Training foundation
The Digital Twin
The digital twin is the training ground for the shared world model. It is not a separate system, it is your current planning system, replicated as a stochastic simulation. Your actual planning rules run against realistic variability, and the gap between how those rules perform and how they could perform is exactly what the AI agents learn to close.
"The digital twin concept started in manufacturing, but its greatest impact will be in supply chain. The ability to simulate your entire network in real-time changes how you make every decision."
How the Digital Twin Works
Your planning rules against stochastic reality, thousands of times
Your Planning System, Stress-Tested
Traditional planning systems use deterministic heuristics: fixed reorder points, fixed safety stocks, fixed lot sizes, fixed lead times. These rules are configured per product-site in your ERP and they work well in stable conditions.
But reality is not stable. Customer demand fluctuates. Supplier lead times vary. Machines break down. Quality issues surface. The digital twin takes your exact planning rules, imported directly from your ERP, and runs them against stochastic reality: the same demand variability, lead time uncertainty, yield losses, quality events, and machine breakdowns that your real operations experience.
The result is a precise measurement of the gap between how your current heuristics perform and how they could perform. The stockouts they didn't prevent. The excess inventory they accumulated. The late deliveries they caused. The expediting costs they incurred. This gap is exactly what the AI agents learn to close across all six decision domains.
Nine Dimensions of Variability
Every variable randomized per entity in your network, fitted from your historical data
Per customer, product, site. Right-skewed, demand spikes larger than troughs. Industry-calibrated variability.
Per vendor, product, site. Long upper tail, disruptions cause 2.5x delays. Fitted from PO-to-receipt history.
Per lane. Tighter variability than external suppliers, internal logistics are more predictable.
Per site, customer. Moderate right skew. Fitted from actual vs. promised delivery dates.
Per product, site. How much usable output vs. planned input. Typical range: 90-99.5%.
Per product, site, resource. Actual vs. planned production speed. Range: 80-105% of standard.
Per product, site. Probability of passing inspection. Quality events cascade into supply disruptions.
Per resource, site. Unplanned downtime, maintenance events. Typical availability: 80-98%.
Per product pair, site. Time to switch production between products. Range: 15 min to 90 min.
Fitted from Your Data, Not Generic Assumptions
Every stochastic variable is fitted from your actual historical data when available (at least 5 observations). The system extracts the 5th percentile as the lower bound, the median as the most likely value, and the 95th percentile as the upper bound, creating a distribution that captures the realistic range of variability your supply chain actually experiences.
When history is insufficient, the system uses industry-calibrated fallbacks. Demand variability, for example, is set by product category:
Each variable is instantiated per entity in your network graph, not as a single global value. A supply chain with 9 finished goods, 81 components, 27 customers, 8 vendors, and 2 plants has thousands of unique distribution instances — each reflecting the specific behavior of that entity.
Industry-Calibrated Simulation
Trials and horizon matched to your industry's characteristics
Each trial is an independent replication of your supply chain operating over the full simulation horizon. More trials = more statistically reliable training data.
Set to 2x your industry's end-to-end supply chain lead time, enough to see one full replenishment cycle complete plus variability effects.
The Learning Sequence
Every training run follows the same eight-step sequence. This is how the digital twin transforms your ERP data into trained AI agents:
The agents learn where the heuristic fails, stockouts it didn't prevent, excess inventory it accumulated, late deliveries it caused, expediting costs it incurred. The trained agents then make better decisions in the same stochastic environment.
Three Strategic Uses
The digital twin isn't just for training. It becomes a permanent strategic asset:
Run 100+ simulations to test changes to inventory policies, ordering strategies, safety stock levels, or cost parameters before deploying to production. See the impact across the full range of possible futures.
What happens if you add a new DC? Remove a supplier? Change your BOM? Shift production to a different plant? The digital twin models the full network effect before you make the change.
Agents trained on 100+ simulated scenarios deploy to production with learned weights, not random initialization. They arrive pre-adapted to your supply chain's specific patterns of variability.
What Gets Measured
The digital twin measures customer service outcomes, not inputs. These metrics show how well your current planning rules performed against stochastic reality, and where the gap lies:
Metric targets (e.g., OTIF ≥ 95%) are your business goals. Metric actuals are computed from simulation results. The gap between targets and actuals is what the AI agents are trained to close.
Works With Your ERP
The digital twin imports your actual planning parameters, not generic defaults. It reads the specific reorder points, safety stocks, lot sizes, MRP types, and procurement types configured in your ERP for every product-site combination.
Supported ERP systems include SAP S/4HANA, SAP ECC, Microsoft Dynamics 365, and Odoo. The Integration layer handles the extraction and mapping automatically, including AI-enhanced interpretation of custom fields.
See Your Digital Twin
Run a simulation of your supply chain and see where the opportunities are.