{"uid":"cap_77HMGCKs4Tc3B4vKYDBFS","slug":"replenishment-timing-string-normalisation-and-two-measure-correlation-ceb1ee7f","name":"Replenishment Timing String Normalisation and Two-Measure Correlation","description":"Replenishment timing — String normalisation + Two-measure correlation: String normalisation + Two-measure correlation for supplied replenishment timing records (lead_days, received_units). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-retail-12-033?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","store_group","date","lead_days","received_units"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"lead_days":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"store_group":{"type":"string","default":"","maxLength":80},"received_units":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","lead_days":10,"store_group":"group-a","received_units":1},{"date":"2026-10-02","row_id":"row-2","lead_days":20,"store_group":"group-b","received_units":2},{"date":"2026-10-03","row_id":"row-3","lead_days":15,"store_group":"group-a","received_units":3},{"date":"2026-10-04","row_id":"row-4","lead_days":30,"store_group":"group-b","received_units":4}],"maxItems":200,"minItems":1}}},"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_KfwSB9DZ0otvFAJ9i_q92","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 string normalisation and two-measure correlation analysis on replenishment timing records (lead_days, received_units) for up to 200 rows, returning two analytical reports.","exampleAgentPrompt":"Can you run string normalisation and a two-measure correlation between lead_days and received_units on my replenishment records? I have 4 rows: row-1 for group-a on 2026-10-01 with lead_days=10 and received_units=1, row-2 for group-b on 2026-10-02 with lead_days=20 and received_units=2, row-3 for group-a on 2026-10-03 with lead_days=15 and received_units=3, and row-4 for group-b on 2026-10-04 with lead_days=30 and received_units=4.","exampleUseCases":[{"title":"Retail store replenishment correlation check","prompt":"I have 50 rows of replenishment timing data across my store groups — each row has a date, store_group, lead_days, and received_units. Can you normalize the store group strings and compute the correlation between lead days and units received so I can see if longer lead times actually result in more units arriving?"},{"title":"Supply chain timing data cleanup","prompt":"My replenishment dataset has inconsistent store group labels and I need two reports: string normalisation to clean them up, and a correlation between lead_days and received_units. Here are my 10 rows with row IDs row-1 through row-10, store groups like 'Group A', 'group-a', 'GROUP A' for dates in October 2026, with lead days ranging from 5 to 45 and received units from 10 to 200."},{"title":"Inventory lead time analysis for two store groups","prompt":"Can you analyze my 30-row replenishment dataset covering group-north and group-south stores from 2026-09-01 to 2026-09-30? I want the string normalisation report for the store_group field and a two-measure correlation between lead_days and received_units to understand if our lead times are predictive of how many units we actually receive."}],"resultDescription":"Returns two analytical reports: (1) a string normalisation report detailing cleaned and standardised values for text fields such as store_group, and (2) a two-measure correlation report quantifying the statistical relationship between lead_days and received_units across the submitted rows. No external lookups or predictions are made; all analysis is performed locally on the supplied data.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, store_group, date, lead_days, received_units) cause row rejection","Invalid date format (not YYYY-MM-DD) causes parsing failure","Null values in lead_days or received_units may affect correlation computation","Fewer than 2 rows may produce degenerate or undefined correlation results","Values exceeding numeric bounds (±1,000,000,000,000) are rejected"],"whenToPreferThis":"Choose this endpoint when you need to simultaneously clean string fields and compute a statistical correlation between two numeric replenishment metrics (lead_days and received_units) on a batch of up to 200 rows, without requiring any external data, live predictions, or professional assessment. It is ideal for offline retail analytics pipelines where you supply your own dataset and need two standardised local reports at low cost.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T18:35:31.417Z","isFirstParty":false,"canonicalSlug":"replenishment-timing-string-normalisation-and-two-measure-correlation-ceb1ee7f"}