Supply Chain Ramp-Up in an Engineer-to-Order Context for Large-Scale Products[Montée en charge d'une chaîne logistique dans un contexte Engineer-to-Order pour des produits de grande taille]

arge-scale products are characterized by significant physical dimensions and high engineering content, and are typically developed in engineer-to-order (ETO) environments. Their architecture comprises numerous modules, subassemblies, and components. The production of these elements relies on extended supply chains composed of specialized suppliers and subcontractors with diverse manufacturing activities and often geographically dispersed operations. Their high structural complexity, highly customized configurations, and distributed production networks limit the ability of these supply chains to adapt efficiently to demand variability, making their management particularly challenging. These products frequently rely on modular and partially standardized architectures to facilitate production, transportation, and storage. Consequently, production and supply capacity planning is especially complex, as planning approaches must accommodate different decoupling points. Customized elements are generally managed using an order-driven logic, whereas modules and standardized components follow a forecast-driven logic based on demand and capacity aggregation [2]. In forecast-driven capacity planning, frameworks such as Sales and Operations Planning (S&OP) are widely used in mass-production environments. However, their application in ETO contexts remains limited. Capacity planning becomes particularly difficult when companies experience rapid market growth or face external disruptions requiring a fast increase in capacity. Such situations involve an accelerated ramp-up of resources throughout the supply chain. This process is even more complex in ETO environments for large-scale products, where production depends on specialized suppliers and subcontractors. These actors must design products, modules, subassemblies, and components while adapting their production systems. Such adaptations require significant investments and lead times, thereby reducing the system's ability to respond quickly to changes in demand and, consequently, compromising supply chain resilience. To support strategic capacity management decisions in ETO supply chains for large-scale products, we propose a methodology based on a linear programming (LP) model that represents resource utilization and enables the analysis of supply chain ramp-up dynamics.