This module automates the calculation, enforcement, and reporting of Net 30 and Net 60 payment terms for B2B transactions. It integrates with ERP systems to trigger invoices upon shipment confirmation and calculates days past due (DPD) automatically.
Configure standard templates in the system (e.g., 'Standard B2B-30', 'Extended Partner-60') including start date logic, grace periods, and currency rules.
Link the payment engine to the order management module so that the invoice date defaults to the shipment confirmation date or agreed delivery date.
Deploy algorithms to calculate DPD dynamically based on the specific term selected, ensuring accurate reporting for accounts receivable aging reports.
Set up automated email/SMS workflows for overdue invoices, distinguishing between standard reminders and final collection notices.

Phase 1: Core Automation (Q3); Phase 2: Predictive Analytics (Q4)
The system enforces strict credit policies by calculating the invoice date plus the agreed term (e.g., +30 days). It triggers automated reminders at T-5, T-1, and DPD+1. For Net 60 terms, it extends the grace period while maintaining real-time cash flow visibility for finance teams.
Automatically adjusts the due date based on the customer's negotiated credit limit or contract tier.
Provides a live dashboard of days past due for every open invoice, color-coded by severity.
Executes predefined reminder sequences without manual intervention based on the Net 30 or Net 60 term status.
Consolidate all order sources into one governed OMS entry flow.
Convert channel-specific payloads into a consistent operational model.
Target: < 32 for Net 30, < 62 for Net 60
Average Collection Cycle (Days)
99.5%
DPD Accuracy Rate
100% of Open Invoices
Automated Dunning Coverage
The Terms and Net Payment function begins by stabilizing current collections through rigorous data cleansing and automated invoice matching, eliminating manual reconciliation errors that delay cash flow. In the near term, we will implement real-time payment analytics dashboards to identify slow-moving accounts and enforce stricter credit limits for high-risk clients, directly reducing Days Sales Outstanding. Moving into the mid-term, our strategy shifts toward integrating dynamic discounting platforms, offering early pay incentives that incentivize customers to settle debts faster while improving liquidity ratios. Finally, over the long term, we will evolve into a predictive revenue management engine, utilizing machine learning models to forecast cash inflows with high precision and proactively adjust credit terms based on macroeconomic trends. This phased approach transforms our function from a reactive administrative role into a strategic asset that drives sustainable growth and financial resilience across the organization.

Strengthen retries, health checks, and dead-letter handling for source reliability.
Tune validation by channel and account context to reduce false-positive rejects.
Prioritize high-impact intake failures for faster operational recovery.
Support multiple channels in one process without separate manual reconciliation paths.
Handle campaign and seasonal spikes with controlled validation and queueing behavior.
Process mixed order profiles while maintaining consistent quality gates.