Simulation has already revolutionized the supply chain industry, particularly in material handling and manufacturing, where internal goods flow management relies heavily on simulation models. Today, these models extend across multiple production sites, enabling integrated and efficient operations. Smart Industry initiatives further enhance this by facilitating programmable manufacturing lines where production stages communicate seamlessly. In complex assembly lines with multiple suppliers, ship-to-line logistics has become a standard practice.

Optimizing logistics—covering warehouse space utilization, cycle times, time-critical deliveries, loading, transport costs, and more—is achievable through simulation. However, a key challenge remains: various stages, sites, machines, and transport systems are often managed by different entities and suppliers. Since these elements are not always part of a unified simulation model, fine-tuning is essential to ensure accuracy. To achieve this, models must be parametrically adjustable, tuned by domain experts, and supported by strong data-sharing collaboration across the value chain. This need for seamless data exchange becomes even more critical with AI integration.

With advancements in AI, we will soon be able to create highly accurate digital twins of complex logistics flows—both within individual companies and across entire supply chains. These digital twins will not only drive efficiency and support risk analysis but also act as real-time decision-making companions during supply chain disruptions. Beyond optimizing logistics, AI-powered simulation will contribute to sustainability by tracking CO₂ footprints, improving reliability and flexibility, and ensuring compliance with regulations.