Customer Award
We are honored to receive this token of appreciation from Ken Innami, Director–Global Key Accounts, and Jim Mangan, Sr. Manager–Global Key Accounts, of Mitsubishi Electric. It has been a pleasure supporting their team, and we look forward to working together in 2019 on additional collaborations.
Overall Equipment Effectiveness
Client: Automotive Customer #1
- Implement customer’s Manufacturing Efficiency System (MES) System
- Implement the system at four (4) customer plants including engine, castings and transmission plant
- Software layer collected data from over 150+ nodes representing in excess of 36 manufacturing lines and 1,200 pieces of major production equipment
- Representative data collected by the system includes:
- Manufacturing Process values
- Plant machine settings
- Operator observations
- Test Instrumentation data
- Raw Materials input data
- Environmental condition parameters and data
Results
The customer’s Manufacturing Efficiency System (MES) has had significant positive impact within the plants implemented with the solution. This includes:
- Increase in OEE by 10%
- Improvement of line throughput by an average 5%
- Scrap rate reduction by over 15%
- Average energy consumption saving of 5%
- Improvement in maintenance schedule efficiency by 20%
MES implementation at the four customer plants has yielded in excess of $10M annual savings. The system is now projected to be implemented across all plants within our customer’s engine, castings, transmission and assembly divisions.
Client: Healthcare Customer
Data Correlation:
- Downtime by Micro-stops
- Cycle Time by Product Type and Mold Number
- Product Quality comparison between lines
- Predictive Maintenance by Reject Rate
- Overall Performance by Shift and Time of Day
Output: Large Wall Panel, Daily Report, PC and Mobile Dashboard, Dedicated Machine Terminal
Example ROI: Servo Motor maintenance requirements can be predicted by comparing scrap rates across identical equipment. Causes of micro-stops and inconsistencies can be identified by grouping event types.
Predictive Maintenance
Client: Automobile Customer #2
Data Correlation:
- Product Quality Measurements since Tooling Change
- Machine Cycle Time since Maintenance Action
- Maintenance Frequency by Product Class
- Process Data Pattern Recognition leading to Maintenance Action
Output: Large Wall Panel, Daily Report, PC and Mobile Dashboard
Example ROI: Using 1 of 3 algorithms to monitor trends in manufacturing process and quality data, and then correlating those trends with maintenance actions, we are able to predict upcoming maintenance requirements in real time.