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There are four ways data management and productivity solutions can help utilize the data derived from existing equipment that can be connected to the IIoT.
Without data collection technology in place, it is safe to say that manufacturers are lucky to have a month’s worth of data collected. With advancements in the way the IIoT can be used, it is possible to dig down into single minute and single second intervals to help get to the root causes of issues.
There is no one-size-fits-all scenario for data collection and management. In fact, often times there are issues with first identifying which data should be captured, and then determining what to do with it. Many plants exist in a current state where obtaining and understanding data it is complicated; the data itself is subjective based on the person collecting it and the uniqueness of each plant.
With adaptation of a data management technology, plant managers can distill the data, identify key problems and root causes. Using statistics and math to evaluate trend lines and control charts, best practice and process can be taught and standardized across the plant.
Without the technology made possible by the IIoT, there is variation in data collection that is indistinguishable from variation in process. If there is a variation in measurement, the data becomes useless. With automated data collection, variance can be more fully understood and managed. Adopters of data management tools are assured that the captured variances in a process are actually variances, and not something else.
When partnered with data visualization and analytics software, advances in the IIoT change what is possible for Lean manufacturing improvement initiatives. Manufacturers can capture more data than ever before, and fast-forward through the challenges of interpreting that information and identifying problems and root causes on the production line.
Data visualization and analytics software can help accelerate the collection, consistency and accuracy of data. The depth and granularity of automatically collected data helps adopters to more quickly identify areas of concern, reduce waste, prioritize improvement activities and evaluate improvement measure effectiveness.
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