Line Balancing & Optimization

Using machine learning for Root Cause Analysis (RCA) to identify important ‘features’ that affect production performance
Advanced Analytics
Manufacturing

It is a challenging exercise to improve a manufacturing line efficiency due the complexity of machine characteristics, machine interactions, and the buffer & speed strategies.

Aspiration

Being able to predict and simulate the production line performance based on the complex line or system to bring improvement on manufacturing operation efficiency

Approach

We acquire, clean, transform, and integrate all data generated by manufacturing line like MES data (machine up and downtime, MTBF and MTTR statistics, machine states, breakdown types, machine speed), line configuration data, buffer size, operation performance indicator, and average production throughput. Then we use this data to create a “Discrete Event Simulation” model to mimic the actual running processes and their interactions and use “Optimization Algorithm” to find the best line parameters that gives the highest overall efficiency.

Outcome
  • Increase line performance with minimum production loss
  • Provide tool to perform what if analysis on a complex line/system
  • Assess buffer sensitivity with respect to line performance
  • Simulate the impact of short vs. long downtime reduction
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