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.

More on this topic

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

| Benoit Spolidor, Maxime Claisse

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.

How can Artificial Intelligence support the performances of Aerospace Supply Chain?

| Benoit Spolidor, Maxime Claisse

Artificial Intelligence is having a positive impact on almost every industry. It improves decision making processes, creating fast and consistent operations management. In the specific field of Aerospace, our conviction is that to be fully efficient, AI must be developed with dedicated characterics. Sopra Steria invests on these features for sustainable and large scale transformation by AI for Aerospace companies.

Remote experts help technicians on-site

| Torbjørn Meland

New technology helps maintain production and increase productivity at operating facilities by reducing the need to send technical experts between factories. By using HoloLens 2, Microsoft Teams, Intune and Dynamics 365 combined with a design-drive process, you can get a solution that gives on-site technicians support and help from remote experts.

AI lead Software Engineering: Sopra Steria Ecosystem Offerings

| Jérôme Perdriaud, Satish Srivastava

Apart from internally developed IP’s given in the previous edition we also have an ecosystem of mature market leading companies, start-ups as well as labs and universities to build competency in their offerings and use them to help our clients. Following are some of the offerings from the ecosystem.

AI led Software Engineering: Sopra Steria Offerings

| Jérôme Perdriaud, Satish Srivastava

Sopra Steria has been investing in AI led software engineering in order to help our clients not only reduce cost and gain efficiency but also empower their businesses by making the processes more responsive and scalable.

AI led Software Engineering Use cases: Application to Testing, Deployment & Operations

| Jérôme Perdriaud, Satish Srivastava

In the previous edition of the series, we have seen how AI transforms the software engineering lifecycle, specifically Management, Requirements, design and development phases. In this edition we will see how subsequent Testing, Deployment and Operations activities are affected by AI.

AI led Software Engineering Use Cases: Application to Development

| Jérôme Perdriaud, Satish Srivastava

In the previous edition of the series, we have seen how AI transforms the software engineering lifecycle, specifically Management, Requirements gathering, Design phases. In this edition we will see how software development activities are affected by AI.

AI led Software Engineering Use Cases: Application to Requirements & Design

| Jérôme Perdriaud, Satish Srivastava

In the previous edition of the series, we have seen how AI transforms the software engineering lifecycle, specifically Management phases. In this edition we will see how Requirement engineering is affected by AI.

Innovating in Pursuit of Climate Action and Environmental Sustainability

| Avinash Lunj, Siva Niranjan

From reducing carbon footprint to improving energy efficiency, the surge of sustainable business continues to increase in prominence. To attract new business, talent and investment, companies are required to demonstrate, that they are putting their climate change strategies into action.

Digital Innovation Factory: Which technical platform select and how operate it over the time?

| Béatrice Rollet, Simon Herd

As seen previously, digital experience and platform offerings call for a massive amount of software with frequent new services, and regularly updated and deleted new features. Long-established companies adopting an Enterprise Platform model must then own a new Digital Innovation Factory encompassing a Technical Platform.

Digital Innovation Factory: How to reshape your software development activities at the era of cloud-native application?

| Béatrice Rollet, Neil Anderson

60% of backend developers use containers in their work. Relying on cloud-native technologies, defining as modern applications packaged in containers, deployed as micro-services, running on elastic infrastructure, and managed through agile DevSecOps processes fits very well with large enterprise who very often encompass a wide variety of software technologies.

The Enterprise Platform and the CIO at the age of the new normal

| Béatrice Rollet, Marlon Bromfield

Covid-19 pandemic has showed that the most digitalized companies, the digital-first companies, were the un-constable winner of this challenging period. Providing business activities through advanced digital experiences or platform offerings, these companies has kept their customers and partners engaged and happy in this challenging period.

AI led Software Engineering Use Cases: Application to Project Management activities

| Jérôme Perdriaud, Satish Srivastava

Using various AI techniques such as machine learning, deep learning, natural language processing (NLP), information visualization etc it is possible to guide the software engineering professionals with AI enabled decision making and automations. 

AI led Software Engineering

| Jérôme Perdriaud, Satish Srivastava

CIOs are expected to partner business, and at times leads, the delivery of digital transformation. The existing IT landscape of a company needs to be rationalized and modernized to be able to achieve the expected business velocity.

