Overall Equipment Effectiveness or OEE is a method of looking at a manufacturing process to determine how well it is operating. It combines three metrics to provide a single measure that encompasses the utilisation of an asset, if it is being run at its optimum speed, and what proportion of scrap to good products are being produced.

Unless you have a site that is already extremely efficient, in virtually all cases that I have come across it is possible to get a payback for a system in just a few weeks.  After that you are saving money and improving your bottom line.

Generally, there are also benefits realised that were not apparent initially.  For example, with one timber mill, it was presumed that the operator on one of their stackers was stood waiting for about 1½ hours in an 8-hour shift.  After an automatic OEE system was installed, it became clear that it was actually 4½ hours.  As a result, their process was adjusted to build up a buffer and the operator redeployed on other tasks during their ‘newly available’ time.

Implementing an OEE system will not inherently make you more efficient or save you money.  The only way to make it work is if you actively use the data it generates.

Ideally more than one member of staff should champion the process.  Otherwise, if that person is busy or if they leave, you are left without appropriate knowledge, and the system is unlikely to be successful. You also need buy-in from the top management all the way down to the shop floor.  Fundamentally you need to be committed to it, or you will just be wasting your time and money.

The availability metric immediately brings up some interesting differences in possible approach.  From the production plan point of view, operators are expected to be running their assets according to a defined shift pattern with allowances for breaks, clean up times etc.  This gives an amount of available time for running on each asset.  It can, however, make it complicated when, for example, operators are off sick, the production plan needs to change on the fly, or an impromptu meeting is held.  Ultimately this leads to an increased administration requirement which, in my experience can result in the system falling out of favour as the overhead is too onerous.

I think in most cases it is better to handle the planned not to run periods by using simple concepts such as identifying when an operator is not logged on to an asset.  Then there is no additional administration and logging on can be enforced through the use of machine interlocks.

Takt or cycle times for machines can be well defined but that is only the start of the story.  Production planning systems will calculate how many products of a particular type can be manufactured in a given amount of time.  They take into account changeover and clean down times and include timings for bringing raw materials to an asset and taking finished goods away.  This gives most sites their performance targets.  However, I have seen many cases where the data in the ERP/MES is based on historic hearsay and is not particularly accurate.

A little while ago, I was stood with a production manager at a site looking at a machine with a speed measurement above it of about 200 m/hr.  I questioned how they knew if it was running at the correct speed and what the target speed was.  The discussion progressed to the fact that when the machine was installed the manufacturer said it could run at 800 m/hr, as the machine was now quite old they had recently said that they thought that it would probably be capable of 400 m/hr.  “Ok” I said, “but why is it running at 200?”.  “That’s a good question” was the response…

The target speed for each product should be reviewed and if there is uncertainty about them then data needs to be collected initially to allow an accurate figure to be determined and then fixed.

OEE accounts for the quality metric with exactly the same weight as availability and performance, however, in most instances the early wins at a site do not initially come from targeting quality issues.  That is not to say that it should be ignored though.  If you speed up production to improve performance, you may inadvertently reduce the quality.

Ideally if the quality metric is not going to be targeted initially, I would suggest using a nominal value, for example, defining a percentage of all products manufactured to be scrap.

World class performance is often quoted at 85% but the reality is that it is wholly dependent on the manufacturing process for a particular type of product.  In some cases, a 50% OEE may be exceptional.

Ultimately, OEE is a benchmark that should be used to determine whether a site is making improvements or not.  It doesn’t matter if the actual value is 85% or 25%.

Manual, paper based systems allow users to become familiar with the concepts of OEE and are, therefore, a good starting point.  However, they have two key issues:

An automatic system will accurately timestamp each stoppage event and can be used to force reasons to be entered at the time they occur.  This leads to better data that can be automatically analysed.

Although tracking OEE for a manual assembly process is inherently dependent on an operator entering accurate information, there is no reason why it cannot be monitored so that data is collated electronically and automatically processed, meaning that there is only minimal administration required.

If you also look at minimising operator interactions using barcode scanners, RFID readers, and the integration of the OEE system with production planning to get works orders / jobs automatically, then there are some significant additional benefits to be realised.

In order to improve, one of the key areas to address is response times.  This may be simply highlighting to management when an asset has been stopped for an extended period of time or improving the notification / call out mechanisms available for maintenance staff.

For longer stoppages there is often a disagreement between production and maintenance about how long a breakdown took to repair.  Recording call out and response times allows these periods to be analysed in more detail to determine, for example, if there are sufficient maintenance staff on a night shift.

In addition, allowing operators to request raw materials or their team leader, direct from their asset can also result in shorter stoppages.

In order to make improvements you will need to go from the 3 key metrics into the data that they are made up of.  For example:

OEE is not a panacea but it is a great tool for identifying where issues are and providing a means of seeing that improvements have been made.

Implementing an OEE system that can be easily expanded over time to cover more assets and include additional functional areas is a way of instilling continuous improvement throughout the workforce.

After all, your competitors are improving and unless you continue to become more efficient, you will get left behind.