How to turn your big data into valuable “smart data”
Blog #11 in a 12-part series covering the Digital Twin Best Practices in Electronics Manufacturing mini-webinar series by Jay Gorajia
These days, there’s 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 “big data” into “smart data”? That is, how can we process the data in order to gain insights on how to make our operations more efficient and profitable?
Today, most of the data generated is sorted, filtered, and summarized in a spreadsheet – ironically, in order to minimize the amount of information to be processed, and to use the dumbed-down data to help make the most basic decisions.
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 – data that provides insights (or foresights), is easily understood, and is immediately actionable.
- Manufacturing analytics tools should be able to aggregate, transform, and unify data from multiple sources and in a variety of formats.
- The tools should help generate critical key performance indicators (KPIs) based on information from the machines, ERP software, and manufacturing execution systems (MESs).
- The generated insights should be easily visualized, customized, and shared.
In the eleventh session of our series of 12 mini-webinars on Implementing “Digital Twin” Best Practices From Design Through Manufacturing, 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.
Main takeaways from this session:
- The need to process data from a variety of sources in order to obtain insightful, actionable information that can be leveraged to improve operational efficiency.
- How to use analytics in order to build valuable knowledge regarding equipment, quality, and supply chain performance, while maintaining complete product, process, and materials traceability.
- How Opcenter Execution Intelligence provides the tools required to turn big data into “smart data”.
