Lifecycle Assessment

Use AI to analyze and optimize environmental impacts throughout product lifecycles for sustainability.

Business impact

• Carbon Emissions — Reduces greenhouse gas emissions by identifying high-impact lifecycle stages
• Cost Reduction — Lowers costs through optimized resource use and equipment refurbishment
• Operational Efficiency — Improves processes by integrating real-time data and predictive insights
• Sustainability Compliance — Ensures adherence to environmental regulations and reporting standards
• Design Cycle Time — Accelerates design decisions by embedding real-time carbon tracking early
• Procurement Efficiency — Optimizes material selection based on embodied carbon and supplier data
• Sourcing Efficiency — Facilitates faster, sustainability-aligned sourcing decisions using AI agents
• Product Development Cycle Time — Reduces development time by integrating environmental modeling dynamically
• Sustainability Ratings — Improves ratings by demonstrating lifecycle impact reductions and transparency
• Material Waste Reduction — Minimizes waste through circularity and lifecycle-informed material choices

Data requirements

• Equipment Usage Data (Numeric) — Tracks operational emissions and wear for lifecycle impact analysis
• Supply Chain Data (Structured) — Aggregates material sourcing and manufacturing emissions for assessment
• Environmental Product Declarations (EPDs) (Structured) — Provides standardized carbon footprint data for materials and products
• Building Information Modeling (BIM) (Code) — Integrates design and material data for real-time carbon tracking
• Sensor Data from IoT Devices (Numeric) — Monitors environmental conditions and equipment performance continuously
• Lifecycle Assessment Reports (Text) — Historical and modeled environmental impact data for benchmarking
• Non-Destructive Testing Data (Image) — Assesses material condition to inform reuse and refurbishment decisions

AI methods and techniques

• Predictive AI — Forecasts environmental impacts and maintenance needs based on historical data
• Generative AI — Creates design alternatives optimizing for sustainability and performance trade-offs
• Agentic AI — Autonomously manages sourcing and refurbishment decisions aligned with sustainability goals
• Symbolic AI — Applies rule-based reasoning for compliance and lifecycle assessment standards

AI models and model families

GPT-4, Claude, Llama 2, Custom ML models for LCA prediction

Industries

Real-world evidence

9 documented case studies on record.

Companies using this: Atkins R, Baytree, Circkit, Curbon, Eckersley O Callaghan, Gilbert Ash, Material Exchange, RESTOR University Birmingham University Cambridge Chetwoods Architects, Vanderbilt University Medical Center.