Capable-to-Promise (CTP) is a real-time availability check that determines whether a new customer order can be fulfilled by a specific date by simultaneously evaluating production capacity, raw material availability, and transportation resources—not just on-hand inventory. Unlike Available-to-Promise (ATP), which only checks uncommitted stock, CTP dynamically simulates the entire manufacturing and logistics pipeline to generate a feasible, resource-constrained delivery commitment.
Glossary
Capable-to-Promise (CTP)
Capable-to-Promise (CTP) is a multi-resource availability check that extends beyond on-hand inventory to evaluate whether production capacity, raw materials, and transportation resources can be allocated to fulfill a new order by a specific date.
ORDER FULFILLMENT LOGIC
What is Capable-to-Promise (CTP)?
Capable-to-Promise (CTP) is a deterministic order promising logic that extends beyond static inventory checks to dynamically evaluate the availability of production capacity, raw materials, and transportation resources before committing to a delivery date.
When an order is entered, the CTP engine performs a multi-resource finite capacity check against the master production schedule, bill of materials, and routing plans. If current resources are insufficient, the system calculates the earliest possible date by modeling lead times for component procurement, machine scheduling, and outbound shipping. This prevents overpromising and ensures that every committed date is backed by a verified, executable supply chain plan.
CAPABLE-TO-PROMISE
Core Characteristics of CTP
Capable-to-Promise (CTP) extends the basic Available-to-Promise (ATP) check by evaluating not just on-hand inventory, but also the simultaneous availability of production capacity, raw materials, and transportation resources to guarantee a delivery date.
01
Multi-Resource Constraint Evaluation
Unlike ATP, which only checks uncommitted inventory, CTP performs a simultaneous multi-resource feasibility check. The algorithm verifies the availability of production capacity (machine hours, labor), raw materials (dependent demand), and transportation slots before committing to an order. This prevents the 'siloed promise' problem where inventory exists but cannot be produced or shipped in time.
02
Finite Capacity Scheduling Integration
CTP is deeply integrated with the Master Production Schedule (MPS) and shop floor calendars. It does not assume infinite capacity. Instead, it loads the new order onto a finite model of the factory to calculate a realistic completion date. Key inputs include:
- Routings: The specific sequence of work centers required
- Bill of Materials (BOM): The precise component structure
- Shift calendars: Available working hours and maintenance windows
03
Dynamic Lead Time Calculation
CTP systems dynamically calculate cumulative lead times by exploding the BOM and offsetting for queue, setup, run, and transit times at each level. This is not a static lookup; it is a real-time backward scheduling exercise from the requested delivery date. If a component has a 3-day lead time and assembly takes 1 day, the system verifies capacity 4 days before the ship date.
04
Alternative Sourcing Logic
Advanced CTP engines incorporate substitution rules and alternative routings. If the primary work center is overloaded, the system can automatically evaluate an alternate production line. If a specific raw material is constrained, it can check for an approved substitute component. This ensures the promise is made against the best feasible path, not just the default path.
05
Real-Time Order Promising
CTP is designed for synchronous, real-time execution within an Order Management System (OMS). When a customer service representative enters an order, the CTP engine returns a reliable promise date within seconds. This requires high-speed in-memory processing of the supply chain model to avoid latency in the sales cycle.
06
CTP vs. ATP: Scope Comparison
The fundamental distinction lies in the scope of the check:
- ATP: 'Do we have it?' (Uncommitted inventory only)
- CTP: 'Can we make it and move it by that date?' (Inventory + Capacity + Materials + Transport) CTP is essential for Make-to-Order (MTO) and Configure-to-Order (CTO) environments where finished goods inventory does not exist before the order.
ORDER PROMISING LOGIC
CTP vs. ATP: Key Differences
A comparison of the resource dimensions evaluated by Available-to-Promise (ATP) and Capable-to-Promise (CTP) when committing to a customer delivery date.
| Feature | Available-to-Promise (ATP) | Capable-to-Promise (CTP) |
|---|---|---|
Primary Constraint Checked | Uncommitted on-hand and planned inventory | Inventory, production capacity, and material availability |
Scope of Evaluation | Single echelon (finished goods) | Multi-echelon (raw materials, WIP, finished goods) |
Production Capacity Considered | ||
Raw Material Availability | ||
Transportation Lead Time | ||
Calculation Complexity | Database query | Finite capacity scheduling and BOM explosion |
Response Time | < 1 sec | 1-30 sec |
Typical Use Case | Make-to-stock (MTS) environments | Make-to-order (MTO) and configure-to-order (CTO) environments |
CAPABLE-TO-PROMISE EXPLAINED
Frequently Asked Questions
Clear, technically precise answers to the most common questions about Capable-to-Promise (CTP) logic, its mechanisms, and its role in multi-echelon order fulfillment.
Capable-to-Promise (CTP) is a real-time order promising algorithm that determines whether a specific customer order can be fulfilled by a requested date by simultaneously checking the availability of on-hand inventory, unallocated production capacity, and required raw material components. Unlike a simple Available-to-Promise (ATP) check that only looks at finished goods stock, CTP dynamically evaluates the entire supply chain's ability to produce and deliver. When an order is received, the CTP engine performs a multi-resource availability check: it explodes the bill of materials (BOM) to verify component availability, checks the finite production schedule for an open time slot, and confirms transportation resources. If any constraint fails, the system calculates the earliest feasible date by simulating the completion of upstream activities, providing a reliable, capacity-feasible delivery promise.
Enabling Efficiency, Speed & Accuracy
Intelligent Analysis, Decision & Execution
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Talk to Us
Search across company data
Give teams answers from docs, tickets, runbooks, and product data with sources and permissions.
Useful when people spend too long searching or get different answers from different systems.
Enterprise searchRAGPermissions
Read more
Automate internal workflows
Use AI to route work, draft outputs, trigger actions, and keep approvals and logs in place.
Useful when repetitive work moves across multiple tools and teams.
AI agentsWorkflow automationGovernance
Read more
Add AI to products and internal tools
Build assistants, guided actions, or decision support into the software your team or customers already use.
Useful when AI needs to be part of the product, not a separate tool.
AI integrationDecision supportModel routing
Read more
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.
LinkedIn
Limited slotsGet a Free AI Consultation
Partnered with leading AI, data, and software stack.
How We Work
Custom AI workflows for your Business
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
01
Review the use case
We understand the task, the users, and where AI can actually help.
Read more02
Pick the right approach
We define what needs search, automation, or product integration.
Read more03
Build the first useful version
We implement the part that proves the value first.
Read more04
Improve from there
We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
Talk to Us