These days, there\u2019s no shortage of data being generated by the machines that populate electronics manufacturing lines. In fact, there is so much data available that making use of it becomes the primary challenge. In other words, how do we transform \u201cbig data\u201d into \u201csmart data\u201d? That is, how can we process the data in order to gain insights on how to make our operations more efficient and profitable?<\/p>\n\n\n\n
Today, most of the data generated is sorted, filtered, and summarized in a spreadsheet \u2013 ironically, in order to minimize <\/em>the amount of information to be processed, and to use the dumbed-down data to help make the most basic decisions.<\/p>\n\n\n\n Are there analytics tools that can help use generate more value from this abundance of data? There are a number of requirements that must be met in order to turn big data into smart data \u2013 data that provides insights (or foresights), is easily understood, and is immediately actionable.<\/p>\n\n\n\n In the eleventh session<\/a>\u00a0of our series of 12 mini-webinars on\u00a0Implementing \u201cDigital Twin\u201d Best Practices From Design Through Manufacturing<\/em><\/a>, we discuss how to use analytics in order to unlock the profit potential of the huge quantities of data being generated by electronics manufacturing machinery. By using data from multiple resources, manufacturers can derive highly-relevant key performance indicators that can be used to trigger and guide efficiency improvement efforts throughout the organization.<\/p>\n\n\n\n Main takeaways from this session:<\/p>\n\n\n\n