Complacency? Surely it’s the yardstick against which all progress is measured? In theory, yes, but experience shows that there are some very simple traps that many organizations fall into when measuring efficiency which can have a profound negative effect on improvement in the organization. Let’s look at the 5 most common ones.

Let’s take an example from the banking sector. A bank administrator needs to take a document, use the data to effect a transaction and report the transaction complete. In one case she notices a mistake on the form – she makes a phone call, checks a fax showing the correct details and rings the internal customer to confirm the correct transaction. Our administrator then fills in her efficiency log sheet. Normally the transaction takes 30 mins, but in the case of the one with missing data it took 90 mins. But that’s not a problem, as she can tick the boxes for ‘missing data’, ‘fax’ and ‘confirmation phone call’ on her efficiency log sheet. Her efficiency will hold up, because those extra activities have a ‘standard time’ of 60 minutes allowed for them.

But we now have a transaction that took 3 times longer than it should because of an omission on the incoming data – and it will look like our administrator is working very efficiently because there’s a time allowance for dealing with mistakes. So where does our lost 60 minutes show up? Nowhere at the moment, we have just done the office equivalent of digging a hole and filled it in, before giving ourselves credit for both parts of the operation.

2. Ignoring the elephant in the room

Many losses and problems are no longer seen as problems and are excluded from the measures altogether. Classic examples of these include 3 hours of training a week that was routinely taken off the working hours of a call centre. A little research showed that the average training per employee had been 2.5 hours, for the whole of the past 6 months. Other opportunities that may be hidden include 30-45 minutes ‘settling-in’ time that was routinely knocked off the available working hours in a branch environment, during which no work was expected to be done.

3. Just not measuring the right stuff

Much of the value of OEE comes from the valuable information it gives on where, and how often, losses are happening, so we can do something about the biggest ones. It’s perfectly possible to come up with a headline OEE figure without recording detailed loss information, but there are two problems with this:

1. We can’t then do an error check and come up with a measure of how accurate our OEE is.
2. We have no idea where to start working to improve our OEE.

Blindingly obvious though it may seem, not recording waste, lost time or rework can be the major failing in many efficiency measurement systems.

4. Using the wrong bottleneck speed

Some of the toughest mistakes to spot are incorrect ‘standard times’ for given operations. The way this situation arises is that someone incorrectly sets up a standard time. For example, let’s say we are allowed 20 minutes for a phone call – but the truth is the call really only take 10 minutes. Put simply, if we spent the first half of the day making calls, and stopped at lunch time, we could still report a 100% efficiency.

This is one of the most common problems, particularly in complex administrative environments, and can quickly lead to a false sense of achievement.

5. Confusing the target with 100%

Many organizations have a tough time telling their teams the hard truth: that they are not perfect. Some cultures seem to feel that admitting a 50% efficiency is like admitting defeat, so they succumb to the natural human urge to cheat. Whilst this makes everyone feel good for a while, it can lead to dangerous level of complacency, with teams declaring themselves ‘world-class’ when exposure to other operations might make them a little more humble.

These are the signs to look for:

• Standards are set so that everyone can easily do them.
• There are clear signs of waste and inefficiency but you regularly hit 100%.
• There are big variations in workload but it always gets done.

And these are just some of the pitfalls. No one wants to be negative about the hard work that goes into generating improvement and the figures that demonstrate it, but there’s a serious side to this. If people genuinely believe over-optimistic efficiency figures where’s the motivation to improve? There’s a big difference in belief that comes from thinking you are at the top of your game (say in the 90% region) and discovering that you are really in the low 40% region.

Next time you see your own organization's efficiency figures, it may be worth asking a few tough questions about what they are based on.

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