This is like giving an auto manufacturer a smart GPS for its supply chain that suggests the best routes not only by cost and speed, but also by how green and responsible each option is – using data instead of gut feel.
Traditional automotive supply chain decisions (supplier selection, sourcing, logistics) are optimized mainly for cost and delivery time, with sustainability goals (emissions, resource use, social impact) treated as afterthoughts. This approach brings sustainability metrics into a structured, data-driven decision framework so trade-offs can be quantified and optimized.
Domain-specific decision models and datasets tuned for automotive supply chains (e.g., BOM complexity, tier-1/tier-2 suppliers, just-in-time constraints) that are difficult for generic tools to replicate.
Classical-ML (Scikit/XGBoost)
Structured SQL
Medium (Integration logic)
Data availability and quality from suppliers (standardized, timely sustainability and operational metrics) to feed the decision models.
Early Majority
Focus on sustainability-aware, data-driven decision support specifically for complex automotive supply chains rather than generic supply chain optimization tools.
80 use cases in this application