Seebo Solution Demo

Predict & prevent losses in production quality and waste

The problem

Charlie, a process engineer at a global chocolate bar manufacturer, was tasked with reducing quality and yield losses in the company’s wafer production line.

Charlie attempts to analyze the time series data from the company’s data historian, together with the quality results of the end product from the ERP system.

But lacking data science expertise, is unable to extract meaningful insights into the root causes of quality issues, such as broken wafers, overweight, and packaging faults. Charlie is also unable to predict when such issues will occur in the future to prevent them from happening.

The solution

In order to mitigate quality losses, Charlie and his production team must answer 3 questions:

  • WHAT are the most painful losses? Using digital twin analytics
  • WHY are these losses happening? Using automated root cause analysis
  • WHEN will these losses happen next? With process-based predictive analytics

Charlie turned to the Seebo Predictive Quality solution for the job.