What you need to know about Manufacturing Analytics

In the past, if you wanted to know how a machine was performing on a shop floor, you would have had to put pencil to paper and note the output, the error rate, the quality, all manually. You would have to look at the quality of the inputs and the available supply of the raw materials. You would have had to look at the output over time to see if there was a consistency to quality, or errors, that needed to be investigated.

In other words, you were looking at potentially weeks of manual effort to figure out why and how a manufacturing process was going awry. That, in and of itself, isn’t a good use of anyone’s time and is a pretty inefficient way to become more profitable.

Then came Industrial IoT and the ability to monitor machines directly, providing quantities of data from the machine, as well as the operator, that can be analyzed in order to improve both production and quality.

Analytics are essentially the collection and manipulation of large quantities of data to reveal insights. Data about the performance of machines and people, going through the process of receiving an order through delivery of that order, is collected and reformatted as easy to understand metrics, to reveal where there are issues with performance or output quality.

This goes beyond the actual collection of the data to include the formulation of insights that can be used at every level of the organization. The advancement of tools and software in this area means that the process of collecting data is no longer manual and the analysis is centrally available, in real time, for everyone from the shop floor manager to the CEO to review and act on.

There are many advantages to leveraging real time business intelligence:

“Increased revenue (33.1%), increased customer satisfaction (22.1%) and increased product quality (11%) are the top three benefits of Industrial Analytics.” (Source)

Production and quality are interrelated.