Your customers remember broken promises more than perfect deliveries.
Tell someone their order ships Tuesday, then ship it Friday? You’ve damaged trust that takes months to rebuild. Order promising prevents this by calculating realistic delivery dates based on real inventory, production capacity, and lead times.
Instead of guessing, you commit to specific dates using real-time data. You check what’s actually available, account for processing time, and give customers dates you can hit every time.
Supply chain disruptions have made delivery accuracy a competitive advantage. Late deliveries trigger service calls, expedited shipping costs, and lost customers when trust erodes.
What you’ll learn
How order promising methods (ATP, CTP) work and when to use each delivery date control method
Step-by-step implementation process for accurate delivery date calculations
Best practices for integrating inventory systems and managing customer expectations
Key metrics for measuring promise accuracy and business impact
TL;DR:
Key takeaways
Order promising prevents overselling by reserving inventory for confirmed customer commitments
Available-to-Promise (ATP) works for stock items while Capable-to-Promise (CTP) handles make-to-order production
Real-time inventory integration reduces promise failures compared to manual calculations
Proactive communication about delays maintains customer trust even when issues occur
Order promising explained
Simple definition
Order promising bridges the gap between customer orders and delivery reality. Instead of guessing when orders will ship, order promising calculates exact dates using current inventory, production schedules, and shipping requirements.
NOTE: Order promising is the commitment to specific delivery dates based on real-time availability calculations, ensuring companies can fulfill orders as promised while maintaining operational flexibility.
This goes beyond checking stock availability. True order promising considers uncommitted inventory, planned receipts, existing customer orders, and processing time needed for picking, packing, and shipping.
Why it matters in modern supply chains
Supply chain disruptions have made accurate promising essential for survival. Companies that guess at delivery dates lose customers to competitors who provide reliable commitments.
Research shows that customers return to retailers after reliable delivery experiences, while many make repeat purchases specifically due to positive delivery outcomes. Late deliveries trigger customer service calls, expedited shipping costs, and potential chargebacks. More damaging is the long-term impact on customer lifetime value when trust erodes.
Key order promising methods
Different businesses need different delivery control methods to optimize their order promising approach. Your choice depends on inventory complexity, production requirements, and customer expectations.
Sales lead time
Sales lead time uses fixed timeframes between order placement and shipment. Add five business days to every sales order regardless of actual inventory or capacity. Simple. Predictable. Often wrong.
This method works for businesses with stable inventory and predictable lead times. But it disappoints customers when reality doesn’t match assumptions.
Available-to-Promise (ATP)
ATP calculates exactly what you can commit to customers on specific dates. It combines uncommitted inventory, planned receipts, scheduled shipments, and existing commitments into one calculation.
The ATP formula: Previous period ATP + Current period receipts – Current period issues – Net requirements for future periods
This atp available to promise calculation includes net issue quantity and inventory receipts to determine the uncommitted inventory balance for each current date.
PRO TIP: Run ATP calculations in real-time to prevent overselling during peak ordering periods. Static calculations updated only nightly create inventory conflicts and broken promises.
ATP + Issue margin
This approach adds processing time to ATP calculations. The promised delivery date equals ATP date plus warehouse processing time for picking, packing, and loading. Essential for companies with complex packaging or capacity constraints during peak periods.
Capable-to-Promise (CTP)
CTP capable to promise extends ATP by including production capacity alongside material availability. While ATP assumes unlimited capacity, capable to promise considers manufacturing schedules, machine availability, labor capacity, and supplier lead times.
Capable to promise is essential for make-to-order environments where products are manufactured after order receipt. The system analyzes order requirements through bill-of-materials and routing information to identify capacity constraints. CTP calculations evaluate a company’s production capacity to determine the earliest ship date for each sales order.
Method comparison
The table below shows when to use each order promising method based on your business requirements.
| Method | Best for | Data required | Implementation complexity | Accuracy level |
|---|---|---|---|---|
| Sales lead time | Simple inventory, predictable lead times | Historical averages | Low | Basic |
| ATP | Stock items, multiple locations | Real-time inventory | Medium | High |
| ATP + issue margin | Complex packaging, capacity constraints | Inventory + processing times | Medium | High |
| CTP | Make-to-order, custom manufacturing | Inventory + production capacity | High | Highest |
How the order promising process works
Order promising follows five steps that transform customer requests into reliable delivery commitments. Each step builds on the previous one to create accurate promises.
