#1 Uncertainty management: understand and propagate
Modern Supply Chains are based on demand-driven flows. Despite its numerous advantages, plans of demand-driven Supply Chains are naturally very sensitive to demand changes and the interdependency of requirements amplifies their impacts along the upstream
flows, which is also known as the bullwhip effect.
In particular, in the Aerospace and Defence industry, the complexity of the supply & delivery networks and the depth of the bills of materials of standard products increase exponentially these effects in terms of instability. The stakes of uncertainty
management are hence of paramount importance, as is the need to apprehend uncertainties within the requirement process.
To that end, Sopra Steria has developed innovative modelling & simulation assets to gain an in-depth understanding of the impacts of uncertainties. Sopra Steria AI algorithms can forecast precisely the demand and in particular its variability by integrating knowledge regarding its nature and origins.
Then, specific proprietary models propagate uncertainties throughout the network, creating a detailed view of the potential risks related to requirement variations. In that sense, Sopra Steria’s AI models bring relevant insights to get a clear vision
of the planning process stakes.
#2 Digital Twin modelling: simulate the end-to-end complete picture
Planning under uncertainty requires clarity on what could happen then finding the best alternatives to a particular adverse situation. Understanding how the global Supply Chain system may respond and evolve through time with respect to various actions
or decision helps provide relevant insights.
This is achievable using a Supply Chain Digital Twin, which provides Supply Chain practitioners with a full model of the Supply Chain processes, their governance and monitoring. Such precise level of digital representation helps simulate scenarios where,
by altering different parameters or decisions, one can find in real-time the best outcome to a challenging situation. This type of simulation is particularly useful under a complex, fuzzy and unpredictable industrial environment and provides significant
value to the overall organization.
Sopra Steria has selected the Supply Chain Digital Twin as a central technology for decision planning in the Aerospace and Defence industry. Whether for capacity management, procurement/production planning, or inventory optimization, Sopra Steria has
developed additional AI-based decision support assets to improve visibility and consistency of decision planning, both on the long-term and for end-to-end scenarios.
#3 Balancing optimisation: organise and plan consistently
Planning challenges are heavily correlated with balancing: supply and delivery plans are typically designed to coordinate internal (or external) resources efficiently in response to a particular demand. In Supply Chain, resources can be production, suppliers,
logistics, people or machines and demand is the delivery of the requested products or components at the right time, place, quantity and quality.
Solving this balancing equation is quite a challenge for any industry and in particular for the Aerospace and Defence industry given its complexity. Indeed, it requires taking into consideration two types of inputs: on one hand specific hard constraints
(such as precedence constraints), and on the other adverse events that may occur randomly (such as a machine breaking down unexpectedly).
The challenge in decision-making for Supply Chain practitioners is to handle these two very different types of inputs on various rolling horizons. Artificial Intelligence is one of the most powerful technologies capable of such feat and Sopra Steria has
developed robust optimization algorithms for optimized production plan that remain consistent within a rolling horizon process. As an example, Sopra Steria has developed a dynamic safety stock optimizer based on dynamic stochastic programming taking
into account uncertainty related to production machines.