Why do we measure just labour productivity when it’s the productivity of all costly input resources used together that matters most:
- Because we always have done – it was relatively easy to count in the old manufacturing days?
- Because if you produce more per worker, there’s more to share out to each worker – which has to be a good result
- Because we still can?
- Because we can’t measure the productivity of capital and other inputs in an equivalent, meaningful and useful way?
So we can’t assess the trade-offs between employing more and/ or better quality employees, say, versus injecting additional capital investment in a new computer system or machine
Instead, we pat ourselves on the back when we automate a process, at great cost, and our labour productivity numbers rise dramatically, even though the labour has probably input no extra time or effort – this is especially so at national level where huge investments are being made in IT hard and software, robotics and artificial intelligence but the focus is only on labour productivity results
The answer is to dig deeper and find a suite of measures, not just one all-embracing figure, which together paints a ‘true and fair’ picture of the productivity level being achieved
In addition: “Why focus on hours or numbers of people input when it’s the results they produce that matter most, regardless of their time inputs?”
Nowadays, there’s far too many people (apparently) working long hours in the office but producing little of value – conversely, there’s many others who do produce output of great value, some needing to work only a few hours per week to do so
Indeed, as work in general moves steadily away from being boring and repetitive to interesting, problem-solving and/ or creative, many organisations need to change how they measure their own productivity levels and review the office time inputs expected of their employees
Way back In the 50s, the Illinois Institute of Technology found that for scientist/ knowledge/ creative/ performing arts workers:
- Output peaked when they put in between 10 and 20 hours per week
- Those who worked 35 hours a week were half as productive as their colleagues who worked about half as much
- Those who worked 60 hours or more were the least productive of all
However, most brainwork first requires lots of slogwork which takes time – time for research, fact finding, data collection and analysis, diagnosis and experimentation – but all such tasks are increasingly being covered by AI
That leaves the need to produce results – a number and quality of plans, diagnoses, decisions and ideas needed – and brainworkers can mull over problems 24/ 7, not just within eight hour slots per day – so why require and pay them for their time inputs alone
Maybe Illinois has it right and the brainwork productivity cliff looms soon after 10-20 hours input per week for most of us given it’s a mental economy now for many of us?
Indeed, the great Maynard Keynes no less foresaw a 15 hour week by 2030 i.e. much less than half the current average in the UK
The route to longer term human well-being and productivity success is surely to work less hours, not more
That has to be the right course to steer from now on for most if not all organisations