Atlas Smart Factory

Four plants ran largely manual production lines with defect rates that varied threefold between sites for reasons nobody could isolate. Quality data existed but sat in paper logs and per-machine controllers that never spoke to each other.

We instrumented the lines before automating them. Six months of sensor data made the variance legible — and showed that two of the four plants needed process changes rather than new equipment.

• Sensor instrumentation and data pipeline across all four plants
• Predictive maintenance models trained on the resulting baseline
• Targeted automation where the data justified the capital
• Operator upskilling programme running alongside deployment