Precision Manufacturing Inc. Case Study

Company Background

Precision Manufacturing Inc. is a tier-2 aerospace component manufacturer based in Grand Rapids, Michigan. They specialize in high-precision aluminum and titanium components for commercial aircraft. Their facility operates 12 CNC machines including:

• 5 Haas VF-4 Vertical Machining Centers (2015-2018)
• 3 DMG Mori NLX 2500 CNC Lathes (2017-2019)
• 2 Mazak Integrex i-200S Multi-Tasking Machines (2020)
• 2 Okuma GENOS M560-V Vertical Machining Centers (2016)

Challenges

Prior to implementing CNC Insight, Precision Manufacturing faced several challenges:

• Unplanned Downtime: Averaging 127 hours per month across all machines, primarily due to tool breakage, spindle issues, and coolant system failures.
• Inconsistent OEE: Overall Equipment Effectiveness averaged 68%, below industry benchmark of 75-85% for aerospace manufacturing.
• Manual Data Collection: Operators manually recorded cycle times, part counts, and downtime reasons, leading to inconsistent and delayed reporting.
• Reactive Maintenance: Maintenance was primarily performed after failures occurred, leading to extended downtime and emergency repair costs.

Precision Manufacturing shop floor before CNC Insight implementation

Implementation Details

The CNC Insight implementation at Precision Manufacturing included:

Phase 1: Hardware Installation (2 weeks)

• Edge Gateway: Installation of two redundant industrial PCs running CNC Insight Edge software.
• Machine Connectivity:
  • Direct MTConnect connection for the 5 Haas machines
  • MTConnect adapters installed on the DMG Mori and Mazak machines
  • OPC UA connection for the Okuma machines
• Additional Sensors:
  • Vibration sensors on all spindle housings
  • Current sensors on main drives
  • Temperature sensors on critical components
  • Power monitoring on 4 machines (as a pilot)

Phase 2: Software Configuration (1 week)

• Data Mapping: Configuration of data points from each machine controller and sensor
• Dashboard Setup: Creation of role-specific dashboards for operators, maintenance, and management
• Alert Configuration: Setup of condition-based alerts for various machine parameters
• ERP Integration: Connection to their existing JobBOSS ERP system for production scheduling data

Phase 3: Training and Calibration (2 weeks)

• Operator Training: 4 hours of training for all machine operators on dashboard usage and data interpretation
• Maintenance Training: 8 hours for maintenance team on alert response and predictive maintenance features
• Management Training: 4 hours for production managers on reporting and analytics features
• System Calibration: 2 weeks of baseline data collection and alert threshold adjustment

Total Implementation Cost

Results After 12 Months

Downtime Reduction

Unplanned downtime was reduced from 127 hours per month to 105 hours per month, a 17.3% reduction. This was achieved through:

• Early Detection of Spindle Issues: Vibration analysis identified bearing wear on two machines before catastrophic failure, allowing for scheduled replacement during off-hours.
• Coolant System Monitoring: Pressure and flow sensors detected coolant issues before they caused tool breakage or part quality problems.
• Tool Life Management: More accurate tracking of tool usage allowed for proactive replacement before breakage occurred.

OEE Improvement

Overall Equipment Effectiveness increased from 68% to 74.5%, driven by:

• Reduced Downtime: As detailed above
• Improved Performance Rate: Real-time monitoring identified and eliminated micro-stoppages and reduced cycle time variations
• Higher Quality Rate: Early detection of process variations reduced scrap rate from 3.2% to 2.1%

Energy Consumption

The four machines with power monitoring showed an 8.3% reduction in energy consumption through:

• Idle Time Reduction: Better scheduling and faster setup times reduced machine idle time
• Optimized Auxiliary Systems: Identified unnecessary coolant pump operation during setup and programming
• Maintenance Improvements: Properly maintained machines operated more efficiently

ROI Analysis

The total first-year investment of $105,000 yielded the following financial benefits:

• Reduced Downtime Costs: 22 hours/month × 12 months × $550/hour = $145,200
• Scrap Reduction: 1.1% reduction on $5.2M annual parts production = $57,200
• Energy Savings: 8.3% reduction on $102,000 annual energy costs = $8,466
• Maintenance Cost Reduction: Prevented two major spindle failures = $16,600
• Labor Efficiency: Reduced manual data collection time = $60,000

Total Annual Benefit: $287,466

Return on Investment: 174% in first year ($287,466 benefit - $105,000 cost = $182,466 net benefit)

Payback Period: 4.4 months

Challenges and Limitations

While the implementation was largely successful, Precision Manufacturing did encounter some challenges:

• Legacy Machine Integration: The older Haas machines required additional work to extract comprehensive data, and some parameters remained unavailable.
• Initial Alert Tuning: The first month saw numerous false positive alerts that required adjustment of thresholds based on normal operating conditions.
• Operator Adoption: Some resistance from operators concerned about increased monitoring was addressed through training that emphasized process improvement rather than performance monitoring.
• Data Overload: Initial dashboard designs provided too much information, requiring simplification to focus on actionable metrics.

Customer Testimonial

"What impressed us most about CNC Insight wasn't just the technology, but their honesty about what the system could and couldn't do. They were upfront about limitations with our older machines and worked with us to find solutions. The ROI has exceeded our expectations, particularly in reducing unplanned downtime and improving our maintenance operations. The system has paid for itself faster than we anticipated."

Future Plans

Based on the success of the initial implementation, Precision Manufacturing is planning to:

• Expand power monitoring to all 12 machines
• Integrate quality measurement data from their CMM into the CNC Insight platform
• Implement the advanced scheduling module to further optimize machine utilization
• Explore predictive tool life management capabilities for high-value cutting tools