In the highly complex and fast-paced semiconductor industry, automation is essential for achieving operational excellence, cost efficiency, and rapid market responsiveness. However, unlocking the full potential of automation depends critically on selecting the appropriate enabling methods based on the specific problem, available resources, and timing constraints. This presentation introduces three complementary approaches—analytical tools, material flow simulation, and digital twins—highlighting their distinct roles, ideal applications, and synergies. Real-world use cases for each method will demonstrate their practical application and proven feasibility. Analytical tools offer quick, insightful evaluations, making them particularly effective for early-phase assessments or clearly defined challenges. They empower decision-makers to assess process improvements, identify bottlenecks, and estimate expected benefits with minimal time and resource investment. A use case will demonstrate how analytical modeling enabled fast decision-making by quickly estimating the required AMR (Autonomous Mobile Robot) fleet size for a specific transportation task under worst-case, base-case, and best-case scenarios. Material flow simulation delivers a more detailed and dynamic analysis, particularly valuable when system complexity or stochastic variability renders static analysis insufficient. It enables the discovery of hidden inefficiencies and supports safe experimentation with different design alternatives. A case study will demonstrate how material flow simulation was applied to optimize both the automation setup and its control strategies. By simulating various system parameters and control approaches, the study revealed the conditions under which the automation concept could reliably deliver 100% tool production uptime, even under varying operational scenarios. Digital Twins, the most advanced method, create real-time virtual replicas of manufacturing systems by integrating live data streams. They enable continuous prediction, optimization, and adaptation of operations. While requiring greater initial effort, digital twins offer powerful benefits, including predictive maintenance, real-time decision support, and scenario forecasting. An application example from special-purpose machinery manufacturing will show how digital twins enabled the testing of software updates virtually—after a tool had already been delivered to a customer—thus eliminating the need for physical access to the hardware. Selecting the correct method—or a combination of methods across different project phases—is a strategic decision that significantly influences the success of automation initiatives. It requires balancing system complexity, project cost, available time, and business priorities. Relying on inappropriate methods risks delayed benefits, elevated costs, and project underperformance. Based on typical automation projects in the semiconductor industry, this presentation provides a practical framework to guide method selection and implementation strategy. By systematically applying analytical tools, material flow simulations, and digital twins where they fit best, semiconductor manufacturers can substantially enhance the precision, agility, and impact of their automation programs. Concrete examples from real-world projects will make the advantages and limitations of each method tangible and offer actionable guidance for successful application.