Conversational Assistants: go to scale

| Patrick Meyer

74% of French companies consider chatbots as a lever for digital transformation and more than a third have already deployed one. By 2020, 80% of them could use a chat assistant. A massive deployment that echoes consumer habits: 69% prefer the bot to a human exchange.

How can you use your IT assets to achieve digital transformation?

| Andre Bakland, Simon Herd, Béatrice Rollet

According to Gartner, for every dollar invested in digitalisation in 2020, three dollars will have to be invested in the modernisation of IT assets. Therefore, opting for the right evolution strategy becomes a crucial issue. Read more.

How Data Science can help in a pandemic situation?

| Marlon Cárdenas

With the aim of covering current and future needs of society, Data Science and Artificial Intelligence are seeking to drive the creation of technological solutions that benefit users in their daily lives. Many disciplines are uniting behind this cause, with health sciences to the fore, especially given the current context of the battle against the Covid-19 pandemic.

How holographic technology is helping doctors deliver better care

| Scott Leaman

Long gone are the days when holograms were the stuff of sci-fi movies and video games. Holographic technology is taking the medical world by storm, and by the looks of it, it’s here to stay. So how exactly is this technology helping doctors, and what are the major developments that we expect in the near future?

How will artificial intelligence transform industry?

| Maxime Claisse, Alexis Girin, Benoit Spolidor

Whilst there is no set definition of artificial intelligence as of yet, experts are in agreement that AI can simulate human cognitive capabilities such as perception, reasoning, action, and learning. AI now promises to completely transform the industrial sector – one of its primary applications.

International Paris Air Show: 5 trends to transform aeronautic

| Youssoupha Diop

The 53rd International Paris Air Show 2019 has confirmed the mounting fierce competition in the world of aeronautics. In this context, data, digital tools and artificial intelligence are now understood to be precious bargaining chips to accelerate transformation and turn these challenges into opportunities.

Anticipate cloud migration with FinOps

| Béatrice Rollet

Innovative and fast cloud services are crucial to digital transformation initiatives. Whilst there is no textbook model on how to adopt these services, it is nonetheless vital for companies to integrate them as fully optimised services in order to control their ROI.

From product to services: Flying the Aeronautics Industry into the Digital Future

| Philippe Armandon, Gaudérique Garrigue

With increasing travel demand and new competitors entering the market, aircraft manufacturers today are under considerable pressure.

How to control and optimise your cloud costs

| Didier Teixeira, Béatrice Rollet, Frédéric Janicot

Using public cloud services means rethinking your IT financial management. 

ASD S5000F: taking Aircraft MRO to new heights?

| Cyrille Greffe

In the 1990s, the combination of computer-aided design (CAD) and the concept of modular documentation gave rise to the first ASD standards (AeroSpace and Defence Industries Association of Europe).

Application replatforming: the Cloud migration booster

| Benjamin Chossat

Simple set-up, low cost and access to the horizontal elasticity of the Cloud: replatforming is often considered the best solution for porting a business application to the Cloud.

7 key strategies to transform applications with the Cloud

| Benjamin Chossat

How to modernise an application efficiently using the Cloud?

Innovating in pursuit of environmental sustainability

| Siva Niranjan

To attract new business, talent and investment, companies have had to demonstrate their environmental credentials more and more over the past years to wide range of stakeholders including institutional investors, regulators, clients, and employees.

Urban Air Mobility: will the future of mobility be in the air?

| David Elmalem, Sébastien Lautier

While the dream of the flying car has often been reserved for science fiction, a very practical and real future is gradually emerging for urban air mobility.

Guidance is the key for adapting DevOps to big business

| Gauthier Deschamps

DevOps is revolutionising agile transformation for big business. The method was initially focussed on software building but by automating production, it frees up resources so as to better resolve organisational and human malfunctions.

How Blockchain technology can improve Industry 4.0’s cybersecurity

| Alexandre Eich Gozzi

Earlier this year, the world’s largest container shipping company Maersk fell victim to a massive ransomware attack from the infamous NotPetya malware.