Objective
By building a real-time and predictive supplier assessment and monitoring model using AI, organizations get the first line of sight into supplier failure and reduce the extent of supply chain disruption. The supplier risk model leveraging AI makes procurement process more efficient.
Outcome
The solution can run noise reduction, relevance-based normalization, other data science-based techniques to provide actionable insights. It then calculates a risk score/index for a supplier. Depending upon the risk scores, it alerts the organization of potential supplier failures.
Business Value
The solution enables supplier profiling and management: who to order from and how much to pay. One can also assign supplier scores based on the supplier risk of the vendors and predict whether supplier is reliable enough based on historical performance of the supplier
H2O's AI and Data Approaches
This solution is powered by the H2O AI Cloud, Driverless AI, AutoML, H2O-3, and H2O.ai Wave. The data science approaches include genetic algorithm, advanced feature engineering, classification algorithms, GLM, GBM, XGBoost, ensemble stacking, and various machine learning interpretability algorithms