Document Processing · Real-time Analytics · Supply Chain Optimization
Sanofi needed to replace siloed, manual manufacturing and supply workflows with a connected, data-driven operating model that could improve medicine quality and safety, prevent supply disruptions, speed product launches, and reduce the reporting burden on technical teams.
Reported impact
Sanofi reports stronger supply resilience, lower manual effort, faster reporting and launch-related work, and better ability to deliver medicines safely and on time to patients.
Sanofi is using an in-house digital manufacturing and supply stack that combines advanced analytics, real-time tracking, generative AI, predictive drug stability modelling, and its Modulus smart-factory model. Data from manufacturing, quality control, laboratories, and supply operations is captured across digitally connected facilities and fed into analytics workflows that optimize production processes, improve supply-chain visibility, and flag potential disruptions earlier. Generative AI is used to automate report creation, including data collection and formatting for Product Quality Reports, while predictive drug stability modelling helps development teams anticipate product behavior and shorten decisions around transfer, submission, and launch. Modulus extends this architecture into AI-enabled, plug-and-play biomanufacturing sites that can flex output across multiple vaccine and biologic types. The overall system serves Manufacturing & Supply and R&D teams with automated planning, experiment design, and faster operational decisions.
Manufacturing, laboratory, quality, and supply data is captured across Sanofi’s connected operations and Modulus facilities, processed through advanced analytics, predictive models, and generative AI workflows, and then delivered to Manufacturing & Supply and R&D teams for supply monitoring, automated reporting, planning, product transfer, and launch decisions.
"It is designed to be fully AI-enabled and digitally connected, which means we can capture all data across the facility and continuously learn from it."
"Our development teams are also leveraging predictive drug stability modelling to better anticipate product behaviours, optimize development decisions and reduce timelines to submission and market launch."
"This helps us not only to predict potential supply chain challenges, but also to rapidly adapt our manufacturing processes to meet public health demand."
"Through strategic integration of digital technologies like advanced analytics, automation and AI across all aspects of our manufacturing and supply chain, we are moving beyond traditional, often siloed processes to a more connected, intelligent ecosystem."
"Using advanced analytics, we are optimizing our production processes, as well as enhancing our supply chain visibility and proactively addressing potential disruptions with real-time tracking."
"We're leveraging generative AI to automate report creation across our organization, including data collection and formatting for our 3,500 annual Product Quality Reports, targeting a 70% reduction in creation time."
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Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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