We successfully integrated our ML-based demand forecasting system with Aveva MES Advanced Planning module to address chronic inventory issues. Our challenge was 32% excess inventory due to disconnected planning systems.
The implementation focused on four key areas: normalizing ML forecast data for MES consumption, establishing bidirectional API synchronization between systems, creating automated feedback loops for forecast accuracy, and optimizing planning parameters based on real-time production data.
Our ML system generates daily forecasts in JSON format with confidence scores. We needed to transform this into MES planning orders while maintaining forecast accuracy and handling data quality issues.
// Sample ML forecast output structure
{
"product": "SKU-A123",
"forecast_qty": 15000,
"confidence": 0.87,
"horizon_days": 14
}
The bidirectional sync was critical - MES needed to push actual production data back to ML for model retraining. We’re now seeing significant improvements in inventory levels and plan accuracy. Happy to share implementation details and lessons learned.
The bidirectional sync is where most implementations struggle. How frequently do you push production actuals back to ML? And how do you handle the timing mismatch - MES updates in real-time but ML typically retrains on daily or weekly batches?
We built a custom transformation service using REST APIs. The ML system outputs probabilistic forecasts with confidence intervals, while MES Advanced Planning expects deterministic quantities with specific UOM and timing attributes.
Our middleware converts confidence scores to safety stock buffers and maps forecast horizons to MES planning periods. We also implemented data quality checks - rejecting forecasts below 70% confidence and flagging anomalies for manual review.
// Transformation logic excerpt
PlanningOrder order = new PlanningOrder();
order.setQuantity(forecast.getQty() * forecast.getConfidence());
order.setSafetyStock(calculateBuffer(forecast.getStdDev()));
order.setPlanningHorizon(mapHorizonToPeriod(forecast.getDays()));
The key was maintaining traceability - every MES planning order links back to its source forecast for audit and feedback loop purposes.
What planning parameters did you optimize? We’re considering similar integration but concerned about over-automation causing planning instability.
We implemented a hybrid approach. MES pushes completion events to a staging database in real-time via event subscriptions. A scheduled job runs every 4 hours to aggregate and transmit batches to the ML system.
This balances data freshness with ML system load. The ML model retrains nightly but uses the accumulated actuals for validation scoring throughout the day. We track forecast vs actual variance in MES dashboards, which feeds back into planning parameter adjustments.
The feedback loop was crucial - we automated adjustment of safety stock multipliers and lead time buffers based on 30-day rolling forecast accuracy. This closed-loop system continuously improves without manual intervention.