The Challenge:
During routine operations, a ventilation blower on one of the facility’s blender lines experienced an unanticipated shutdown.
This failure initially went undetected by BAZAN’s existing monitoring systems, as the conditions created by the shutdown fell outside the scope of their predefined alert thresholds.
The Solution:
BAZAN had recently deployed APERIO, which consists of a wide array of data quality engines. Among these is the sophisticated “Out-of-Range Machine Learning (ML) Engine” that was designed to address precisely this type of challenge.
APERIO rapidly identifies anomalies in vast volumes of data, including the condition that caused this specific shutdown.
Benefits:
Enhanced Safety: By detecting the component shutdown rapidly, APERIO averted a number of risks.
Improved Product Integrity: Early detection of the issue prevented contamination, safeguarding the integrity of the production line.
Protocol Overhaul: The incident prompted a comprehensive review of BAZAN’s alert protocols, expanding monitoring coverage to include previously overlooked conditions.