Overview
A data foundation built for manufacturing quality
A tier-one supplier to the Apple ecosystem manufactures high-volume precision components and assemblies across multiple campuses. AOI, CCD, X-ray, SPI, electrical test, and reliability stations generate a continuous stream of images, logs, coordinates, and defect records.
Challenge
Inspection growth had outpaced line-side storage
Local disks and independent file servers created inconsistent folder structures and retention practices. A representative factory could generate more than 10 million inspection images and over 10 TB of quality data per day, making manual retrieval, cross-site sharing, and capacity expansion increasingly difficult.
- Daily inspection growth exceeded what line-side disks and conventional file servers could manage efficiently.
- Different equipment brands generated incompatible naming conventions and metadata.
- Quality teams needed to retrieve evidence across factories without copying entire directories.
Solution
One governed platform for the complete data lifecycle
Dataram IDM standardizes data ingestion and metadata across equipment and factories. It consolidates inspection files in elastic object storage, indexes them by product, lot, serial number, process, equipment, and time, and applies automatic compression, tiering, retention, and deletion policies. Open interfaces connect YMS, MES, and QMS workflows.
Equipment-side collection normalizes data and applies manufacturing tags automatically.
Object storage delivers elastic capacity for small files, images, logs, and test records.
Open APIs make governed files available to existing yield, execution, and quality systems.
Results
Measurable value at production scale
Quality teams gain a consistent cross-site traceability workflow instead of searching file paths. Line-side storage pressure is reduced, total cost is better controlled, and governed historical inspection records can be prepared for defect analytics and AI model training.
- 100-billion-file-scale search supports rapid, multidimensional quality investigations.
- Automated lifecycle policies reduce repetitive administration and control retention cost.
- Historical evidence becomes reusable for process analysis and governed AI datasets.
Customer identity and selected implementation details have been anonymized to protect confidentiality. No source diagrams or architecture images are reproduced in this article.
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