Authors - Shripad Kanakdande, Atharva Kanherkar, Ayush Dhoot, P.B.Tathe Abstract - Efficient inventory forecasting and waste management are essential for streamlining supply chains and cutting expenses, particularly in sectors like retail and food services where inadequate stock management can lead to large losses and environmental damage. This study presents a data-driven approach to inventory prediction that makes use of sophisticated machine learning models that evaluate past data, sales patterns, and seasonal fluctuations. The model seeks to increase demand forecasting accuracy by utilizing predictive skills, which would ultimately result in improved stock management and customer satisfaction. In order to help organizations reduce waste and increase resource efficiency, it also focuses on improving waste management through real-time monitoring and forecasting of surplus inventory. Furthermore, combining sustainable practices with predictive analytics promotes long-term corporate viability while minimizing environmental harm. In addition to increasing operational effectiveness, this all-encompassing strategy supports more general environmental sustainability goals. The suggested framework gives businesses a practical way to optimize and streamline their supply chain operations while fulfilling sustainability goals by offering a complete solution that can minimize the ecological footprint and the costs associated with keeping inventory.
Open Zoom
