Supply chain & operations.

Challenges

Why data matters

Inventory, flows, maintenance, quality: operations generate huge volumes of data, sensors included. Used well, they reduce costs and stock-outs.

What data brings

What data changes

Demand forecasting

Forecasting models to size production, inventory and supply.

Inventory and flow management

End-to-end visibility of inventory, orders and deliveries.

Predictive maintenance

Using IoT data to anticipate failures and plan interventions.

Site performance

Production, quality and safety indicators comparable across sites.

Field applications

Mobile tools for operators and technicians.

Generative AI

First generative AI use cases

• Maintenance assistants querying technical documentation
• Incident report analysis
• Assisted intervention planning

Method

The Visian approach

1. Framing with the department concerned: decisions to inform, indicators, priorities.
2. Data : source inventory, quality measurement, corrections at source.
3. Tool : dashboard, model or AI assistant delivered in short iterations.
4. Adoption : team training, usage tracking, skills transfer.

References

Visian achievements

• TotalEnergies: smartwatch maintenance app for operators
• SANEF: 6 IoT use cases
• ENGIE: predictive maintenance and Databricks data engineering
• CMA CGM: data analysis and CSR business analysis

All references