Real-Time Order Promising & Available-to-Promise
Accurate delivery date commitments using real-time ATP calculations, capacity modeling, and demand forecasting to balance customer expectations with operational feasibility.
Why This Matters
What It Is
Accurate delivery date commitments using real-time ATP calculations, capacity modeling, and demand forecasting to balance customer expectations with operational feasibility.
Current State vs Future State Comparison
Current State
(Traditional)Static lead time commitments based on simple rules (e.g., 'in-stock ships in 3-5 business days'). No real-time consideration of fulfillment capacity, carrier transit times, or Inventory Management allocation. ATP calculated from on-hand Inventory Management without considering existing demand commitments or inbound receipts. Frequent promise date misses leading to customer dissatisfaction. Over-promising to win orders creates operational strain.
Characteristics
- • Oracle SCM Cloud
- • SAP ERP
- • Microsoft Dynamics 365 Supply Chain Management
- • Fluent Commerce
- • Inventory Management Systems
- • Order Management Systems (OMS)
Pain Points
- ⚠ Data Latency: Static or batch-updated ATP calculations can lead to overselling.
- ⚠ Complexity in Configured Products: Managing ATP for configurable items requires complex calculations.
- ⚠ Capacity Constraints: ATP does not consider production capacity, leading to unrealistic promises.
- ⚠ Integration Challenges: Disparate systems may not synchronize well, causing inaccurate availability data.
- ⚠ Manual Processes: Use of Excel and email increases errors and slows response times.
- ⚠ Lack of Standardization: Variability in ATP/CTP implementation can lead to inconsistent customer experiences.
- ⚠ Static data updates can lead to inaccuracies in real-time order promising.
- ⚠ Complexity in managing lead times and component availability for configurable products.
Future State
(Agentic)AI-powered order promising engine calculates precise delivery dates in real-time by orchestrating multiple data sources: current Inventory Management positions, reserved/allocated Inventory Management, in-transit inbound receipts, fulfillment node capacity, carrier service levels, and expected processing times. Machine learning predicts fulfillment throughput and identifies capacity constraints 24-48 hours in advance. System performs capable-to-promise (CTP) analysis considering future Inventory Management from production or inbound shipments. Dynamic safety stock calculations protect against demand volatility while maximizing sellable ATP. Automated promise optimization balances aggressive promises (conversion) vs. conservative promises (operational buffer). Real-time promise updates displayed to customers during checkout.
Characteristics
- • Real-time Inventory Management (on-hand, reserved, allocated, in-transit)
- • Fulfillment node capacity and throughput
- • Order queue and processing times
- • Carrier service levels and transit times
- • Demand forecasts by SKU
- • Historical promise vs. actual delivery performance
- • Production schedules (for make-to-order or CTP)
Benefits
- ✓ 95-98% promise accuracy (vs 85-90%)
- ✓ 95-99% ATP accuracy through real-time reservation
- ✓ 70-85% reduction in promise miss rate (2-4% vs 10-15%)
- ✓ 10-15% conversion improvement from accurate fast-delivery promises
- ✓ 20-30% reduction in safety stock through dynamic optimization
Is This Right for You?
This score is based on general applicability (industry fit, implementation complexity, and ROI potential). Use the Preferences button above to set your industry, role, and company profile for personalized matching.
Why this score:
- • Applicable across multiple industries
- • Moderate expected business value
- • Time to value: 3-6 months
- • (Score based on general applicability - set preferences for personalized matching)
You might benefit from Real-Time Order Promising & Available-to-Promise if:
- You're experiencing: Data Latency: Static or batch-updated ATP calculations can lead to overselling.
- You're experiencing: Complexity in Configured Products: Managing ATP for configurable items requires complex calculations.
- You're experiencing: Capacity Constraints: ATP does not consider production capacity, leading to unrealistic promises.
This may not be right for you if:
- Requires human oversight for critical decision points - not fully autonomous
Parent Capability
Real-Time Operational Dashboards
Real-time operational intelligence with AI-powered anomaly detection and prescriptive recommendations achieving substantially faster issue detection and resolution.
What to Do Next
Related Functions
Metadata
- Function ID
- function-ofs-order-promising-atp