AI Manufacturing Analytics Tool — Upload Production Data, Ask

What this export contains

What usually goes wrong with it

• OEE calculation requires combining availability, performance, and quality data

  Your production export has planned vs actual units and downtime but calculating OEE requires a formula that combines three components — rarely done on the floor without a dedicated tool.

  DataMimi Ask 'what is OEE by production line this month?' and Datamimi calculates availability, performance, and quality factors from your export columns and returns OEE per line.

• Defect rate by shift is never compared systematically

  Quality issues are logged per incident but identifying whether the night shift consistently produces more defects requires aggregating by Shift across weeks of data.

  DataMimi Ask 'which shift has the highest defect rate over the last 8 weeks?' and Datamimi aggregates defects by Shift across all rows and calculates the rate.

• Downtime cause analysis is done verbally, not from data

  Downtime events are logged by cause code but nobody aggregates which causes account for the most total minutes lost per month.

  DataMimi Ask 'which downtime cause code accounts for the most total minutes lost this quarter?' and Datamimi groups by Downtime Cause and sums the minutes.

• Scrap cost is tracked in units, not dollars

  The production export shows scrap units but converting that to dollar cost requires a VLOOKUP to the product cost sheet — a step that's skipped in most weekly reviews.

  DataMimi Upload your production export and product cost sheet and ask 'what is the total scrap cost by SKU this month?' — Datamimi cross-references both files.

Common questions