Extrapolation is a dodgy game. That is to take a set of figures from past performance and then projecting them into the future. All too often, even in the world of artificial intelligence, a linear projection is deemed sufficient. Having a mass of collected data and then plotting a line that nicely fits somewhere in the middle. Assuming that there’s a value that is atypical of the accumulated data set.
It’s well accepted that predicting the future is fraught with assumptions. Real life is multidimensional and full of combinations and permutations of possibilities that are off the charts. However, everyday there is enough stability to make planning a useful activity.
There’s the phenomenon of the step change. Overnight a new law may be applicable. In the morning a new system may be switched on. In the evening, a major crash may stop transport links for hours. Events, both predicable and unpredictable throw a spanner in the works.
Trying to predict future workforce needs based on current facts and historic trends is something every major industry must do. I say “must” accepting some struggling and pathological organisations survive on a day-to-day basis.
So, if I start with a 100 people and expect a growth of 5% each year, it’s not unreasonable to think that I might need 105 people next year. It’s surprising how often this basic calculation is presented. Same with the predicable response from a chief executive who would prefer an improvement in productivity. Deliver the same output with the same, or less, people.
Predicting one year ahead is a must. Predicting 10-years ahead is nice. However, in certain industries where, say the products have long lives or the demand for services isn’t going away a long-term projection has meaning.
The search for productivity goes on. Digitisation has made inroads into the paper-based world of a couple of decades ago. I remember when I first held a digital camera in the office. In the mid-1990s it was a whizzy bit of kit to be loaned out on rare occasions.
Artificial intelligence, and all the technology wrapped around it, are reshaping business strategy, risk assessments and workforce planning. Everyone is seeking a competitive advantage.
This morning when I woke up, I was introduced to a new word. It was a BBC radio broadcast called: Rethink: manufacturing or services?[1] It doesn’t exactly roll off the tong. It’s servitization.
The premise is that the line between organisations manufacturing products and those who provide services isn’t a ridged one. Progressively, whatever the product, train, plane or automobile, they use masses of data not only to make but to operate. The data flows rapidly backwards and forwards between maker and operator. And lots of it too.
So, if I had to estimate the number of service engineers that would be needed in the next 10-years, I’d better be aware that their jobs are going to change dramatically. Radically.
However, overall will the numbers of people needed by an industry decrease or not? Or is it just that the nature of work will change for roughly the same overall numbers? That’s not so easy as a straight-line graph. Maybe, as productivity increases so it becomes possible to take on new tasks that previously were not viable. The performance of a product continuously improves.
I’d buy a service and not so much a product. I’ll buy 100,000 miles, not a car.
[1] https://www.bbc.co.uk/programmes/m00325dd
