Performance-driven support engineering
In complex environments, every design choice can affect the future support of a system: maintenance, spare parts, support resources, skills, inventory, infrastructure, and logistics flows.
That is why we integrate support considerations into projects as early as possible, using a concurrent engineering approach. This approach makes it possible to identify constraints, assess different scenarios, and measure their impact on operational and economic performance.
We work in particular to:
- allocate, specify, and demonstrate support requirements;
- analyze maintenance and Supply Chain requirements;
- assess the impact of design choices on support;
- model the performance of a system and its logistics organization;
- simulate different support scenarios;
- identify improvement levers for operational availability;
- capitalize on and leverage lessons learned (REX).
This approach makes it possible to move from a reactive approach to one focused on anticipating and optimizing support.
Optimize operational availability
Operational availability is a key indicator of a system’s performance. It depends directly on the ability to keep the system operational, have the necessary resources available, and reduce downtime.
Our approach involves analyzing all the factors that may influence this availability: system architecture, maintenance policy, testability and reparability, procurement lead times, spare parts stocks, support resources, or the organization of maintenance activities.
Through modeling and simulation, different scenarios can be compared before they are implemented. This makes it possible to identify the configurations that offer the best balance between service level, availability, resources deployed, and costs.
This approach can help answer practical questions, including:
- What level of spare parts is needed to achieve the target availability?
- What maintenance resources should be planned for?
- What impact does a procurement lead time have on availability?
- What maintenance policy can improve overall performance?
- How can support resources be sized to meet operational needs?
Rationalize spare parts and support resources
Stocks and support resources account for a significant share of the costs associated with a system’s life cycle. Oversizing them generates unnecessary expenses; undersizing them can, conversely, reduce operational availability.
Our expertise helps identify the right level of support through trade-off analyses between costs and availability.
We assess different scenarios to rationalize:
- spare parts stocks and item references;
- stock levels;
- maintenance resources;
- support equipment;
- the resources required for operations;
- logistics and supply capabilities.
Modeling and simulation make it possible to test assumptions, measure the consequences of choices, and provide an objective basis for decisions.
The goal is simple: avoid deploying resources where they do not create value, while ensuring the expected performance in the field.
Manage the cost of ownership throughout the life cycle
The cost of a system is not limited to its acquisition. Maintenance, spare parts, infrastructure, human resources, supplies, and support operations all contribute to the cost of ownership.
An effective support strategy must therefore take into account the economic consequences of decisions from the earliest phases.
We support our clients in identifying the main cost drivers and evaluating scenarios that can reduce the system’s total cost without compromising its performance.
This life-cycle cost approach makes it possible, in particular, to compare different architectures, maintenance policies, or logistics organizations in order to select the most appropriate solutions.
Reduce the logistical and environmental footprint
Operational performance and environmental performance are now closely linked.
Reducing logistics flows, optimizing inventory, limiting travel, improving maintainability, or extending equipment service life are all improvement levers that can simultaneously reduce costs and environmental impact.
We incorporate these considerations into our support analyses to identify opportunities to reduce the logistics footprint and environmental impact.
Concurrent engineering plays an essential role here: by addressing technical, operational, logistics, economic, and environmental considerations from the design stage, it helps bring out more consistent and sustainable solutions.
Leveraging lessons learned
A system evolves over the course of its operation. Available data, events encountered in the field, and maintenance practices are a valuable source of improvement.
Lessons learned (REX) help identify gaps between expected performance and performance actually observed, then turn these insights into concrete actions.
We incorporate this approach into the continuous improvement of support in order to:
- identify the causes of performance degradation;
- measure the effectiveness of maintenance policies;
- adjust resource sizing;
- optimize stocks and support resources;
- inform design or upgrade decisions;
- sustainably improve operational availability.
Support thus becomes a dynamic process, able to adapt to operational realities and changes to the system.
