An artificial intelligence framework for recycling dormant and obsolete inventory in supply Chains

Abstract

In the automotive sector, excess inventory increases costs and prevents progress toward Sustainable Development Goals, particularly those related to Industry, Innovation, and Infrastructure (SDG 9) and Responsible Consumption and Production (SDG 12). This study introduces an innovative approach to converting obsolete or recycled dormant inventory into a stock that meets customer demand and is marketable by examining the real case of an automotive manufacturer. The optimization, driven by an artificial intelligence tool, transforms at-risk inventory into demand-responsive stock. Results indicate that the tool can modernize up to 84,31% of the affected inventory, offering substantial benefits, including reduced storage costs and the freeing up of strategic space for new opportunities. This method enhances supply chain resilience and sustainability by reducing waste, improving resource efficiency, and boosting adaptability to disruptions. This paper explores how supply chain innovations in this field address economic, environmental, and social imperatives. It draws on quantitative research into the role of advanced analytics and artificial intelligence technologies in inventory innovation to advance global goals for more resilient and sustainable supply chains.