GenAI Technology Approach for Sustainable Warehouse Management Operations: A Case Study from the Automative Sector

The emergence of Generative Artificial Intelligence (GenAI) is reshaping logistics and supply chain operations, offering new opportunities to improve efficiency, accuracy, and responsiveness. In the automotive manufacturing sector, where high-volume throughput and precision are critical, the integration of AI technologies into warehouse management represents a strategic advancement. This study presents a case analysis of the implementation of AI-driven reception processes at an Automotive facility in Blaj, Romania. The research focuses on the transition from manual operations to automated recognition using industrial-grade imaging systems integrated with enterprise resource planning platforms. The integrated approach used combines Value Stream Mapping, quantitative performance analysis, and statistical validation using the Wilcoxon Signed-Rank Test. The results reveal a substantial reduction in reception time up to 79% and significant cost savings across various operational scales with improved data accuracy and minimized logistics failures. To support broader industry adoption, the study proposes a Cleaner Logistics and Supply Chain Model, incorporating principles of sustainability, ethical compliance, and continuous improvement. This model serves as a strategic framework for organizations seeking to align AI adoption with long-term operational resilience and environmental responsibility. The findings validate the operational and financial advantages of AI-enabled warehousing management in achieving sustainable digital transformation in logistics.