Tire manufacturers can use improvements in acquiring and storing measurements from equipment in the final finish area, as these advances have created huge amounts of data for tire makers to use to monitor production actively for relevant trends that can pinpoint upstream product, machine and process issues.
However, organization of that data is traditionally optimized for efficiency (real time transactions) instead of effectiveness (right time queries). As a result, end users can lose productive hours finding problems instead of fixing them.
This paper discusses how manufacturers can use proven manufacturing intelligence (MI) methodologies and tools to extract relevant information from large, complex sets of tire test data. With visual results, trend alerts and the ability to quickly perform ad hoc analysis, users can more efficiently identify problems without the traditional drain on IT resources.