AI-driven demand forecasting for sustainable inventory model in fuzzy environment

Inventory management in the present world’s supply chains has to overcome challenges such as uncertain product demand, defective items, and market-related uncertainties. Artificial Intelligence (AI) and advanced models that use fuzzy logic offer solutions to these challenges. This research showcases a framework that uses Random Forest to predict a product’s demand based on the past sales data, capturing seasonal variability, growth and other factors such as holidays, discount offers, etc., to improve inventory planning. while using inventory models that use fuzzy logic to account for uncertainty in the amount of defective items. The proposed model contains various processes, namely item inspection, defective item disposal processes, and backordering, providing a reliable and strong framework for real-world applications. The model promotes sustainability as it also considers the carbon emissions generated during the supply chain. The model is tested using the specified numerical examples. And the effect of varying different parameters on different outputs of the model is examined in sensitivity analysis. Finally, managerial implications contain strategies that will optimize order quantities while reducing costs and maximizing profits for retailers. However, this article’s USP is how AI’s predictive capabilities, typically used in industries like finance, healthcare, and manufacturing, can revolutionize supply chain management.