Nearly all applications related to sensor and machine data aim to improve operational efficiency. For example in manufacturing plants, understanding what contributes to unscheduled downtime, and thus productivity, is a prevalent use case. Factors impacting quality and reducing scrap product is another. Our “Smart Factory” projects at First Analytics are no different in their focus.
A good, quick overview of these applications, including an explanation of OEE (overall equipment effectiveness) can be found in this blog post by David Frede of SAS.
David notes that while plants care about productivity and quality, safety is always number one. So, why not use that same data to drive safety initiatives?
In a prior blog post we introduce the “4 Ps of Safety.” Under one of those Ps (“places”) falls equipment. Data from machines can and should contribute to understanding the risk factors in the environments in which your employees operate (and sometimes where you interact with the public).
Machine data has been around for a long time. Only in recent years, with advances in joining that data with predictive analytics, has the data truly been leveraged for impact. To follow on with those recent success, we should not overlook the number one opportunity: safety.