Productivity – are we measuring it correctly?

We hear a lot about productivity. People publish data about it, make claims about it, try to sell you services to improve it – it’s everywhere.

But a big question that I don’t see being asked enough is: what does ‘productivity’ mean?

In my experience, most people don’t think about it deeply enough and don’t have much of an idea what they are really measuring. It’s just a number, and we can report on it or track its variance. If it’s going up – happy days. If not, then duck in case someone thinks it’s your fault.

The definition of productivity most commonly used and understood is ‘the rate of output per unit of input’. This made a certain sense when our economy largely consisted of ‘direct labour’ – producing volumes of output of similar products, often in factories, mills or other manufacturing-related businesses. By this logic, the productivity equation suggests that an output of 1 for an input of 0.5 represents a high level of productivity while an output of 1 from an input of 1.2 is disastrous.

However, to apply that same thinking to our modern world – which often involves highly technical, creative and one-off outputs or decisions of massively differing impacts and values – is simply illogical: what if the first is the production of a bolt while the second is the creation of the Covid vaccine?

The problem with productivity

Any data or measurement is useless to a manager and leader until:

• They have an accurate understanding of what it means.
• They know why it matters.
• They understand what they should do differently as a consequence.

This above all is my problem with the equation for productivity. Any compound measure of productivity, such as total output, typically fulfils none of those three criteria. It tells us little about our effectiveness, gives us no usable insight into how well we are doing and enables no conclusive decisions, other than to gather more data – and gives birth to further data-gathering, graphs, spreadsheets and reports that provide very little insight and are ineffective as a basis for decisive action. Even comparison with past performance is likely to be misleading, especially after Covid.

Productivity is only a helpful indicator if it is compared to expectation – an understanding of what would be a ‘norm’ and of what ‘optimum’ productivity would look like. Without that, it’s like measuring the weather by the percentage of cloud cover – but without factoring in the air temperature, wind speed and whether or not it is raining.

Therefore, I suggest that we shouldn’t measure productivity in the modern world without factoring in the concept of value and impact, and it’s only useful to do so at all as a comparative indicator rather than as an absolute measure.

Measuring value and impact

The only sensible benchmarks we can compare our productivity with are:

• Meeting stakeholder expectations
• Optimum performance.

Both require us to already have established a clear picture of what ‘good’ looks like both in general terms and in the eyes of the beholder.

To measure value and impact, I would argue first that both must be viewed from a multi-stakeholder perspective – and are often only considered from the perspective of ‘apex stakeholders’ such as investors or customers.