Predictive maintenance
Use machine, sensor, and maintenance data to identify early warning signals and prioritize service actions before avoidable downtime.
Manufacturing AI
Reduce downtime and improve quality with governed plant intelligence
DigiScience helps manufacturers validate AI use cases across predictive maintenance, visual inspection, anomaly detection, quality analytics, and production decision support while plant experts retain operational accountability.
Best first proof
One line, one asset class, one inspection workflow, or one maintenance signal with clear baseline and improvement metric.
High-value use cases
Each use case connects business pain to AI outcome, secure cloud architecture, governance, and measurable value.
Visual inspection
Use computer vision to support defect detection, quality checks, and review queues with human verification and traceable decisions.
Production intelligence
Combine operational events, shift notes, quality data, and business context into decision dashboards and knowledge assistants.
Pilot deliverables
A manufacturing pilot should prove feasibility before production rollout.
Use-case baseline
Target line, asset, quality issue, event source, and measurable KPI.
Target line, asset, quality issue, event source, and measurable KPI.
AI workflow prototype
Prediction, classification, anomaly, vision, or knowledge workflow on approved sample data.
Prediction, classification, anomaly, vision, or knowledge workflow on approved sample data.
Governance controls
Human approval, audit trail, data classification, model monitoring, and failure-mode review.
Human approval, audit trail, data classification, model monitoring, and failure-mode review.
Scale roadmap
Production architecture, integration backlog, cost view, rollout risk, and success criteria.
Production architecture, integration backlog, cost view, rollout risk, and success criteria.
Pilot blueprint
Data inputs
Sensor history, alarms, maintenance work orders, failure records, quality events, asset hierarchy, operator notes, production context, and approved visual data.
Reference architecture
OT or IoT sources → secure ingestion and storage → feature or vision pipeline → model and evaluation → operator workflow → CMMS or quality action → monitoring and audit.
Target measures
Unplanned downtime, MTBF, maintenance lead time, false-alert rate, defect escape rate, review time, operator acceptance, and cost per prediction or inspection.
Start with one plant workflow and one accountable KPI
The pilot scope is selected after reviewing the data sources, integration path, operating constraints, safety requirements, and baseline measures.
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