport and energy consumption, have been implemented that show quantifiable measures of carbon footprint reduction for the activities involved in managing inventories. The findings hence indicate that AI provides a transformative way of associating business goals and objectives with those that are sustainable. Conclusions are drawn on broader implications for business sustainability and point toward further research that may aim to optimize the role of AI in sustainability and business operations.