MALION Ops Manual

Purpose and scope

MALION Ops is a decision-support workspace for masterbatch manufacturing. It centralizes line health, quality signals, and maintenance actions so teams can respond faster without losing traceability.

• Built for production supervisors, maintenance leads, and QA/lab teams.
• Supports daily decision-making; it does not replace formal SOPs or safety controls.
• All recommendations require human review before action.

Getting started

Use the left navigation to move between live operations, predictive recommendations, and historical analytics.

• Start on Overview to see plant-level status and shifts in throughput or scrap.
• Open Recommendations for AI, planned, and protocol-based actions.
• Use Maintenance to create or track work orders and PM schedules.

Predictive maintenance capability

The predictive layer highlights risk trends and probable failure windows so you can schedule work before downtime. Outputs are advisory and built to be transparent and auditable.

• Signals used: vibration, torque, temperature drift, energy draw, runtime hours, and QA deviations.
• Outputs: risk level, time-to-threshold estimate, and confidence band from historical patterns.
• Horizon: near-term (hours to days); it does not forecast long-range asset replacement.

Transparency and limitations

Predictions and recommendations are not guarantees. This system explains why it suggests an action and where uncertainty exists.

• Confidence scores are relative indicators, not probabilities of failure.
• False positives and missed detections are possible, especially with noisy sensors.
• Model suggestions never trigger automatic shutdowns or process changes.
• If upstream data is delayed, recommendations may be stale; always verify with line readings.

Recommendations workflow

Recommendations come from three sources and are organized by severity and status.

• AI Prediction: pattern-based signals from equipment trends.
• Planned / PM: calendar-driven maintenance schedules.
• Protocol: SOP or compliance-driven actions based on thresholds.
• Acknowledge, assign, or convert to work orders after validation.

App manual (core screens)

Use this as a quick guide to each section and the typical tasks performed there.

Data sources and governance

Data is consolidated from operational systems and should remain auditable.

• Typical sources include MES, SCADA/PLC feeds, LIMS, and maintenance logs.
• Every recommendation should link to the underlying signals or report.
• Audit trails capture who acknowledged or changed status and when.

Support and escalation

If a recommendation conflicts with on-line readings, pause and escalate.

• Contact the maintenance lead and QA for any critical recommendation.
• Flag data quality issues in the shift report with timestamps.
• Use SOP and EHS procedures as the final authority.