Available-to-Promise (ATP)

The fundamental ATP equation is a time-phased netting of supply against demand. It starts with on-hand inventory, adds scheduled receipts (purchase orders, production orders), and subtracts actual customer orders and forecasted demand for each future time bucket. The result is the uncommitted quantity available to promise to a new customer order for a specific delivery date. This calculation is performed in real-time during the order entry process.

ATP can be deployed in two distinct modes:

• Push ATP (Discrete ATP): Inventory is allocated to specific customer orders on a first-come, first-served basis. The system checks availability against a single, fixed quantity at a specific location.
• Pull ATP (Capable-to-Promise): The system searches across the entire supply chain network—including upstream component availability, production capacity, and transportation constraints—to determine if a new order can be fulfilled, even if finished goods are not currently in stock.

The ATP calculation operates over a defined planning horizon divided into discrete time buckets (typically days or weeks). In the near-term buckets, the system relies on actual, firm planned orders. In the mid-to-long-term buckets, it uses the Master Production Schedule (MPS) and forecasted supply. The precision of the ATP quantity degrades further into the horizon as planned supply becomes less certain, which is why ATP is most reliable for short-term order promising.

When a new sales order is entered, the system must decide which ATP bucket to consume. Common consumption logic includes:

• Forward Consumption: The order consumes ATP from the requested date and then searches forward in time for the next available ATP.
• Backward Consumption: The system first tries to fulfill from ATP available before the requested date, minimizing late shipments.
• Mixed Consumption: A hybrid approach that searches a defined window both backward and forward to find the optimal fulfillment date that balances service level and inventory holding costs.

While ATP checks only the availability of uncommitted finished goods inventory and scheduled receipts, Capable-to-Promise (CTP) is a more complex, multi-resource check. CTP dynamically evaluates whether the production system has the available capacity, raw materials, and labor to manufacture a product that is not currently in stock. CTP is essential for make-to-order (MTO) environments where ATP alone would return a false negative.

In a distributed order management system, Global ATP searches for availability across multiple distribution centers and plants simultaneously. The allocation logic uses configurable sourcing rules to determine the optimal fulfillment site based on factors like distance to customer, inventory carrying cost, local tax implications, and service level agreements. This prevents a local stockout from rejecting an order that could be profitably fulfilled from another node in the network.

A multi-resource availability check that extends beyond on-hand inventory. CTP evaluates whether production capacity, raw materials, and transportation resources can be allocated to fulfill a new order by a specific date.

• ATP checks what is available; CTP checks what can be made available.
• Requires integration with Manufacturing Execution Systems (MES) and Material Requirements Planning (MRP).
• Example: An order for 500 units passes ATP (0 in stock), but CTP confirms the factory can produce them by Tuesday using available components.

A decision logic layer that overlays ATP and CTP to prioritize order fulfillment based on profitability rather than just chronological order entry.

• Segments customers by Customer Lifetime Value (CLV) or order margin.
• During constrained supply, PTP allocates scarce inventory to the most profitable channels or customers first.
• Uses dynamic margin analysis to evaluate the true cost-to-serve, including freight and handling, before making a promise.

The overarching real-time system that synthesizes ATP, CTP, and PTP to generate a reliable delivery commitment. It translates complex supply chain constraints into a simple customer-facing date.

• Allocated ATP: Reserves a portion of supply for specific high-priority customers or channels before general availability.
• Backorder Processing: When ATP fails, the logic automatically calculates the earliest feasible ship date based on incoming supply.
• Integrates with Order Management Systems (OMS) to ensure the promise is captured and tracked through fulfillment.

A time-phased planning methodology that applies dependent demand logic to distribution networks. DRP calculates net requirements and planned order releases for each echelon based on forecasts and current inventory positions.

• Drives the replenishment signals that ATP logic depends on for future supply visibility.
• Balances projected on-hand inventory against safety stock targets at each distribution center.
• Example: DRP determines that a regional warehouse needs a resupply order released today to prevent a stockout next week, feeding that planned receipt into the ATP engine.

The algorithmic process of calculating the precise quantity of buffer inventory required to absorb demand and supply variability. This directly feeds the ATP calculation by defining the threshold below which inventory is not 'available' to promise.

• Cycle Service Level vs. Fill Rate: Different statistical targets dramatically change the safety stock requirement.
• Demand Sensing: Uses short-term POS data to reduce forecast error and, consequently, the safety stock needed.
• A poorly optimized safety stock leads to either failed ATP promises (stockouts) or excessive working capital (overstock).

A holistic methodology that optimizes stock levels across the entire network simultaneously. MEIO informs ATP by determining the optimal positioning of inventory to maximize aggregate availability.

• Inventory Pooling: MEIO might recommend holding safety stock centrally rather than locally, affecting regional ATP availability.
• Lateral Transshipment: If a local ATP check fails, MEIO rules can trigger a peer-to-peer transfer to fulfill the order.
• Models the Bill of Distribution to understand how upstream delays cascade into downstream ATP failures.