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Bottle filling lines are complex systems with various specialized machines like fillers, cappers, labelers, conveyors, washers, inspectors, and packaging machines. Each machine is essential for safely and efficiently packaging liquids into bottles. The filler, which accurately fills bottles with liquid, is especially important. Problems in other parts of the production line often result in bottle jams or shortages at the filler. Our customer faced frequent issues with the filler and couldn't identify the causes of these disruptions.
Our client is an international manufacturer of filling and packaging systems for the beverage, food and non-food industries. The company has approximately 5000 employees and annual sales of more than 1.2 billion euros.
Bottle filling lines are intricate systems consisting of a variety of specialized machines, including fillers, cappers, labelers, conveyors, bottle washers, inspectors, and packaging machines. Each machine is critical to the seamless and safe packaging of liquids into bottles for consumer use.
At the heart of these operations is the filler, which plays a pivotal role in accurately filling bottles with liquid. And at this lead machine, problems elsewhere in the production line typically manifest themselves as bottle jams or shortages. Our customer was experiencing frequent problems with the lead machine, and was struggling with unidentified causes for these disruptions.
The AI engine powering Process Booster, aivis®, was fed with the raw historical process data of a whole bottle-filling plant, provided as MES database dump. The set main goal was to investigate root causes for jams and shortages at the lead machine (filler) of one specific bottle filling line. aivis® then performed a Root Cause Analysis (RCA) on the raw data of the line. The provided insights identified the top malfunctioning machines with the top malfunction causes for the lead machine including the influencing variables on the malfunctions.
Based on the insights provided by aivis® AI and the prediction models it created, the ability to proactively identify and resolve jams and shortages at the filler before they occur, was significantly enhanced through timely warnings and countermeasures.
Based on the insights provided by aivis® AI and the prediction models it created, the ability to proactively identify and resolve jams and shortages at the filler before they occur, was significantly enhanced through timely warnings and countermeasures.
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