Sustainable Operations & Decarbonation

Sustainable Operations & Decarbonation missions in industry rely on close collaboration with Data and AI teams. Reducing carbon footprints, optimizing resource use, and transitioning to sustainable operations require precise measurement of industrial processes (collecting reliable data, real-time analysis) and a deep understanding of actionable levers (explaining impacts, predicting scenarios, prescribing operational solutions). This demands advanced tools: data science, machine learning, AI, and digital simulation.

Core Responsibilities

a. Data Governance and Collection for Industrial and Environmental Data

Data Centralization: Collect, clean, structure, and govern data from industrial operations (IoT sensors, SCADA, ERP, MES) and external sources (carbon databases, regulations, energy suppliers, etc.).
Key Performance Indicators (KPIs): Define and track decarbonation KPIs (e.g., carbon intensity per unit produced, energy efficiency, waste recycling rates, water consumption) and sustainable operations metrics (e.g., ESG-adjusted Overall Equipment Effectiveness).

b. Reporting and Visualization for Decision-Making

Operational Dashboards: Design visualization tools (Power BI, Tableau, Grafana, Python) to make environmental and operational performance data accessible to industrial teams and leadership.
Automated Reporting: Use AI to generate decarbonation reports compliant with industry standards (e.g., GHG Protocol, ISO 50001, local regulations) and stakeholder expectations (investors, regulators, customers).

c. Modeling and Optimization for Sustainable Operations

Model Development:
- Explanatory Models: Analyze correlations between industrial processes and their carbon impact (e.g., link between production speed and energy consumption).
- Predictive Models: Forecast future emissions based on production scenarios, energy mixes, or regulatory changes.
- Prescriptive Models: Recommend concrete actions to reduce carbon footprints (e.g., optimizing production schedules, substituting raw materials, improving energy efficiency).
Digital Twins and Simulation: Model production lines or entire plants to test decarbonation scenarios (e.g., impact of electrifying processes, integrating renewable energy).
AI and Machine Learning Applications:
- Detect anomalies in energy consumption or emissions (e.g., gas leaks, electricity overuse).
- Dynamically optimize production parameters to minimize carbon footprints without compromising productivity.

d. Cross-Functional Collaboration and Operational Transformation

Interface with Industrial Teams: Collaborate with production, maintenance, procurement, and energy teams to integrate sustainability data into operational processes (e.g., selecting low-carbon suppliers, predictive maintenance to reduce waste).
Regulatory and Technological Watch: Monitor decarbonation innovations (e.g., green hydrogen, carbon capture) and regulatory changes (e.g., EU Taxonomy, Carbon Border Adjustment Mechanism).
Change Management: Train industrial teams to use data/AI tools and raise awareness of decarbonation challenges.

Concrete Project Examples

Energy Optimization in a Plant: Use AI to adjust production parameters (temperature, pressure, speed) in real time to reduce energy consumption by 15% without affecting yield.
Digital Twin for Decarbonation: Model a production line to simulate the impact of replacing natural gas with green hydrogen and identify bottlenecks.
Automated Waste Detection: Implement machine learning algorithms to pinpoint sources of energy or raw material waste in production chains.
Low-Carbon Transition Plan: Develop a roadmap to electrify 30% of thermal processes by 2030, based on cost and emission scenarios.
Scope 3 Emissions Traceability: Create a tool to map and reduce indirect emissions across the supply chain, in collaboration with suppliers.

Environment and Challenges

Complex Industrial Data: Data comes from heterogeneous sources (sensors, ERP, manual reports) and is often noisy or incomplete. Rigorous cleaning and cross-referencing are essential.
Balancing Performance and Sustainability: Solutions must reconcile emission reductions, cost control, and productivity maintenance.
Innovation Under Constraints: Decarbonation technologies (e.g., hydrogen furnaces, carbon capture) are often immature or expensive. The challenge is to identify realistic, scalable opportunities.