AI-Based Demand Forecasting and an Intelligent Sustainable Inventory Model for Green Deteriorating Products
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Abstract
The increasing need for green products and the unpredictable nature of the market demands have posed serious challenges in dealing with deteriorating green products. Traditional inventory theories may not consider AI demand prediction, especially where demand varies. To solve this problem, this study formulates an intelligent sustainable inventory model using AI-based demand prediction for deteriorating green products. The model aims to determine optimal inventory policies by evaluating relevant inventory costs, including ordering, holding, and deterioration costs. AI-based forecasting predicts the demand needed for making inventory decisions. The effect of important variables on the efficiency of the model is investigated through numerical experiments and sensitivity analysis. The study also investigates the impact of demand forecasting, inventory, and deterioration losses. The proposed framework establishes an integrated approach to intelligent inventory planning by connecting AI-based demand prediction with sustainable inventory optimization. This is because it allows analysis of how better forecasts can help with replenishment, waste reduction, and efficiency. The current study makes a valuable contribution to green inventory management through integration of artificial intelligence-based forecasting into the optimal design of inventory for deteriorating items.
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