Every manufacturer knows their theoretical capacity. Most manufacturers do not know, with confidence, what is actually happening on their production floor right now.
This use case describes how Micraft MES closes that gap — replacing manual shift reports and estimated OEE with live production data that operations teams can actually act on during the shift, not after it.
There is a fundamental difference between production monitoring and production reporting. Reporting tells you what happened. Monitoring tells you what is happening — and creates the opportunity to act before the situation gets worse.
Most manufacturing businesses are doing reporting and calling it monitoring. A shift supervisor walks the floor at 10am and estimates output. An OEE figure is calculated at end of shift from manually compiled data that may reflect what the supervisor remembered rather than what the machine actually did. A downtime event is attributed to a reason that the maintenance team discussed afterward — not recorded by the operator at the moment it happened.
By the time a manager knows that Line 3 has been down for 45 minutes, 45 minutes are already gone. An alert at minute 5 changes the outcome. A report at minute 45 does not.
When the production team and the management team have different OEE numbers for the same shift, neither is trusted. Improvement initiatives built on data nobody trusts produce improvements nobody can verify.
When inspection happens at the end of the process rather than inline, defects are discovered after an entire batch has been produced to the same defective specification.
Three days after a significant downtime event, the investigation begins. The downtime reason recorded is the supervisor's best recollection — not the operator's direct input.
These are not edge cases. They are the daily operational texture of manufacturing businesses running without real-time production monitoring.
Every production order is tracked from release to completion — with real-time output quantity, cycle time against standard, and completion forecast. A manager opening the dashboard sees the current state of every order on every line — not the state as of the last manual update.
OEE is calculated continuously from actual events: Availability from machine start/stop times; Performance from actual output rate vs standard; Quality from inline inspection results — not estimated from shift summary totals.
When a machine stops, Micraft MES prompts the operator to record the reason. Categories are configured to your operation. The reason is recorded at the moment it happens, by the person who was there. This is the foundation of effective root cause analysis.
A digital ANDON system triggers alerts (email, SMS, audio, visual) to the right people the moment a defined condition is detected. The shift supervisor and maintenance team are informed at the moment something needs attention.
Quality checkpoints defined within the production workflow where defects originate. Trend analysis from inline data identifies drift before the defect rate crosses an unacceptable threshold — enabling process adjustment.
The morning meeting runs on data that is already available — not on reports being compiled while the meeting is in progress. Decisions about maintenance, overtime, and procurement are made on current performance data.
The shift starts with a clear picture of every order and machine. Deviations are visible as they develop. The shift report at end of shift is generated automatically from data the system collected all shift.
Downtime data with accurate reasons is the foundation of every maintenance conversation. MTBF analysis identifies assets with the highest failure frequency based on facts, not recency bias.
Inline inspection data provides a continuous picture of process quality. Statistical process control charts built from actual data identify drift before it produces defects.
Based on Micraft MES implementations across manufacturing environments:
From capturing reasons at source and enabling accurate root cause analysis.
From eliminating visibility gaps that allowed bottlenecks and idle time to accumulate.
On every unit, batch, operator, and machine — replacing partial manual records.
A single source of truth that the production and management teams agree on.
Operator terminals at each station. Tablet interfaces for mobile supervisors. Direct machine interface via OPC-KepServer for automated data capture from PLCs and SCADA systems.
Standard deployment goes live in 30 to 60 days. Phased rollout available for multi-line and multi-site implementations.
Production orders received automatically from SAP, Oracle, Microsoft Dynamics. Completion actuals pushed back to ERP without manual re-entry.
Cloud, on-premise, or hybrid deployment. Active/passive high-availability cluster configuration for critical environments.
