MICROFACTOR

Turn quality and process signals into actionable alerts — not after-the-fact reports

Quality insight lagged line decisions. We started from acceptanced alert scenarios and connected process, QC, and planning data with a response loop.

A discrete manufacturing companyManufacturing

Challenge

Late detection, hard attribution

QC, process, and equipment data were siloed; anomalies were fully visible only after batches moved on. Leadership wanted “AI” without acceptanced alert definitions or cross-team closure — prior work stopped at dashboards.

Approach

Scenario definition → signal engineering → response drills

Lock acceptanced alert scenarios

With quality, process, and planning, define 1–2 high-value alerts (e.g. critical-step drift), false-positive tolerance, and response SLAs.

Signal and feature engineering

Align sampling and labels; prefer explainable features and rule/model mixes that ops can maintain.

Cross-team response drills

Wire alerts into existing tickets or stand-ups; expand lines only after a live drill.

Outcome

Quality alerts inside the line rhythm

  • Pilot steps have explainable alerts and named responders — not only post-hoc summaries.
  • Clear ops/IT ownership for data and models.
  • An expansion checklist and acceptance template — avoiding “another big screen.”

Capabilities

  • Data science
  • Ops AI
  • AI consulting