Digital Supply Chain

The digital transformation of supply chains relies on integrating data and AI to optimize the visibility, resilience, and efficiency of logistics flows.
Missions include modeling flows, predicting disruptions, automating decisions, and improving collaboration among partners.

Core Responsibilities

a. Data Governance and Collection for Supply Chain

• Data Centralization: collect, clean, and structure data from ERP, WMS, TMS, IoT, and external partners (suppliers, carriers, customers).

• Key Performance Indicators (KPIs): define and track logistics KPIs such as service level, delivery lead times, logistics costs, stock-out rates, and carbon footprint.

b. Reporting and Visualization for Decision-Making

• Dashboard Design: create visualization tools (Power BI, Tableau, Python) to monitor supply chain performance in real time.

• Automated Reporting: leverage AI to generate reports on logistics performance, risks, and optimization opportunities.

c. Modeling and Optimization of Logistics Flows

• Model Development:

  • Explanatory Models: analyze the causes of delays, cost overruns, or stock-outs.

  • Predictive Models: anticipate disruptions (e.g., transport delays, raw material shortages) and forecast demand.

  • Prescriptive Models: recommend actions to optimize inventory, routes, or production capacities.

• AI and Machine Learning Applications:

  • Dynamic optimization of inventory and procurement.

  • Automation of logistics planning (e.g., order allocation to warehouses, carrier selection).

  • Analysis of unstructured data (contracts, emails, customs documents) to detect risks or opportunities.

d. Cross-Functional Collaboration and Digital Transformation

• Interface with Business Teams: collaborate with procurement, production, logistics, and sales to integrate data insights into decisions.

• Technological and Regulatory Watch: monitor logistics innovations (blockchain, drones, autonomous vehicles) and ensure compliance with customs and environmental regulations.

• Change Management: train teams to use data/AI tools and foster a data-driven culture.

Concrete Project Examples

• Inventory Optimization: apply machine learning to reduce inventory levels while maintaining a 99% service level.

• Delay Prediction: build a predictive model to anticipate delivery delays due to weather, traffic, or strikes.

• Planning Automation: deploy an AI tool to optimize delivery routes and reduce transportation costs by 15%.

• Traceability and Transparency: implement a blockchain solution to track products from origin to final customer.