One of my favourite scenes in “This is Spinal Tap” – an iconic spoof “rockumentary” – is when my namesake, lead guitarist Nigel Tuffnell, struggles to explain the competitive edge provided by an amplifier whose volume goes to 11 (and not just 10 like every other amp).

Many leaders (and team members) struggle with the idea of team and individual performance (eg productivity) exceeding 100%. Targeting performance above these levels will be met with a barrage of cynicism and comments such as “How hard are you squeezing the lemon?“, “You can’t get blood out of a stone.” and “Are you flogging them to death?”

There are two quite separate issues here: one is technical and the other is behavioural. I recognise that readers may be more interested in the people/behavioural aspect and I would cover that issue first, but it does help to have at least a basic understanding of the technical issue. So I’ll cover the technical issue here and the behavioural one in a subsequent post.

The Pointy Headed Stuff
People tend to use the terms ‘productivity’ and ‘efficiency’ interchangeably. While closely connected, they are not the same thing.

In simple terms, productivity is about how much value added output you get per unit of input e.g. customer enquiries resolved per person per day, whereas efficiency focuses on how long it takes to produce those outcomes relative to how long it was expected to take. Efficiency is usually represented as a percentage (actual/ expected x 100%).

I’ll share an anecdote first to highlight why it is important to understand the difference between these two terms.

Several years ago, a few weeks after starting a new job, my boss came out to visit my team. I had two team leaders display their results on charts: Team A’s efficiency was 80% and Team B’s efficiency was 110%. My boss asked, in front of the respective team leaders, if I was going to sack Team A’s leader. My answer was – “Come back in the afternoon and I will have fixed it.”

A few hours later, Team A’s efficiency was 120% and my boss thought that I was the new wunderkind of operations management. If the charts had shown their productivity performance (payments processed per person for Team A and enquiries handled per person for Team B), my boss wouldn’t have been able to make the comment and, even if he had, I wouldn’t have been able to lift performance by 50% in a couple of hours. How did I do it? All will be revealed with a simple example.

Worked Example
If we expect it to take 8 minutes to resolve an enquiry, the productivity of a team member working an 8 hour day should be 60 resolved enquiries per person per day (8 hours x 60 mins / 8 mins per enquiry). If that’s the case, then they are 100% efficient (the actual time worked is 8 hours and the expected time to resolve 60 enquiries is 8 hours).

Setting an expected (standard) time for a task in services is an imprecise science given the inherent variability, so you can easily adjust it and it would be hard to argue that the new time is wrong. The team member is still producing the same amount of output in the same amount of time but adjust the standard time down and the efficiency will go down, adjust the standard time up and efficiency will also rise. How did I save Team A’s leader from being sacked? Simple, I increased the standard time for each task by 50%.

A dubious practice no doubt, but what if we decide that our current process is a little too risky and we need to add some extra control steps? We now expect the new process to take 10 minutes per enquiry. Because the process actually takes longer, it’s reasonable to assume our team member will not be able to resolve as many enquiries in a day – only 48 to be precise. Hence, productivity has fallen – we’re not getting as much bang per buck. But if the team member manages to resolve those enquiries in 10 minutes, they are still 100% efficient.

How can this make sense? Well, the team member is working just as hard, so their efficiency shouldn’t change. The productivity drop is a direct consequence of a decision made by the process owner and not the responsibility of the team member. One would assume, that the relative risk reduction benefit more than offsets the incremental cost of additional team members to do the work.

If the team member is a star performer and works extra hard to resolve an enquiry every nine minutes, their efficiency would be 111%. However their productivity of 53 enquiries per person per day is still less than before the additional control steps were added. So efficiency up, but productivity down! Confusing to say the least, so which should you use?

Which Is Best?
For me, productivity is simpler to understand and use – if each team member can do 60 per day and I have 600 enquiries to resolve, I know I need a team of 10. However, services aren’t that simple and very few service businesses produce just one type of outcome. So, if your team resolves enquiries, opens accounts and processes payments, how do you aggregate their output? You can’t directly, but if you convert everything to time, you now know how many hours it should take to resolve those enquiries, open those accounts and process those payments. You can then add all of the hours together and compare that number with how long it actually took for the whole department or business to deliver this level of output.

So, while I find productivity intuitively simpler, efficiency is a far more practical metric to use.

One word of warning if you use efficiency: unless two teams are doing exactly the same work, in the same environment you cannot benchmark performance across teams – you can only use it to track a specific team’s relative performance over time.

And Finally
In the end, what we are trying to achieve is to lift the performance of our team. Being clear about what we’re measuring and how the calculations work is a good start, but to really “move the needle” on performance, you need to have a far better understanding of the behavioural and human issues at play and the impact the relevant metric has on the people that make up the team.

So why do we mix up efficiency and productivity? If it’s hard to determine the actual standard time, what time should be set? How can a team member running at 110% still be given a stretch target, without the manager being labelled a slave driver? Why could a team member, running at 75%, still be at risk of burn out?

The answers to these questions and more will be shared in part 2 when we tackle more of the behavioural issues surrounding productivity and efficiency.

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