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An industrial glass-container manufacturer
From Manual Sheets To Shift-Level Plant Decisions
A full ingestion-to-dashboard stack that moved a glass plant off manual spreadsheets toward shift-level efficiency decisioning — including an engine that cleans the plant's own messy data.
The proof
The results, kept honest.
01
The challenge.
Daily plant data lived in spreadsheets with no shift-level view of efficiency, and the raw data was messy — placeholder and inconsistent job entries that defeated naive ingestion. The plant needed engineered efficiency metrics it could act on by shift, not a monthly retrospective.
02
The approach.
A Python ingestion path (Sheets API) into a Postgres warehouse with row-level security, feeding web dashboards.
A custom dirty-data job-mapping engine that resolves placeholder and inconsistent entries via a composite business key — so KPIs sit on trustworthy joins.
Engineered efficiency KPIs (furnace capacity utilization, glass pack efficiency), defect/downtime tracking at shift granularity, and a shift-approval app.
03
What shipped.
An ingestion → warehouse → dashboard stack with the job-mapping engine, shift-level defect/downtime tracking and a shift-approval app, currently in UAT.
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