Selecting Optimal Alternatives for Sustainable Intralogistics in Automotive Industry Warehouse Supply Chains

Intralogistics activities within automotive industry warehouses play a critical role in the efficient management of materials, components, and finished goods throughout production and distribution processes.These activities contribute significantly to the optimization of warehouse space, reduction of delivery lead times, and minimization of storage costs.Furthermore, effectively implemented intralogistics processes are essential for ensuring continuous production and the timely delivery of high-quality products to customers.This study focuses on the enhancement of sustainability in intralogistics operations within warehouses by adopting innovative technologies, optimizing resource utilization, and minimizing environmental impact.The automotive industry's supply chain was examined, with particular emphasis on the challenges associated with intralogistics and sustainability.The study explores the optimization of warehouse intralogistics processes, which directly affect operational efficiency, cost reduction, and overall productivity.Based on the identified challenges, three potential solutions-mini-load (ML) Automated Storage and Retrieval System (AS/RS), AutoStore systems (ASSs), and Autonomous Mobile Robots (AMRs)-were analyzed through a sustainability lens.The evaluation of these technologies was conducted with respect to various sustainability criteria, such as energy consumption, space efficiency, cost-effectiveness, and environmental impact.The primary objective is to identify the most suitable solution for improving the sustainability and operational efficiency of intralogistics processes within the automotive industry.By investigating the potential of these innovative technologies, the research aims to provide a comprehensive understanding of how they can address contemporary intralogistics challenges, enhance operational performance, and align with the broader objectives of environmental sustainability and cost optimization.