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Days Payables Outstanding – Sequential Input-Change Attribution
x402
Days Payables Outstanding – Sequential Input-Change Attribution is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-22).
Calculates how each input change (average payables, credit purchases, period days) incrementally affects Days Payables Outstanding (DPO) by stepping through inputs one at a time in declared order, producing a waterfall attribution report.
Price / call$0.01
Success—
StatusUnknown
How this service works
Days payables outstanding - Sequential input-change attribution: Change each input from baseline to final in declared field order, reporting each incremental effect; order dependent. Metric: Days payables outstanding. Supplied figures only; returns JSON with formula, units and limitations. One of 50 calculators with 20 analysis modes. No live feed, financial advice or trade execution. 0.01 USDC includes one bounded sequential input-change attribution report.
Output
A JSON object containing: the DPO formula used, the sequential step-by-step attribution showing each input's incremental effect on DPO as inputs are changed one at a time from baseline to final values in declared field order, units for all figures, and explicit limitations of the sequential (order-dependent) methodology.
Example use cases
Quarter-over-quarter DPO driver analysis
“Our DPO jumped from 52 to 61 days this quarter. Baseline was 90-day period, average payables $8,200, credit purchases $42,000. New period is 90 days, average payables $9,500, credit purchases $40,000. Break down sequentially how each input change contributed to that DPO increase.”
Year-end payables performance review
“I need a waterfall breakdown of our DPO change for the annual review. Last year: 365-day period, average payables $1,200,000, credit purchases $9,800,000. This year: 365-day period, average payables $1,450,000, credit purchases $10,500,000. Step through each input change and tell me the incremental DPO effect.”
Supplier payment strategy scenario comparison
“We extended our payment terms and I want to see how it affected DPO step by step. Before: 30-day period, average payables $500,000, credit purchases $2,000,000. After: 45-day period, average payables $620,000, credit purchases $2,000,000. Give me the sequential attribution so I can see whether the period change or the payables balance change had more impact.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-finance-payable-days-waterfall?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to understand not just the total change in DPO between two periods, but the order-dependent contribution of each individual input. It is ideal for management reporting, financial storytelling, and waterfall charts where attribution sequence matters. Use it over a simple DPO delta calculation when stakeholders need to see which lever (payables balance, credit purchases, or period length) drove the change. Note that results are order-dependent; if you need order-independent (Shapley-style) attribution, a different analysis mode would be more appropriate.
Side effects
- credit_purchases of zero causes division-by-zero; schema enforces exclusiveMinimum:0
- period_days out of range (must be 1–3660) returns validation error
- Missing required fields (average_payables, credit_purchases, period_days) in before or after objects returns error
- Mismatched currency or period units between before and after states produce mathematically valid but economically meaningless results
- Very large values near the 1e12 maximum may introduce floating-point precision issues
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| after | object | |
| before | object |
Tags
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