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How Michelin Uses AI-Simulation Technology to Optimize Its Global Profit Margin by 5%

How Michelin Uses AI-Simulation Technology to Optimize Its Global Profit Margin by 5%

In a recent webinar hosted by the Digital Twin Consortium, Thibaut d’Hérouville, VP Group Industrial Supply Chain at Michelin, and Michel Morvan, co-founder and Executive Chairman at Cosmo Tech, discussed how Michelin uses our Prescriptive Simulation Twins to optimize its strategic sourcing. Here are the key points of discussion from this 30-minute webinar, and the replay with the full story (at bottom of this page).

Michelin, a €20 billion global tire and mobility products manufacturer that is no stranger to digital twins, has been using its modeling capabilities to improve tire road wear for the past 30 years.

Understanding the power of next-generation digital twins, the global manufacturer looked to find the right partner to solve the complex and costly sourcing challenges it faced.

“We’ve been using digital twins in our product development for the past thirty years. Now, what you can do with simulation digital twins is completely different,” Thibaut commented during the Digital Twin Consortium (DTC) webinar.  

Faced with today’s rapidly changing markets, the global leader needed a technology that could simulate beyond just physical assets. Michelin required a robust simulation solution that could take into account key indicators such as service levels, CO2 emissions, inventory, distribution, plant capacity, and 1,700 product models (in the case presented), all within a complex manufacturing and distribution matrix. 

“The fact that the world is changing so fast means that before making a major decision such as whether or not to build a new factory representing a quite significant capital investment, you have to run simulations to understand your future risk,” stressed Thibaut.  “We know the past, but with Cosmo Tech technology we have the capability to compare different options to evaluate risks.”

Convinced that digital twins were now capable of solving Michelin’s objective to optimize its strategic sourcing, it sought to find a partner providing the robustness and 360° simulation capabilities required. “It’s not the first time that I tried to solve these problems with digital twins. I had a lot of failures in this area. But now, the fact that you can manipulate and deal with a lot of data plus the power of the new algorithms means it’s a technology we have to invest in,” said Thibaut.  

Strategic sourcing is a significant challenge for Michelin, whose local-to-local strategy is to manufacture a product near the point of sale. Making the right decisions can speed up supply lines, reduce carbon emissions, improve service levels and substantially reduce its heavy logistics costs resulting in higher profits. 

With 70 production plants worldwide, operations in 170 plus countries, and a growing number of product models (mandated by country regulations, market evolution, ect.), determining the optimal sourcing strategy to maximize profits in the coming five years is critical.

“Our logistics cost is €1.9 billion, and our inventory level is almost €4 billion. In emerging markets, we’re selling more than 50 million passenger car tires per year.  Even if we are increasing our capacity, the issue is where do we need to produce our tires,” stressed Thibaut.

The magnitude of the global sourcing challenge meant that Michelin required a way to test different strategies and scenarios against each other to identify the best options, as well as having the capability to ask ‘how-to’ questions in order to automatically generate direction and action recommendations to optimize three concurrent key performance indicators (KPI): cost, quality of service and stock levels. 

“The number of product models we need to manufacture to serve each market is increasingly complex. There is a lot at stake in finding the right balance between locally manufacturing versus exporting,” explained Thibaut. 

Making the right decision requires looking at demand volatility, plant retooling requirements, a shortening product life cycle, heightening customer service expectations, and the heavy financial impact of inventory.

With the growing pressure for companies to decrease their CO2 emissions, Michelin is committed to reducing its carbon footprint. Understanding how its logistics strategy impacts CO2 emissions is vital to the tire manufacturer’s sourcing strategy.

Cosmo Tech’s AI-Simulation Technology allowed Michelin to run more than 80,000 simulations, each with more than 3,000 different and dynamic decision variables, and built-in optimization algorithms to determine the best strategy to adopt. 

Michelin was able to identify an actionable strategic sourcing plan for the next five years that would reduce their logistic costs by €10 million annually. Global profit margin is optimized by more than 5%, transport and customs costs reduced by more than 60%, and immobilized transportation costs significantly.

As Michel Morvan of Cosmo Tech later commented, “The challenge for Michelin was to elaborate an optimal capacity investment plan in a situation of volatile demand composed of a high diversity of products. Our AI-Simulation Technology, with its capacity to holistically model and simulate the most complex systems, empowered Michelin to understand the impact of their decisions, test alternative sourcing strategies before implementing them.”

Are you looking to empower your supply chain? Discover how your organization can cut costs for better profits with our Prescriptive Simulation Twins. You can learn more about our industry solutions by visiting Cosmo Tech Solutions.

Watch the complete webinar here: