Manufacturing industries are undergoing major transformations driven by Industry 4.0, which enables companies to extract actionable knowledge from field data. In this scenario, traditional Manufacturing Execution Systems must evolve toward advanced Production Planning and Control to manage increasingly automated and adaptive shop floors. In the meantime, the automotive sector is being reshaped by the rise of Electric Vehicles, whose production is characterised by high automation, uncertainty in already medium-low volumes. Thus, these production systems remain complex, with non-linear flows and higher defect rates compared to conventional automotive processes.

Motown addresses these challenges by proposing a context-aware short-term decision-making framework for high-quality manufacturing. Digital models are continuously updated from shop-floor data to support real-time decision making and to enhance the entire data chain from sensors to actuators. The project also develops an Internet of Things architecture to maintain alignment between digital representations and the physical shop floor, enabling an effective data-driven approach. Real industrial cases from EV motor production, as well as experiments on a miniaturised physical line, are used to validate the proposed models and algorithms. Expected outcomes include new methods and tools embedded in a real-time IoT-compatible architecture, improving competitiveness and sustainability across manufacturing. Ultimately, Motown supports more efficient EV production and contributes to the broader adoption of electric mobility.