SupplyChain Optimizer uses machine learning to help mid-market manufacturers and distributors predict demand fluctuations, optimize warehouse inventory levels, and identify potential supply chain disruptions before they impact operations.
The platform ingests data from ERP systems, point-of-sale terminals, weather APIs, and shipping trackers to build a holistic view of the supply chain. Its simulation engine lets logistics managers run "what-if" scenarios — testing the impact of supplier delays, demand spikes, or route changes — before committing to decisions.
The platform ingests data from ERP systems, point-of-sale terminals, weather APIs, and shipping trackers to build a holistic view of the supply chain. Its simulation engine lets logistics managers run "what-if" scenarios — testing the impact of supplier delays, demand spikes, or route changes — before committing to decisions.
Key Features
Tech Stack
Python
Scikit-learn
Apache Airflow
PostgreSQL
Grafana
Docker
AWS SageMaker
FastAPI