ATP Simulation

Creates a logically isolated copy of the live ATP environment. Planners can introduce hypothetical changes—such as a plant shutdown, a spike in demand, or a delayed shipment—and observe the cascading impact on order commitments without altering actual reservations or releasing bad commitments to customers.

Allows users to simulate the failure of a supply node:

• Supplier Delay: Push a purchase order out by 2 weeks.
• Capacity Loss: Reduce a work center's output by 40%.
• Quality Hold: Block a specific lot from allocation. The engine recalculates the ATP netting logic to identify which customer orders will be shorted.

Planners can inject a hypothetical large sales order or a forecast override into the simulation. The system evaluates whether the order can be fulfilled using Cumulative ATP across the horizon or if it requires order splitting across multiple warehouses, revealing the true cost-to-serve for the opportunity.

Enables side-by-side comparison of sourcing rules. A planner can test if switching a regional DC from a primary to a secondary supplier resolves a projected stockout. The simulation quantifies the trade-off between On-Time In-Full (OTIF) rates and landed cost, providing data for Profitable-to-Promise (PTP) decisions.

Visualizes the projected available balance over the ATP Horizon under the simulated scenario. It highlights the exact date when a stockout will occur, allowing planners to proactively adjust safety lead time or initiate backorder processing workflows before the disruption actually happens.

Maintains full demand pegging and supply pegging links within the simulated environment. If a simulated order fails, the planner can trace exactly which supply element (e.g., a specific production run) was insufficient, enabling precise root-cause analysis of the hypothetical failure.

The foundational real-time check that determines if and when a product can be delivered. ATP Simulation extends this by allowing planners to run hypothetical scenarios without committing actual inventory.

• Evaluates on-hand stock and scheduled receipts
• Generates a committed delivery date
• The live system that ATP Simulation mirrors for testing

An advanced promising method using a constraint solver to simultaneously evaluate material, capacity, and transportation limitations. ATP Simulation often models these constraints to test feasibility under stress.

• Models real-world bottlenecks
• Generates only feasible delivery dates
• Essential for accurate what-if scenario outcomes

The process of linking a specific supply receipt to a specific customer order. In ATP Simulation, pegging logic is critical for understanding how a hypothetical demand spike would consume available supply and impact other orders.

• Creates traceability from supply to demand
• Enables precise impact analysis
• Reveals which orders would be affected by a simulated change

A scheduling method that generates a production plan by modeling real-world constraints of work centers, labor, and tooling. ATP Simulation integrates with these schedules to test if a new order can be manufactured in time.

• Prevents resource overloading
• Models queue times and setup times
• Provides the capacity half of the CTP equation

The critical KPI measuring the percentage of orders delivered complete and on the confirmed date. ATP Simulation helps planners predict how hypothetical changes would impact OTIF before they make live commitments.

• Measures perfect order fulfillment
• Directly tied to customer satisfaction
• The ultimate metric for validating simulation outcomes

A buffer added to standard lead times to absorb supply variability. ATP Simulation allows planners to model the impact of adjusting these buffers—increasing them to improve reliability or reducing them to optimize inventory.

• Protects against late deliveries
• Directly impacts promised dates
• A key variable in simulation scenarios