Plantnode exposes Real Time Performance Monitoring's
critical success factor: Machine Truth.

A Simple Calculation for OEE and the influence of Machine Truth

Calculating OEE is very simple.

If you know the maximum number of pieces you can produce in a given time period and the total number of good pieces actually produced you have all you need to calculate your OEE. Divide the actual figure by the maximum figure, multiply the result by 100 to express as a percentage and that is your OEE.  Simple!

While this figure is good to know it tells you nothing about where to start looking to make improvements. That is why OEE is usually expressed as a more complex equation:

                Availability% X Performance % X Quality% = OEE

The answer is the same but now there is some breakdown as to what is causing your OEE to be where it is. The challenge is, how accurate are your percentages?

How data accuracy influences OEE

Hopefully calculating the Quality% is not a big problem, Availability and Performance are a bit more difficult. Both of these hinge on recording down time accurately. And this is where it gets tricky, particularly if you rely on manually collected data.

Here is an example from a company implementing Plantnode to collect data automatically from their machines.  They continued with their manual data collection while Plantnode bedded in.

One machine was causing a problem, with an OEE of only 46% based on this manually collected data, which indicated that the machine’s availability was 76.67 %:

With quality known to be running at 98%, the problem had to be the average speed of the machine (performance), which had to be 61.2% to give the OEE of 46%. With the machine expected to average a minimum of 85% of the maximum speed all efforts were focused on increasing it.

At the same time, Plantnode was automatically capturing data from the machine, recording precisley when it was running and when it was down, using the time taken for set up recorded as such by the operator scanning in a status barcode.

The results from Plantnode were quite dramatically different from the manually collected data. We shouldn't be surprised. In fact, this is very typical of what we find when comparing manual data and data recorded direct from the machine. The accuracy of manual data collection systems is influenced by procedure, interruptions, current panics and all kinds of factors, which leads to unreliability.

Three things in particular stand out in this case.

• There are lots of very short periods of downtime, individually of little significance and hard to record manually, but they all add up
• Set up did not take the amount of time allowed – if there is an allowance (of an hour, say) for set up, that is what usually goes on the sheet of manually collected data.
• The shift finished early.

The end result is that the machine was revealed to be only producing for 51.5% of the available time and performance (speed) was 91.1%, considered very acceptable.

So the problem was downtime, not speed, and that is where management needed to concentrate. Can you imagine the rows every week when pressure was applied to individuals to increase the speed of the machine and all the time it was running at 91.1%, well within the acceptable range?

This is what we call Machine Truth.

Machine Truth

The beauty of Machine Truth is that it's dead accurate, and no one can argue with it. Once you have agreed the rate that constitutes running, any time below that rate is downtime, and the rest of your OEE calculation flows from there. You can then see where your real opportunities to improve lie and get to work on them.

Plantnode enables you to take this analysis to a more granular level with reasons being recorded against each instance of downtime or slow running. From there it will produce Pareto charts highlighting the most common reasons for lost time and under performance, and by knowing your problems you are already well on your way to a solution.

OEE and Machine Truth Information

If you'd like to know more, please check out these case studies, call us on +44(0)870 410 4149, or use this link to contact us.