How a Glass Manufacturer Replaced Spreadsheets With AI
Shift-level
Not disclosed
Not disclosed
The facility relied on manual spreadsheets for daily plant data, with no shift-level visibility into operational efficiency. Placeholder and inconsistent job entries defeated naive ingestion, and analysis was limited to monthly retrospectives rather than actionable shift-level insight.
TrustEvals built an end-to-end ingestion-to-dashboard platform: Python ingestion via the Sheets API, a PostgreSQL warehouse with row-level security, and web dashboards. Engineered KPIs include furnace capacity utilization and glass pack efficiency, plus shift-level defect and downtime tracking and a shift-approval application.
A custom dirty-data job-mapping engine resolves placeholder and inconsistent entries using a composite business key, so KPI calculations stay reliable. The stack moves plant operations off spreadsheets onto an automated, warehouse-driven decisioning model.
Best fit for manufacturers stuck on spreadsheet-based reporting whose messy operational data needs cleaning before it can drive shift-level decisions.
Shift-level tracking
Dirty data resolved
Governed warehouse replaces sheets
Unmukt Raizada
The manufacturer worked with the experts to build an ingestion-to-dashboard platform on Python, a PostgreSQL warehouse with row-level security, and web dashboards. A custom composite-business-key engine resolves dirty job data, so defect and downtime tracking now runs at shift granularity instead of monthly aggregation.
The build centered on data synthesis and reporting: Python data ingestion via the Google Sheets API, a PostgreSQL warehouse with row-level security, engineered efficiency KPIs, web dashboards, a shift-approval application, and a composite-business-key cleaning engine. No specific AI model is named in the record.
Defect and downtime tracking now runs at shift level instead of monthly, a composite-business-key engine fixes placeholder and inconsistent job entries that defeated naive ingestion, and a governed PostgreSQL warehouse with engineered efficiency KPIs replaces manual spreadsheets.
The record does not specify a timeline. At the time of the case the solution was in user acceptance testing rather than confirmed full production.
It is best suited to manufacturers stuck on spreadsheet-based reporting whose messy operational data needs cleaning before it can drive shift-level decisions.
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