Order Promising Engine

The engine executes Available-to-Promise (ATP), Capable-to-Promise (CTP), and Profitable-to-Promise (PTP) logic synchronously during the order entry process. It does not merely query a static database; it solves a multi-variable constraint problem involving:

• Material availability across multi-echelon inventories
• Finite capacity of work centers and labor
• Transportation lane capacities and lead times
• Customer-specific allocation rules and sourcing rules The result is a feasible delivery date returned in sub-second response times.

A sophisticated engine performs a cascading search across the supply network to find the optimal fulfillment node. This Global ATP capability evaluates:

• On-hand inventory at local distribution centers
• In-transit inventory scheduled for imminent receipt
• Planned production orders within the ATP Horizon
• Alternative products defined in a supersession chain
• Substitute locations via multi-sourcing optimization The search path is governed by configurable sourcing rules that define the sequence of supply sources to evaluate.

Upon generating a promise, the engine creates a hard or soft link between the demand and the supply. This demand pegging and supply pegging mechanism provides full traceability for impact analysis. Key functions include:

• Order reservation to guarantee inventory for the specific demand
• Cumulative ATP logic to promise large orders against supply arriving over multiple future periods
• Backorder processing workflows that automatically re-peg and re-promise unfilled orders as new supply materializes
• Order splitting to partially fulfill an order from multiple locations when a single source is insufficient

The engine respects critical planning boundaries to prevent instability in the execution pipeline. Demand Time Fences (DTF) define the point where actual orders consume the forecast, while Planning Time Fences (PTF) freeze the master production schedule. The ATP Horizon defines the future window for projecting availability. These fences ensure that:

• Short-term promises do not disrupt frozen production schedules
• Forecast-driven supply is not erroneously promised to new orders
• The engine operates on a stable, time-phased projection of supply and demand

A critical characteristic is the ability to operate in a non-destructive simulation mode. ATP Simulation allows planners to test hypothetical scenarios without affecting live commitments. This capability supports:

• Evaluating the impact of a large, unexpected order before acceptance
• Testing alternative sourcing configurations and sourcing rules
• Assessing the effect of a supply disruption or delayed shipment
• Validating the feasibility of a new customer contract with specific service level agreements The simulation engine uses the same core logic as the live engine, ensuring results are perfectly representative of real-world outcomes.

The engine externalizes business logic into a configurable Rule-Based ATP layer. This allows business users, not just developers, to define promising behavior. Configurable parameters include:

• Customer hierarchies and priority tiers for allocation management
• Product substitution logic via supersession chains
• Safety lead time buffers to absorb variability
• Shelf-life ATP rules for batch-managed, perishable goods
• Cost-to-serve thresholds for Profitable-to-Promise (PTP) decisions This configurability ensures the engine adapts to evolving business strategies without code changes.

The foundational real-time check that determines if on-hand or scheduled inventory can be committed to a customer order. ATP calculates the uncommitted portion of inventory and planned receipts, ensuring a promise doesn't create a stockout. It operates within the ATP Horizon, the future window over which availability is projected. Modern engines perform this check in milliseconds during order capture.

Extends ATP by evaluating not just inventory, but also production capacity and material availability. When stock is insufficient, CTP dynamically calculates if a product can be manufactured and delivered by the requested date. It interacts with Finite Capacity Scheduling to model real-world constraints of work centers, labor, and tooling, generating a feasible production plan before making the commitment.

Adds a financial intelligence layer to order promising. PTP evaluates the Cost-to-Serve—including picking, packing, freight, and special handling—against the order's margin and the customer's lifetime value. It enables businesses to prioritize high-value orders during constrained supply, ensuring every commitment aligns with profitability targets rather than just operational feasibility.

An advanced promising method that uses a constraint solver to simultaneously evaluate:

• Material constraints (component availability)
• Capacity constraints (machine and labor hours)
• Transportation constraints (shipping schedules and lane capacities)

This holistic approach generates a truly feasible delivery date, avoiding the pitfalls of sequential checks that can over-promise when one constraint is overlooked.

Demand Pegging links a specific supply receipt (purchase order, production run) to a customer order, creating full traceability. Supply Pegging does the reverse, linking an order to its fulfilling supply elements. This bidirectional visibility enables rapid impact analysis—if a shipment is delayed, planners instantly see which customer orders are affected and can re-promise accordingly.

Global ATP searches across a network of multiple plants and distribution centers to find the optimal fulfillment location. Omnichannel ATP extends this across sales channels, providing a unified inventory view for e-commerce, retail stores, and wholesale. This enables flexible fulfillment options like ship-from-store or buy-online-pick-up-in-store (BOPIS), maximizing service levels while minimizing split shipments.