Initial Situation
The manufacturer produced precision automotive components across six CNC and automated assembly lines. While the facility had some PLC-based machine signals available, the company did not operate a fully integrated Quality Management System capable of monitoring operational performance in real time.
Instead, production performance and downtime were recorded manually at the end of each shift using spreadsheets and handwritten operator logs. This approach created major delays between when issues occurred and when they were investigated.
The plant leadership suspected that a significant amount of production capacity was being lost, but they lacked accurate operational data to quantify the problem.
Baseline Operational Metrics (Before Session Pilot)
A three-week operational audit was conducted before implementing Session Pilot to establish baseline KPIs.
Production Schedule
3 shifts per day
8 hours per shift
24 operating hours per day
6 production lines
Total planned production time per day:
24 hours × 6 lines = 144 machine-hours/day
Total planned production time per month:
144 hours/day × 30 days ≈ 4,320 machine-hours/month
Measured Downtime
Manual logs showed frequent machine stoppages that were poorly categorized.
Estimated downtime per line per day:
2.1 hours
Total downtime across the facility:
2.1 hours × 6 lines = 12.6 hours/day
Monthly downtime:
12.6 hours/day × 30 days = 378 hours/month
Percentage of lost production capacity:
378 ÷ 4320 ≈ 8.7% downtime
However, because downtime reporting was inconsistent, engineers believed the real number was closer to 10–12%.
Baseline OEE
Before deployment, the plant estimated OEE using periodic manual calculations.
Metric | Baseline |
Availability | 78% |
Performance | 86% |
Quality | 94% |
Calculated OEE:
0.78 × 0.86 × 0.94 ≈ 63% OEE
Operational Response Time
When a machine stopped:
Operator manually informed supervisor
Supervisor contacted maintenance
Maintenance team diagnosed issue
Average response time:
25 minutes
Many stoppages lasted longer simply because supervisors were unaware of the issue immediately.
Key Processes Impacted by Poor QMS Visibility
Several operational processes were negatively affected.
Production Monitoring
Supervisors lacked real-time dashboards to observe machine states across lines.
Downtime Documentation
Operators logged stoppages manually, resulting in:
incomplete records
inaccurate downtime categorization
lost operational insights
Maintenance Dispatch
Maintenance teams often learned about issues after significant delays.
Continuous Improvement
Lean improvement initiatives relied on shift-level summaries rather than real-time operational data.
The Solution
The manufacturer implemented Session Pilot’s AI-driven Operational Quality Management platform, integrating machine data streams into a unified production monitoring environment.
Session Pilot connected to PLC signals and machine state outputs to collect:
machine running status
idle conditions
stoppage events
operator inputs
production counts
The system introduced several capabilities.
Real-Time OEE Monitoring
Machine performance across all six lines could now be viewed in real time.
Automatic Downtime Detection
Machine stoppages were automatically recorded without relying on manual reporting.
AI Downtime Categorization
Session Pilot used event patterns and operator inputs to classify downtime causes.
Production Dashboard
Supervisors accessed a live operational dashboard showing:
machine status
downtime alerts
performance metrics
OEE indicators
Incident Alerts
Supervisors and maintenance teams received alerts when machines stopped unexpectedly.
Results After 3 Months
Following deployment, operational KPIs were tracked continuously.
Downtime Reduction
Average downtime per line decreased from:
2.1 hours/day → 1.65 hours/day
Total facility downtime:
Before:
12.6 hours/day
After:
9.9 hours/day
Monthly downtime:
Before:
378 hours/month
After:
297 hours/month
Downtime reduction:
21%
This translated to 81 additional machine-hours recovered per month.
OEE Improvement
Availability improved due to faster response and better monitoring.
Metric | Before | After |
Availability | 78% | 87% |
Performance | 86% | 87% |
Quality | 94% | 95% |
New OEE:
0.87 × 0.87 × 0.95 ≈ 74%
Total OEE improvement:
+11 percentage points
Incident Response Time
Average response time dropped significantly.
Before:
25 minutes
After:
9 minutes
This improvement alone reduced the average downtime duration per event by nearly 40%.
Downtime Reporting Efficiency
Before Session Pilot:
Production supervisors spent 3–4 hours per week compiling downtime reports from spreadsheets.
After deployment:
Downtime reports were generated automatically in real time.
Reporting effort reduced by:
70%
Data Accuracy Improvements
Downtime events captured automatically increased data accuracy.
Average recorded downtime events per week:
Before:
~38 recorded events
After:
~71 recorded events
This revealed many previously undocumented micro-stoppages.
Financial Impact
Recovered production capacity:
81 machine-hours/month
If the average production value per machine hour is:
€220/hour
Recovered production value:
81 × 220 = €17,820/month
Annualized production recovery:
≈ €213,000 per year
Operational Impact
Session Pilot enabled the manufacturer to transition from reactive troubleshooting to proactive operational management.
Plant managers now use the system for:
real-time production supervision
maintenance prioritization
downtime root cause analysis
continuous improvement initiatives
Production teams gained full visibility across all six lines, allowing faster intervention and improved operational efficiency.
If you'd like, I can next write Case Study 2 with the same level of numerical depth, including:
defect rate baselines
scrap rates
batch sizes
operator reporting frequency
quality escape metrics
That one becomes very powerful for selling Session Pilot as an AI QMS platform.