https://www.mdu.se/

The manufacturing industry is currently undergoing a rapid transformation driven by increasing demand for mass customization and shorter product lifecycles. Within this context, production ramps up during the transitional phase between initial production and stable, full-capacity operation is a critical driver of competitiveness and financial payback. However, this phase is inherently unstable, particularly in greenfield projects where a significant "data gap" and lack of established routines lead to failure rates of approximately 60%. Current planning methods often struggle to capture the socio-technical complexity of manual mixed-model assembly lines, where human variability and high product diversity create shifting bottlenecks and balancing challenges.

The purpose of this study is to develop and demonstrate a reusable, parameter-driven Discrete-Event Simulation (DES) model to support decision-making during the greenfield ramp-up of a new crawler excavator assembly line at Volvo Construction Equipment (Volvo CE) in Eskilstuna. Following a collaborative case study approach and the established Banks' 12-step simulation methodology, the research utilized triangulation by cross-referencing Gemba observations, semi-structured interviews, and technical benchmarks from international sister plants.

The empirical results, generated through the FACTS Analyzer software, demonstrate that a realistic ramp-up model must explicitly represent station-level cycle times, demand-driven takt time, and time-dependent efficiency (learning curves). The simulation revealed that progressive efficiency gains, from approximately 80% in the first year to 87% in the third year, are fundamental to maintaining target output under increasing demand. Furthermore, the study identifies how strategic buffering and sequencing rules can mitigate the "noise" of manual variability.

The study concludes that DES serves as a robust Decision Support System (DSS) for greenfield projects, enabling managers to evaluate "what-if" scenarios for line balancing and product-mix strategies before physical assets are committed. By providing a virtual instrument to validate operational strategies, this research reduces the strategic and technical risks inherent in complex manufacturing transitions.