Pit-Stop Manufacturing: Decision Support for Complexity and Uncertainty Management in Production Ramp-Up Planning

Abstract

The current research presents an extension of the Pit-Stop Manufacturing framework. It addresses the challenges of managing complexity and uncertainty in the production ramp-up phase of manufacturing systems, bridging the gap in existing approaches that lack comprehensive, quantitative, and system-level solutions. This research integrates state-of-the-art methodologies, utilising such metrics as Overall Equipment Effectiveness and Effective Throughput Loss to enhance ramp-up management. The developed framework is represented by a conceptual model, which is translated into a digital product combining multiple artefacts for comprehensive ramp-up research, namely a digital twin of the production system, a Custom Experiment Manager for multiple simulation runs, and a Graph Solver that uses the stochastic dynamic programming approach to address the decision-making issues during the production system ramp-up evolution. This work provides a robust decision-support tool to optimise production transitions under dynamic conditions by combining stochastic dynamic programming and discrete event simulation. The framework enables manufacturers to model, simulate, and optimise system evolution, reducing throughput losses, improving equipment efficiency, and enhancing decision-making precision. This paper demonstrates the framework’s potential to streamline ramp-up processes and boost competitiveness in volatile manufacturing environments.