Engineering Management Strategies for AI-Driven Logistics Systems: Bridging Operational Efficiency and Strategic Alignment

Abstract

The integration of Artificial Intelligence (AI) into logistics operations has revolutionized supply chain management, yet its success depends significantly on effective engineering management. This study proposes a strategic framework that enables engineering managers to lead AI adoption in logistics systems while aligning with organizational goals. By analyzing use cases such as AI-enabled route optimization, dynamic inventory control, and predictive fleet maintenance, the paper identifies critical success factors from a managerial perspective—such as cross-functional collaboration, data infrastructure readiness, and change management. A mixed-method approach is employed, combining qualitative interviews with logistics managers and quantitative analysis of AI system performance in logistics firms. The findings emphasize the engineering manager’s role in selecting the right AI technologies, ensuring seamless integration into legacy systems, and creating feedback loops between AI outputs and business KPIs. The proposed framework offers practical guidance for engineering leaders to scale AI initiatives that enhance logistical efficiency, resilience, and strategic agility.