Initial Situation
The manufacturer had implemented multiple digital tools over the years, including machine monitoring software and a standalone Quality Management System (QMS). However, the systems operated independently and were poorly integrated.
Production teams monitored machine performance through machine dashboards, while the quality department maintained defect reports, inspection records, and compliance documentation inside a separate legacy QMS platform.
Because these systems were not connected, operational data had to be manually consolidated to generate reports or conduct root cause investigations.
This created several operational inefficiencies:
• Quality issues could not easily be linked to machine conditions
• Production teams lacked visibility into defect trends
• Root cause investigations required manual data gathering
• Compliance reporting consumed significant engineering time
While the company had a QMS system for compliance purposes, it was not functioning as an operational quality intelligence platform.
Baseline Production Metrics (Before Session Pilot)
Production Output
Average daily production:
950 precision components/day
Operating schedule:
2 shifts per day
5 days per week
Monthly production volume:
950 × 22 ≈ 20,900 units/month
Baseline Quality Metrics
Internal quality reports showed the following performance levels.
KPI | Baseline |
Defect rate | 3.8% |
Scrap rate | 1.4% |
Rework rate | 2.4% |
First Pass Yield | 96.2% |
Monthly Defect Volume
20,900 × 3.8% = 794 defective units/month
Breakdown:
Rework units:
20,900 × 2.4% ≈ 502 units/month
Scrap units:
20,900 × 1.4% ≈ 292 units/month
Baseline Operational Inefficiencies
Manual Reporting
Quality engineers manually compiled production and quality reports each month.
Data sources included:
CNC machine logs
inspection spreadsheets
quality deviation reports
ERP production records
Average reporting workload:
40–45 hours per month
Equivalent to roughly 1 full work week of engineering time.
Root Cause Analysis Delays
Because production and quality systems were disconnected, engineers needed to manually correlate:
machine conditions
production timestamps
inspection results
Average investigation time:
4 days per quality incident
This slowed corrective actions significantly.
Quality Visibility
Plant managers lacked real-time visibility into quality trends.
Quality reports were typically reviewed:
once per week or once per month
This delayed operational decisions.
Key Operational Processes Impacted
The fragmented system environment affected several critical workflows.
Quality Event Tracking
Defects recorded in the QMS could not easily be tied to specific production events.
Compliance Reporting
ISO quality reports required manual consolidation of multiple datasets.
Root Cause Analysis
Engineers lacked unified datasets to analyze defect patterns.
Continuous Improvement Programs
Lean initiatives relied on incomplete operational data.
The Solution
Session Pilot implemented a unified AI-driven Quality Management platform that connected production monitoring with quality intelligence.
The system integrated:
• Machine performance monitoring (OEE)
• Shop-floor defect reporting
• Inspection data
• production batch tracking
This created a single operational platform combining production and quality data.
Production–Quality Data Integration
Session Pilot automatically linked quality events with operational data including:
machine ID
production batch
operator shift
cycle time
machine status
This allowed engineers to analyze how production conditions influenced defect patterns.
Real-Time Operational Dashboards
Managers gained access to real-time dashboards displaying:
• OEE performance
• defect frequency per line
• defect trends per workstation
• production throughput
• inspection pass/fail rates
This replaced static monthly reports with live operational visibility.
Automated Reporting
Session Pilot automatically generated:
monthly quality reports
defect trend analysis
production performance summaries
compliance documentation
Results After First Operational Quarter
Within three months of deployment, the company saw measurable improvements across multiple operational metrics.
Reporting Efficiency
Manual reporting workload reduced dramatically.
Before:
40–45 hours/month
After:
14 hours/month
Reduction:
65% decrease in reporting workload
This freed engineering teams to focus on improvement initiatives instead of administrative tasks.
Production Visibility
Managers gained full operational visibility across:
6 production lines
12 machining centers
4 assembly cells
Real-time dashboards allowed supervisors to detect performance issues immediately.
Root Cause Identification
By linking production and quality datasets, investigation times improved significantly.
Average investigation time:
Before:
4 days
After:
2.2 days
Improvement:
45% faster root cause identification
Defect Reduction
Improved production–quality data correlation allowed engineers to identify several recurring issues.
Defect rate reduced from:
3.8% → 3.0%
Monthly defect volume:
Before:
794 units
After:
20,900 × 3.0% = 627 units
Reduction:
167 fewer defective units per month
Scrap Reduction
Scrap rate improved slightly due to earlier containment of defects.
Before:
1.4%
After:
1.1%
Monthly scrap reduction:
Before:
292 units
After:
20,900 × 1.1% = 230 units
Reduction:
62 units/month
Continuous Improvement Activity
Because operational data was now accessible, improvement initiatives increased.
Number of improvement actions launched per quarter:
Before:
10 initiatives
After:
13 initiatives
Increase:
30% more continuous improvement activities
Financial Impact
Scrap cost per unit:
€55
Monthly scrap savings:
62 × €55 = €3,410/month
Defect reduction savings (rework avoided):
Estimated €20 per unit
167 × €20 = €3,340/month
Total monthly operational improvement:
€6,750/month
Annual operational improvement:
≈ €81,000/year
Operational Impact
Session Pilot transformed the company's disconnected systems into a unified operational intelligence platform for production and quality management.
Plant leadership now uses the platform for:
• real-time operational monitoring
• faster root cause investigations
• automated compliance reporting
• data-driven continuous improvement
The company transitioned from reactive quality management to proactive operational control, significantly improving manufacturing visibility and decision-making.