The process starts when customers place orders or request delivery quotes. The system captures product specifications, quantities, delivery location, and customer priority levels in real-time. Next, the system performs ATP calculations checking current inventory, committed stock, and planned receipts while identifying optimal fulfillment locations.
For unavailable items or make-to-order products, the system evaluates production capacity including schedules, machine availability, and supplier lead times. After completing analysis, the system generates delivery commitments and reserves inventory or capacity to prevent overselling.
Finally, sales order promising requires ongoing monitoring as conditions change. Systems provide real-time alerts for potential issues, enabling proactive communication and alternative solutions.
Benefits and common challenges
Accurate order promising delivers measurable benefits across customer satisfaction, operations, and financial performance. Enhanced customer satisfaction occurs when orders arrive as promised. Reduced cancellations result from accurate delivery information upfront. Improved inventory management enables better optimization through demand visibility.
However, implementation faces predictable obstacles. Data accuracy issues from inventory discrepancies and outdated information create unrealistic promises. Supply chain disruptions invalidate existing commitments. System integration problems prevent effective promising when systems don’t communicate.
Solutions include implementing data governance processes, building resilience through contingency planning and safety stock, and establishing system integration through APIs or unified platforms.
Best practices for implementation
Successful order promising requires careful attention to data quality, system integration, and customer communication. Start with these foundational elements:
Real-time data integration – Establish systems providing accurate inventory information across all locations including on-hand quantities, committed stock, and planned receipts
Safety margin management – Build appropriate buffers into calculations accounting for processing time variability and transportation delays based on historical performance
Proactive communication protocols – Develop standardized processes for notifying customers about delays with clear explanations and revised delivery dates
Performance monitoring – Track promise accuracy percentage, on-time delivery rates, and customer satisfaction scores
Modern supply chain management systems include built-in engine capabilities for planning optimization that can handle complex CTP scenarios across multiple order lines. These systems analyze delayed demand, delayed supply orders, and delayed demand orders to provide cumulative ATP calculations.
Many businesses partner with experienced third-party logistics providers who have sophisticated order promising capabilities built into their fulfillment operations, eliminating the need for complex in-house system development.
PRO TIP: Start with simple ATP calculations and gradually add complexity as your systems mature. Attempting sophisticated CTP without strong data foundations often produces worse results than simple methods.
Advanced order promising considerations
Sophisticated order promising implementations must account for various delivery control methods and supply chain management complexities. When managing multiple sales orders simultaneously, businesses need systems that can handle atp delayed demand situations and known demand patterns.
The requested delivery date from customers may not align with available inventory or production capacity. Effective order management systems calculate multiple promised date scenarios, considering current date constraints and earliest ship possibilities within the appropriate time frame.
For purchase order fulfillment, systems must track receipt dates and planned supply to ensure accurate promises. This becomes critical when dealing with delayed demand across multiple order lines, where business objectives require keeping customers happy despite supply chain disruptions.
When evaluating fulfillment partners, businesses should consider working with the best fulfillment companies that have proven track records of accurate order promising and reliable delivery performance.
Key metrics to track
Measuring order promising performance requires tracking both operational metrics and customer impact indicators. These metrics provide comprehensive visibility into effectiveness:
Promise accuracy percentage – Orders delivered on or before the promised date
On-time in-full (OTIF) delivery – Combines delivery timing with order completeness
Average promise lead time – Time between order placement and promised delivery
Customer satisfaction scores – Track satisfaction related to delivery experience
Advanced metrics include cumulative ATP performance, delivery date control method effectiveness, and planning optimization results to ensure your order promising approach continues to reliably promise delivery dates while meeting business objectives.
Companies seeking to improve their order promising capabilities should understand how to choose a 3PL partner that can provide advanced order promising technology and proven delivery performance.
