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Title
Michelin uses machine learning to predict stock outages 15 days ahead in its supply chain
…tage of stock. Using machine learning models trained on past supply chain data, the application was designed to predict stock levels in 15 days’ time and recommend actions to prevent outages from occurring in the future. Michelin used Databricks for both the unification of the input data, collabora…
Description
Michelin used the Databricks Data + AI Platform to democratize data access across the organization and build machine learning models trained on past supply chain data to predict stock levels 15 days ahead and recommend actions to prevent outages. Michelin used Databricks for unifying input data, collaborating on model training, and deploying and orchestrating models into production, with data continuously streamed and kept up to date in Delta Lake. The stock-outage prediction use case was an early success that helped Michelin scale to hundreds of AI and data use cases across the company.
…ys’ time and recommend actions to prevent outages from occurring in the future. Michelin used Databricks for both the unification of the input data, collaboration on how to apply the data to train machine learning models, and the deployment and orchestration of models into production. And with real-time capabilities, the data itself was continuously streamed and kept up-to-date in Databricks Delta Lake while business analysts used Databricks SQL to run queries and visualize insights. The number of use cases has grown considerably since then. “Using lakehouse mea…
Company
Michelin
…rity and Trust Ready to get started? Get a Demo Login Contact Us Try Databricks Customer Stories / Michelin CUSTOMER STORY Driving toward peak operational efficiency Michelin uses Databri…
Industry
Manufacturing
…Follow Michelin's IS and digital teams on LinkedIn Share this post Details Industry : Manufacturing Use Case : Data Engineering Cloud : Azure Product : Databricks SQL , Delta Lake…
Problem
Michelin's former on-premises data platform lacked the necessary openness and flexibility; with data siloed by department and the platform's centralized nature, providing services at scale was challenging and employees were often unable to access the tools and technologies they needed.
…t tools available so users could solve their use cases by themselves.” However, Michelin’s former on-premises data platform lacked the necessary openness and flexibility. With data siloed by department and the centralized nature of the platform, providing services at scale was challenging, and employees were often unable to access the tools and technologies they needed. A scalable, flexible and — importantly — open platform was needed. And for this…
Solution
Michelin used the Databricks Data + AI Platform to unify input data and collaborate on training machine learning models on past supply chain data, deploying and orchestrating the models into production, with data continuously streamed and kept up to date in Databricks Delta Lake while business analysts used Databricks SQL to run queries and visualize insights.
…ys’ time and recommend actions to prevent outages from occurring in the future. Michelin used Databricks for both the unification of the input data, collaboration on how to apply the data to train machine learning models, and the deployment and orchestration of models into production. And with real-time capabilities, the data itself was continuously streamed and…
Business value
Using lakehouse, Michelin has been able to scale to hundreds of use cases on the platform beyond the original stock-outage prediction use case, empowering citizen data analysts and data scientists alike to build and share their own use cases and best practices.
…visualize insights. The number of use cases has grown considerably since then. “Using lakehouse means we’ve been able to scale to hundreds of use cases on the platform, regardless of what they’re focused on. In fact, we don’t know what 80% of them…
AI capabilities
Predictive Analytics
…gly.” An early use case involved employing AI to predict the shortage of stock. Using machine learning models trained on past supply chain data, the application was designed to predict stock levels in 15 days’ time and recommend actions to prevent outages from occurring in the future. Michelin…
Business functions
Supply Chain & Logistics
…zen users” and distributed IT teams could develop their own use cases — such as using AI to predict stock outages and to reduce carbon emissions in the supply chain. Their centralized legacy data platform, however, made it difficult for their i…
Technology
Databricks Data + AI Platform, Delta Lake, Databricks SQL
…post Details Industry : Manufacturing Use Case : Data Engineering Cloud : Azure Product : Databricks SQL , Delta Lake Ready to get started? Try Databricks for free Learn more about our product Talk…
Deployment model
Cloud
…In Share this post Details Industry : Manufacturing Use Case : Data Engineering Cloud : Azure Product : Databricks SQL , Delta Lake Ready to get started? Try Databricks for…
Deployment options
cloud
…In Share this post Details Industry : Manufacturing Use Case : Data Engineering Cloud : Azure Product : Databricks SQL , Delta Lake Ready to get started? Try Databricks for…
Implementation approach
Started with an early use case predicting stock outages, then scaled considerably to hundreds of use cases on the platform over time.
…cipate the needs of their stakeholders and design their use cases accordingly.” An early use case involved employing AI to predict the shortage of stock. Using machine learning models trained on past supply chain data, the applicatio…
Onboarding approach
Adopted a self-serve, community-driven approach where citizen users and distributed teams build and share their own data products, code and best practices on a common platform.
…cribed, accessible and trustworthy. “We believe in the democratization of data, a self-serve approach where users can come to a platform, build a new data product and then run it,” said Joris Nurit, Head of Data Transformation at Michelin. “We wanted to ensu…
Headline outcome
…tage of stock. Using machine learning models trained on past supply chain data, the application was designed to predict stock levels in 15 days’ time and recommend actions to prevent outages from occurring in the future. Michelin used Databricks for both the unification of the input data, collabora…
Use case type
Predictive operations
…gly.” An early use case involved employing AI to predict the shortage of stock. Using machine learning models trained on past supply chain data, the application was designed to predict stock levels in 15 days’ time and recommend actions to prevent outages from occurring in the future. Michelin used Databricks for both the unification of the input data, collaborat…
