Supply Chain Management in Aerospace: maximising agility with AI-based risk monitoring

by Benoit Spolidor - Head of Artificial Intelligence, Sopra Steria
by Maxime Claisse - Artificial Intelligence Lead (Supply Chain & Optimisation), Sopra Steria
| minutes read

One of the main challenges of today’s Aerospace Supply Chain Practitioners is to manage their operations in such a complex and volatile environment. The Supply Chain purpose of fulfilling customer service promise while controlling costs within the overall industrial chain has become harder, in particular because Aerospace manufacturers are facing a lack of visibility in their supply and delivery processes.

Disruptions are getting more frequent, and yet more difficult to handle and predict. The consequences on the performances of Aerospace manufacturers such as Supply Chain costs, service level, immobilised stock, WIP or CO2 emissions can be devastating given the interdependencies within networks. 

Improving operations by getting end-to-end visibility of the Supply Chain network 

To control and improve the performance of Aerospace manufacturers given this context, the operations management process must be responsive, suited for, and especially consistent with the end-to-end scenario. Indeed, global vision on the entire network avoids local optimisations which, by propagating through side effects or system dynamics, can impact negatively the overall Supply Chain performance.

Sopra Steria has identified two levers to address this issue: 

First, by gaining real-time and predictive end-to-end relevant insights on current supply and delivery information, Supply Chain practitioners can take decisions faster and in a more coherent manner to monitor operations. 

Second, the consistency needed within the decisions must necessarily be based on internal information, but also external. In particular, exogenous risks must be taken into consideration, creating stock-outs and/or unexpected delay which can appear throughout the Supply Chain. The exogenous risks management is therefore essential in flow monitoring: it ensures that Supply Chain Managers have all the required information available at hand so that operations management is resilient to this type of risks.

Sopra Steria’s Artificial Intelligence-based cockpit brings clarity to decision-making 

To help Supply Chain practitioners exploiting these two levers, Sopra Steria has developed specific AI models and assets covering 3 interdependent steps: 

  1. Availability of information for the user, in particular how to capture exogenous risks and gather all the necessary E2E real time insights for his or her decision-making process
  2. Performances prediction and risk propagation along the supply chain network, from the risk detected up to the customer
  3. Recommendations of action plans for the user to optimise key indicators and mitigate risks

Understanding in real-time Supply Chain events and performance with Descriptive AI

The first step consists on being able to collect real-time transactional insights from product flows, inventories, and orders all over the network –both logistics edges and production sites, including suppliers-, and transform this raw data into valuable KPI to understand the current situation rapidly.

To do so, Sopra Steria developed a specific Supply Chain Management Data Platform, including an Aerospace Supply Chain Data Model that leverages information flows coming from different siloted Information Systems by structuring them into a real-time modelling of the Supply Chain. Such approach allows for the exploitation of the model by connecting relevant dashboards for KPI monitoring, finally creating a Supply Chain real-time E2E cockpit. 

Sopra Steria’s proprietary AI Algorithms or integrated from select partners are also able to capture all the exogenous risks that can impact the Supply Chain performance in real-time. These AI algorithms gather and filter public information from open data sources on the Internet (news websites, Twitter, etc…) and identify events that can be a potential source of risks of disruptions within the supply and delivery process, such as natural disasters, fires, traffic congestion, customs regulations, etc. This exogenous real-time risk information is finally ingested into the cockpit to complete the overall Supply Chain supervision.

Users are hence able to take informed decisions based on E2E visibility of both internal and external real-time situations of their Supply Chain. 

Forecasting the propagation of events along the Supply Chain network and their impact on KPIs with Predictive AI

The second step consists of using the power of Artificial Intelligence to get predictive insights on how the Supply Chain performance is evolving. Sopra Steria developed Machine Learning algorithms that allow Supply Chain practitioners to get information on the future trajectory of their KPIs in order to get a better understanding of what will happen next. Beyond predictive KPIs, Sopra Steria uses a Supply Chain Network Digital Twin to simulate the propagation of the impacts of the detected risks along the Supply Chain network. In particular, complex effects such as uncertainty propagation within interdependent networks and their impact on KPI such as customer OTD can at last be accurately modelled and simulated. Users are then able to simulate scenarios to get clear vision of the potential risks impact on the overall Supply Chain performances at any level of its network.

Optimising operations with explicit next best action recommendation with Prescriptive AI 

Visibility, both on current end-to-end situation and on predictive KPIs, is key for decision-making. Beyond visibility, the final step for the AI-based cockpit is then to leverage this information using optimisation algorithms to generate tangible recommendations to users. 

Prescriptive AI algorithms developed by Sopra Steria are able to provide multiple decision recommendations to Supply Chain practitioners, enabling them to take better and faster decisions consistent with the end-to-end situation. For instance, in regards with risk management, mitigation action plans can be tested to understand the best way to handle the disruption captured. By simulating the improvement on Customer KPIs, the AI-based cockpit can optimise action plan parameters and give explicit recommendations of new allocations, routings, or priority choice to the Supply Chain practitioners. They can finally take into account the overall complexity of the global picture for their decision-making. 

A decision making process orchestrated by Supply Chain Practitioners for efficient Risk Management

These 3 steps combined together along with the sequential human decision & interactions create a complete environment to improve the overall agility in operations management. AI can capture the relevant information, predict their impact and help take optimised action as well as provide users with a global vision. This allows for an improved consistency of the information flow within the network; operations are at last managed properly, rapidly and effortlessly.

Sopra Steria Aerospace references and results 

Sopra Steria has assisted several major clients in the Aerospace industry in the functional and technical specifications as well as the implementation of such AI-based systems ranging from Supply Chain cockpit, Predictive KPIs, Risk Management tools to Decision Support Systems for Risk Management as well as their long-term roadmap using their assets. 

Such AI-based systems create significant improvements in the customer service level (5 to 10 percentage points), while reducing inventory and logistics costs (up to 15 percentage points). Overall, Sopra Steria has observed significant gains in decision-making ability as information reliability and speed of analysis in decision-making.


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