IoT sensor integration layer
An IoT sensor integration layer across all 6 assembly plants, ingesting machine and line data into one platform for real-time visibility.
Minda Industries · Manufacturing / Industrial IoT
An industrial IoT and traceability platform across six plants - connecting line data, AI defect detection, and end-to-end traceability so defects are caught before they reach OEMs.
Minda Industries is one of India's leading auto-component manufacturers, supplying switches, sensors, horns, and locking systems to OEM companies.
Across six assembly plants, production data lived in disconnected machine logs and paper travelers, which meant defects were typically caught after a batch had already moved downstream, or worse, after it had reached an OEM. Plant managers had no real-time view of line performance, so equipment slowdowns and quality drift went unnoticed until they showed up in yield reports days later. When an OEM raised a quality query, tracing a single component back to its raw material batch and shift required manually cross-referencing paper logs across plants, often taking days and straining the OEM relationship.
Baaz deployed an IoT sensor layer across all six assembly plants and built a real-time production monitoring platform with AI-based defect detection, giving plant teams visibility into quality and equipment performance as it happened. A QR/barcode-based traceability system was layered in to track every component from raw material to OEM delivery, backed by a compliance portal for OEM-facing reporting.
An IoT sensor integration layer across all 6 assembly plants, ingesting machine and line data into one platform for real-time visibility.
A real-time production monitoring dashboard giving plant teams live insight into line performance and quality as it happened.
An AI vision-based defect-detection system that flagged defects on the line, catching quality issues at the source instead of in yield reports.
QR/barcode-based traceability tracking every component from raw material to OEM delivery, turning multi-day audits into minutes.
A compliance portal for OEM-facing reporting, so quality queries could be answered fast with traceable evidence.
A plant manager mobile app for on-the-floor visibility and alerts, putting real-time quality and equipment data in their hands.
By putting real-time visibility and traceability directly into the hands of plant managers, Baaz helped Minda Industries catch quality issues at the source, protecting OEM relationships built over decades and turning audit days into audit hours.
Baaz deployed an IoT sensor layer across all six assembly plants and built a real-time production-monitoring platform with AI defect detection and end-to-end traceability. It replaced disconnected machine logs and paper travelers, giving plant teams live visibility into line performance and quality instead of waiting days for yield reports.
An AI vision-based system flags defects on the line at the source, rather than after a batch has moved downstream or reached an OEM. This cut the defect escape rate to OEMs by 67% and lifted first-pass yield from 93.1% to 98.4% across the six plants.
A QR/barcode-based traceability system tracks every component from raw material to OEM delivery, backed by a compliance portal for OEM-facing reporting. When an OEM raises a quality query, tracing a component back to its raw-material batch and shift now takes about two hours instead of three days.
The platform uses Python with YOLOv8 and LSTM models for vision and predictive analytics, OPC-UA for machine connectivity, Kafka for streaming, and InfluxDB for time-series data. A React dashboard and a React Native plant-manager app sit on top, with AWS IoT Core handling device connectivity.
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