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Customer Award

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Data Acuity Plaquepeople recieving 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


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.