{"uid":"cap_7Ne1RtLb6pmhbnhttZEWB","slug":"unit-fill-rate-sequential-input-change-attribution-d906e1e3","name":"Unit Fill Rate Sequential Input-Change Attribution","description":"Unit fill rate - Sequential input-change attribution: Sequential input-change attribution for Unit fill rate. Supplied retail figures only. Local calculation and scenario analysis; no live inventory, account access, platform fee lookup or order execution. One of 25 profiles or calculations across 20 shared operations in this sector. 0.01 USDC includes one bounded sequential input-change attribution deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-retail-unit-fill-waterfall?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"after":{"type":"object","default":{"ordered_units":110.00000000000001,"fulfilled_units":104.50000000000001},"required":["fulfilled_units","ordered_units"],"properties":{"ordered_units":{"type":"number","default":100,"maximum":1000000000000,"minimum":0,"exclusiveMinimum":0},"fulfilled_units":{"type":"number","default":95,"maximum":1000000000000,"minimum":0}},"additionalProperties":false},"before":{"type":"object","default":{"ordered_units":100,"fulfilled_units":95},"required":["fulfilled_units","ordered_units"],"properties":{"ordered_units":{"type":"number","default":100,"maximum":1000000000000,"minimum":0,"exclusiveMinimum":0},"fulfilled_units":{"type":"number","default":95,"maximum":1000000000000,"minimum":0}},"additionalProperties":false}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_nowiA0zdYOvVx0iuHvLJg","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Performs sequential input-change attribution (waterfall analysis) to decompose how changes in ordered_units and fulfilled_units drive changes in unit fill rate.","exampleAgentPrompt":"Run a sequential attribution waterfall on my unit fill rate: before we had 100 ordered units and 95 fulfilled, and after we had 110 ordered units and 104.5 fulfilled — break down how much each input change contributed to the fill rate shift.","exampleUseCases":[{"title":"Diagnose weekly fill rate drop","prompt":"My fill rate dropped this week — before I had 200 ordered units and 194 fulfilled, now I have 210 ordered and 196 fulfilled. Can you run a sequential attribution to tell me whether the drop came from more orders or fewer fulfillments?"},{"title":"Validate fulfillment improvement impact","prompt":"We improved our fulfillment process and want to prove it: before the change we had 500 ordered units and 460 fulfilled, after we had 520 ordered and 495 fulfilled. Run the waterfall attribution to show how much of the fill rate gain came from better fulfillment versus order volume changes."},{"title":"Scenario analysis for sales planning","prompt":"I'm modeling a scenario where ordered units go from 1000 to 1200 but fulfilled units only increase from 950 to 1100 — run the sequential input-change attribution so I can see how each factor individually impacts the fill rate."}],"resultDescription":"A structured waterfall breakdown showing: the before and after unit fill rates, the total fill rate delta, and the sequential attribution of how much each input variable (ordered_units change, then fulfilled_units change) contributed to the overall fill rate movement — enabling users to isolate which factor drove the change.","failureModes":["ordered_units of zero rejected (exclusiveMinimum: 0 enforced)","fulfilled_units exceeding ordered_units may produce fill rates above 100% — no validation guardrail described","values exceeding 1,000,000,000,000 rejected by schema maximum","missing required fields (ordered_units or fulfilled_units in either before/after) returns validation error","payment failure or insufficient USDC balance prevents execution"],"whenToPreferThis":"Choose this endpoint when you need to decompose a change in unit fill rate into its component input drivers (ordered units vs. fulfilled units) using a sequential attribution / waterfall methodology. It is ideal for retail analytics, supply chain reporting, and scenario planning where you have two snapshots of fulfillment data and want to understand causality. It operates entirely on supplied figures with no live data access, making it suitable for offline analysis of your own exported retail metrics.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-23T02:01:13.215Z","isFirstParty":false,"canonicalSlug":"unit-fill-rate-sequential-input-change-attribution-d906e1e3"}