Use Case - Machine Learning
Machine Learning
Every event triggers learning.
Overview
Machine Learning continuously improves the entire ERP by using data from every transaction.
Key AI Steps
- 1.Orders → forecast updates
- 2.Inventory → replenishment tuning
- 3.Logistics → carrier performance modeling
- 4.Invoices → vendor scoring
- 5.GL → anomaly detection model training
Impact
- System gets smarter every day
- Predictive accuracy compounds
- Fully autonomous workflows become achievable
Orders → Forecast Updates
Every new customer order is immediately used to refine and update demand forecasts. This ensures forecasting models stay current and responsive to real-time demand signals.
Inventory → Replenishment Tuning
Real-time inventory levels and stock movements are continuously analyzed to adjust replenishment parameters. This helps maintain optimal inventory levels while reducing stockouts and excess inventory.
Logistics → Carrier Performance Modeling
Shipment and delivery data is used to continuously improve models that measure carrier performance and reliability. This supports better carrier selection and more accurate logistics planning.
Invoices → Vendor Scoring
Invoice processing data is fed back into the system to refine vendor reliability and risk scores. This enables more informed sourcing decisions and proactive vendor management.
GL → Anomaly Detection Model Training
General Ledger entries are leveraged to train and improve models that detect financial anomalies and exceptions. This strengthens financial controls and early issue detection.
Continuous Recalibration from Real-Time Data
The system’s learning algorithms are continuously and automatically tuned based on every transaction. This ongoing recalibration enables a self-improving ERP that adapts in real time.