oeplan uses advanced machine learning to forecast automotive after-service parts demand — so you stock the right parts, in the right place, at the right time.
Capabilities
From demand sensing to replenishment planning, oeplan covers the entire after-service parts lifecycle with intelligent automation.
ML models trained on historical sales, vehicle parc data, seasonality, and failure curves to predict part-level demand months ahead.
Account for variable supplier lead times, transit delays, and customs clearance to ensure parts arrive exactly when needed.
Automatically redistribute excess stock across warehouses and dealerships to minimize obsolescence and maximize availability.
Detect emerging failure patterns across vehicle models and generations to proactively stock replacement parts before demand spikes.
Understand demand patterns at the dealer level and push tailored stocking recommendations to each location in your network.
Live dashboards tracking fill rates, backorder rates, inventory health, and forecast accuracy across your entire parts operation.
Process
oeplan ingests your data, learns demand patterns, generates forecasts, and delivers actionable replenishment plans — automatically.
Integrate DMS, ERP, and vehicle parc data through our secure connectors. Setup takes hours, not months.
Our ML engine analyzes historical patterns, seasonal trends, and vehicle lifecycle data to build part-level forecast models.
Receive daily updated demand forecasts at SKU level for every location — with confidence intervals and anomaly alerts.
Automated purchase orders, transfer recommendations, and safety stock adjustments flow directly into your systems.
Join leading automotive companies using oeplan to reduce stockouts, cut excess inventory, and delight their customers.