To support its clients along these business areas, Sopra Steria has developed multiple AI assets and accelerators that are used as catalysts to reduce project development cost and accelerate their time-to-market.
#1: Uncertainty management
By nature, the visibility on unscheduled MRO operations and post-sales activities is low and quite difficult to improve, as customer requests may be due to unexpected breakdowns, failures, or other unplanned exogenous events. The field of predictive maintenance,
which matured thanks to AI, provides a first step to improve visibility thanks to better forecasts.
However, predictive maintenance does not provide any type of best practice or next best action for all the related operations that have still to be organized, planned or performed and that are quite complex, in particular in a fuzzy environment. AI can
bring further value to predictive maintenance by helping Supply Chain Practitioners understand the uncertainty around their decisions, and help them take action by offering explicit recommendations. Typical AI algorithms used for that purpose may
include stochastic optimization, dynamic programming or reinforcement learning.
For instance, Sopra Steria has developed innovative algorithms to optimize buffer stock levels and positioning for spare parts using stochastic optimization. This approach helps reduce both immobilized costs and improve the availability ratio of spare
parts with respect to unscheduled maintenance events.
#2: Allocation & constraint management
In the specific field of post-sales management, several key business processes are generally highly constrained by standard and norms, the functional specificities of the manufactured components and their industrial environment. These constraints imply that some soft rules or hard rules have to be implemented; soft rules meaning that some priorities or choices can be rearranged to a certain extent, hard rules meaning that operational parameters at stake absolutely cannot go beyond a certain threshold.
Such constraints create for Supply Chain Practitioners and in particular Supply Chain Managers a very challenging environment for decision-making, in particular in operations planning and production or inventory allocations.
For planning, an illustrative example is the maintenance operations planning, where a large number of industrial constraints have to be respected – capacities, tooling, availability of machines, etc. – along with the human resource constraints, especially HR planning and skills availability for maintenance operations. The choice of an operations plan is consequently often impossible to make without an efficient dedicated AI-based decision tool.
For allocation problems, another illustrative example is the re-use of repaired spare parts. Depending on the spare part itself, the configuration of the parent component and the maintenance operations to be performed, the spare part may not be compatible with all types of aircrafts. Moreover, the allocation priority can change depending on the final customer according to various contractual clauses. As a consequence, this inventory allocation may become a very large combinatorial problem, impossible to be solved manually without making assumptions and simplifications.
Sopra Steria has developed optimization algorithms that drastically accelerate the time required to solve this type of allocation problems, both efficiently and systematically able to reach a solution.
#3: End-to-end optimization and global monitoring
By generating new flows of raw requirements, post-sales activities such as MRO generate new Supply Chain Operations that need to be properly managed. Indeed, new networks need to be continuously designed, products and component have to be delivered and inventories need to be maintained. All these Supply Chain operations may interfere with upstream product flows, meaning the Supply Chain that enables the delivery of new manufactured products.
The power of modelling providing by AI allows to take into account the complete end-to-end vision of the product flows, including post-sales operations. In that way, both downstream and upstream flows are taken into account and optimized simultaneously. Such global approach leveraged by AI reduces the risk of local optimizations, and is a great driver for cost control and complexity management.
Sopra Steria has developed assets for an end-to-end visibility Supply Chain cockpit including MRO activities and planning optimization to smooth out activities and improve overall performance with downstream post-sales logistics.
Benefits
Sopra Steria has helped several aeronautics manufacturer in the implementation of AI-based decision support tool for MRO Supply Chain. For instance, Sopra Steria has developed an aircraft fleet management solution that takes into account maintenance operations, enabling the optimization of related operations and in particular logistics.
Sopra Steria also worked on a decision support tool to schedule the activities of end-to-end supply chain including post sales operations to reduce the entire cycle time and improve service level. As an order of magnitude, Sopra Steria estimates, based on its analyses and references, the following benefits related to MRO Supply Chain thanks to its aforementioned assets.