In both the defense and civil sectors, every decision impacts both costs and operational performance. A rigorous analytical approach is essential to ensure an optimal balance between efficiency and cost-effectiveness throughout the lifecycle of operational civil and military systems.  

The effectiveness of a system depends on its technical performance and availability. However, availability is often the primary driver of hidden costs: a system rendered unusable due to failures or logistical delays compromises mission success. Three key factors influence availability: reliability (failure frequency), maintainability (repair speed), and support efficiency (management of parts, tools, and personnel).  

An integrated approach to support (Integrated Logistics Support, ILS) and system design is crucial. Rather than separating system manufacturing from maintenance, collaboration between manufacturers and operators from the design phase helps optimize logistics and prevent unnecessary costs. The use of advanced analytical tools, such as predictive models and decision-support software, facilitates the assessment of trade-offs between cost and performance. 

Conversely, poorly informed decision-making can result in costly and inefficient systems, inadequate maintenance strategies, and logistical challenges that affect operational availability. In a context of budget constraints, the goal is to maximize performance for every investment, thereby ensuring the success of both civil and military operations.  

A system is defined as any operational entity or component fulfilling a mission or operational function. This can range from a nuclear power plant, a train, an aircraft, or a land vehicle to a subsystem of these entities. These systems operate within specific environments and usage scenarios. As a result, even identical systems may exhibit different cost/performance ratios depending on the environment and operational conditions. More than ever, data intrinsic to the system, as well as environmental and operational scenario data, are critical in guiding decision-making.