The ATP Horizon is the specific future time window over which the Available-to-Promise (ATP) engine projects inventory receipts, production output, and demand consumption to determine if a customer order can be fulfilled. It establishes the boundary of visibility for the order promising logic; any requested delivery date falling beyond this horizon cannot be reliably committed against planned supply because the planning data is considered too uncertain or not yet generated.
Glossary
ATP Horizon

What is ATP Horizon?
The ATP Horizon defines the future time window over which the Available-to-Promise calculation projects inventory and capacity availability to make order commitments.
Setting the ATP Horizon involves balancing computational performance against planning confidence. A longer horizon, often aligned with the cumulative lead time of the product, provides greater visibility for quoting distant delivery dates but increases data processing load. A shorter horizon limits the promise window but ensures commitments are made only against firm, near-term supply schedules, reducing the risk of rescheduling and protecting On-Time In-Full (OTIF) performance.
Key Characteristics of the ATP Horizon
The ATP Horizon defines the temporal boundary for order promising calculations. It dictates how far into the future the system projects inventory positions and capacity availability to make reliable commitments.
Finite Time Boundary
The ATP Horizon is a finite, configurable window extending from the current date to a defined future point. Beyond this boundary, the system lacks the detailed supply and demand data required for precise promising. It typically spans the cumulative lead time of the product plus a planning buffer. Setting it too short risks uncommittable orders; setting it too long introduces forecast inaccuracy.
Demand Consumption Logic
The horizon is divided into distinct zones by the Demand Time Fence (DTF). Inside the DTF, the forecast is ignored, and only actual customer orders consume supply. Between the DTF and the horizon's end, a mix of forecast and actual orders is used. This prevents double-counting demand and ensures that a spike in real orders doesn't get hidden by an outdated statistical forecast.
Horizon-Specific Netting
The ATP Netting calculation behaves differently depending on the time bucket's position within the horizon. In near-term daily buckets, netting is precise. In future weekly or monthly buckets, it becomes more aggregated. The horizon's granularity directly impacts the accuracy of the delivery date quote. A horizon with daily buckets for the first 4 weeks provides far more reliable promises than one using only monthly buckets.
Global ATP Horizon Synchronization
In a multi-site Global ATP deployment, each plant or distribution center may have a different planning horizon. The global promising engine must synchronize these disparate horizons to create a unified view. A failure to align horizons can lead to a 'promising gap' where one site shows available supply that another site has already committed because its local horizon is shorter.
Frequently Asked Questions
Clear, technical answers to the most common questions about the ATP Horizon, its calculation, and its critical role in order promising logic.
The ATP Horizon is the future time window over which the Available-to-Promise (ATP) calculation projects inventory and capacity availability to make reliable order commitments. It is defined by the span of time for which the system has detailed, time-phased supply and demand data. This horizon typically extends from the current date through the cumulative lead time of all procured and manufactured components, plus any Demand Time Fence (DTF) . Within this boundary, every scheduled receipt from purchase orders and production runs is netted against every known demand, such as sales orders and forecasts, to generate a precise, period-by-period Projected Available Balance. Beyond the ATP Horizon, the system lacks the granular data required for a deterministic promise and must rely on rougher capacity checks or aggregate forecasts.
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Related Terms
Master the core concepts that define the ATP Horizon and its role in real-time order promising. These terms are essential for understanding how inventory and capacity projections translate into reliable delivery commitments.
Available-to-Promise (ATP)
The foundational real-time check that determines the uncommitted inventory and scheduled receipts available to promise to a customer order. The ATP calculation subtracts existing demand from supply within the ATP Horizon to generate a reliable delivery date, preventing stockouts and overselling.
Demand Time Fence (DTF)
A critical boundary within the ATP Horizon. Inside the DTF, the planning system ignores the demand forecast and uses only actual customer orders to drive supply requirements. This prevents the system from building inventory for a forecast that will never materialize as real sales.
Planning Time Fence (PTF)
A stabilization point within the master production schedule. Inside the PTF, planned orders are frozen and cannot be automatically rescheduled by the planning engine. This provides a stable window for factory floor execution and material procurement, preventing nervousness in the supply chain.
Cumulative ATP
A calculation method that sums available inventory across multiple periods within the ATP Horizon. This allows a single large order to be promised against the total supply available over a future timeframe, rather than being rejected because a single period lacks sufficient quantity.
ATP Netting Logic
The core arithmetic engine that computes the projected available balance at the end of each period within the ATP Horizon. The formula is: Projected Available = Prior Period Balance + Scheduled Receipts - Gross Demand. A positive balance indicates available-to-promise quantity.
Global ATP
An order promising check that searches for availability across a network of multiple plants and distribution centers simultaneously. The system evaluates sourcing rules and transportation costs to find the optimal fulfillment location, extending the effective ATP Horizon across the entire supply chain.

About the author
Prasad Kumkar
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
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