Sustainability Data Management

Client challenge example: 

With ESG criteria becoming strategic, managing non-financial data quality is a challenge for CSR officers due to: 

• Increasing regulations 
• Limited time 
• Growing data volume and inadequate tools for non-financial data specifics (often missing physical flow qualifiers, requiring cross-service cooperation) 

Our intervention objectives: 

Implement a data governance strategy, especially for non-financial data: 

• Data mapping and sensitivity analysis 
• Employee awareness of data and socio-ecological stakes 
• Deployment of standardized data management tools and processes 
• Long-term sustainability data management strategy 

Success modalities: 

• Strong sponsorship from strategic management 
• “Culture-building” project involving all services including top management, IT/data operations, and relevant business units 
• Governance and trained data owners in each service 
• Ongoing data quality measurement to improve reliability 

Energy efficiency audit of industrial sites

Client challenge example: 

Energy efficiency and decarbonation address strategic issues for industrial sites: 

• Financial savings (10-25%)
• Reduced exposure to external factors (price fluctuations, availability) 
• Access to new subsidies (e.g., EU taxonomy loans) 
• Anticipation of future regulations or client sustainability expectations

These benefits reassure investors, improve attractiveness, and ensure activity resilience and sustainability. 

Our objectives: 

Depending on infrastructure and processes, energy efficiency engineers can: 

• Implement measurement and monitoring tools (EMS) per ISO 50001 
• Redesign processes to reduce overall consumption (e.g., self-consumption, tech improvements) 
• Reduce energy losses (e.g., waste heat recovery, process optimization) 
• Modify energy mix (e.g., electrification) or select alternative inputs 

Success modalities: 

• Understand environmental impacts over product lifecycle (LCA recommended) 
• Set up R&D/watch units to stay updated on energy-saving innovations 

Process modeling for site decarbonization 

Client challenge example: 

Due to high costs of installing/calibrating processes, it’s necessary to simulate future processes in advance (often as part of R&D) to estimate emission reductions and co-externalities (pollution, water use, costs). 

Our objective: 

Develop data science and AI models to optimize manufacturing processes. 

Success modalities: 

• Coordination with process experts 
• Access to rich literature on future processes 

Climate change adaptation plan 

Client challenge example: 

The IPCC’s worst-case scenario (RCP 8.5) predicts +4.5°C temperature rise by 2100. Ignoring this risk exposes companies to severe physical and financial damage. 

It’s essential to assess exposure of critical installations to climate hazards and develop an appropriate adaptation plan. 

Our objectives: 

• Assess environmental (heatwaves, rainfall, flow rates) and technical site data 
• Evaluate current and future resilience (without adaptation) using OCARA method 
• Draft detailed action plan for a reference site, including further studies or investments 
• Possibly create an adaptation “standard” applicable to similar sites 

Success modalities: 

• Improve data quality alongside study to reduce uncertainty 
• Availability of on-site contacts 

Product eco-design 

Client challenge example: 

LCA identifies main impacts across the value chain of manufacturing, distribution, use, and end-of-life. 

To comply with regulations (CSRD), reputation, or market resilience, impact reduction solutions must be thought of upstream (not piecewise to avoid pollution shifting). 

Initiate eco-design reflections for future and ideally existing products. 

Our objectives: 

Evaluate eco-design levers considering full value chain and production scale: 

• Internalize processes 
• Relocate 
• Use new materials 
• Modify energy mix 
• Valorize value chain externalities 
• Improve reparability 

Success modalities: 

• Engage suppliers and customers 
• Use multi-criteria approach to avoid shifting impacts