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Days Inventory Outstanding – Sequential Input-Change Attribution (Waterfall)
x402
Days Inventory Outstanding – Sequential Input-Change Attribution (Waterfall) 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).
Computes a waterfall attribution of how changes in period_days, cost_of_sales, and average_inventory each incrementally affect Days Inventory Outstanding (DIO), stepping through inputs in declared field order.
Price / call$0.01
Success—
StatusUnknown
How this service works
Days inventory outstanding - Sequential input-change attribution: Change each input from baseline to final in declared field order, reporting each incremental effect; order dependent. Metric: Days inventory 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 DIO formula, the baseline DIO value, the incremental effect on DIO attributed to each input variable changed in declared field order (period_days, then cost_of_sales, then average_inventory), the total change in DIO from baseline to final, units, and a limitations note explaining that results are order-dependent.
Example use cases
Quarter-over-quarter DIO driver analysis
“My company's days inventory outstanding shifted between quarters — before: 365-day period, $120,000 cost of sales, $18,000 average inventory; after: 365-day period, $135,000 cost of sales, $20,000 average inventory. Can you run a waterfall attribution showing which input caused what change in DIO?”
Annual inventory efficiency review
“I need to explain to my CFO why our days inventory outstanding changed year over year. Last year was a 365-day period, $500,000 cost of sales, $80,000 average inventory; this year is a 365-day period, $480,000 cost of sales, $95,000 average inventory. Show the sequential step-by-step attribution of each driver's impact on DIO.”
Working capital optimization scenario
“We changed our reporting period from 365 to 180 days and our cost of sales dropped from $200,000 to $90,000 while average inventory stayed at $25,000. Walk me through the incremental DIO impact of each of those input changes in order.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-finance-inventory-days-waterfall?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to understand the order-dependent sequential contribution of each DIO input variable to a total change — ideal for waterfall chart preparation, CFO-level variance commentary, and supply chain driver analysis. Prefer it over a simple before/after DIO calculator when stakeholders need to see how each lever (period length, cost of sales, average inventory) individually moved the needle.
Side effects
- cost_of_sales of 0 or missing violates exclusiveMinimum constraint and returns a validation error
- missing required fields (average_inventory, cost_of_sales, period_days) in either before or after object returns a schema validation error
- period_days outside 1–3660 range returns a bounds error
- non-numeric values for financial fields return a type error
- before and after objects with identical values return a zero-change waterfall with no incremental effects
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| after | object | |
| before | object |
Tags
x402
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