Three Ways IoT Can Improve Your OEE
By: Opex Analytics
Tags: Machine Learning, Data Science, IoT, Analytics

Three Ways IoT Can Improve Your OEE

by Michael Watson

In manufacturing, the term IoT (Internet of Things) is gaining a lot of momentum. Production machine sensors, which gather and collect data, are leading a new wave of productivity improvement.

One obvious benefit is better visibility of the plant floor and possibly better communication between machines. However, when you combine sensor data with machine learning (including Artificial Intelligence) algorithms, even more possibilities open.

The main uses of IoT analytics (sensor analytics) on production machines are:

Improving Overall Equipment Effectiveness (OEE) — The measure of how much time your machine is actually making products. Improving Preventive Maintenance program, which can improve OEE, but also can lower overall equipment repair and replacement costs. Improving quality of the product coming off the line.

Improving OEE

The first item on this list is often overlooked when discussing sensor analytics because most tend to focus their efforts on using sensor data to predict various production machine failures.

However, we’ve found that companies can make major improvements in OEE by focusing mainly on small adjustments happening on the line. For example: the hand-overs from one shift to the next and the change-overs from one product to the next.

When we worked on change-over reduction programs, we knew that SMED* techniques measured time from the last part of the previous run to the first good part of the next run.

But we also found a lot of wasted time, both in waiting for the last part to clear out as well as wasted time as the machine gets back up to full speed on the next part. We discovered we could achieve additional OEE improvements by focusing on this part of the change-over as well.

There are similar problems surrounding breaks and shift changes, as well as small downtime adjustments during a production run. We’ve found there are plenty of OEE improvements available in these areas. To find these problems, we use sensors to time-stamp when each job was finished as well as the various events and faults coming from the machines. We can then analyze this data with simple, machine-learning techniques, which enable us to find the patterns that lead to a loss of time. Think of all of this as using IoT to go after the easy things.

Improving Preventive Maintenance program

While improving OEE focused on minor failures, we also use IoT to help predict major machine and equipment failures on a line.

The objective is to predict failures before they happen, avoid costly repairs, and set up better preventative maintenance programs. And, depending on the frequency of major equipment failure, this also improves OEE.

There is a lot we can do in this area. We can analyze the diagnostics to understand what has gone wrong and the damage incurred. We can also use the prognostic to predict what will happen as well as the expected time until failure.

Improving the Quality of the Product

Last on the list: using sensor analytics to monitor and predict the quality of the product. The machine may be running fine, but if the product is not meeting specs, you really shouldn’t be counting this toward a good OEE measure.

There is a wide range of things we can do here. We can measure temperature, vibration, and humidity, we can send alters to inspect, or we can use artificial intelligence vision systems to inspect each product coming off the end of the line.

There are other ways to think about IoT on manufacturing lines and your case will be tailored to your specific environment. This post was meant as a reminder that if OEE is your overall goal, there might be different ways to think about IoT.

(*SMED– Single Minute Exchange of Dies. This is the collection of change-over reduction techniques popularized by Toyota.)

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