Tiny variations: The big picture
Excellence doesn’t look the same from every angle
Here in the middle of Mental Health Awareness Week, I'm reminded that the human brain operates on a continuum of reliabilty, having hit one of those mornings where everything I touch seems to go unspectacularly awry: a to-and-fro with a designer on an upcoming whitepaper (the fault is entirely mine for re-jigging the order of questions in a survey without telling him which bit of text goes with which graph) and a small coffee spillage makes me realise that ‘functional’ is a long way from ‘optimal’.
As with all good processes, then, it’s important to match the task to the materials, and it seems smart to acknowledge my temporary woolliness and focus instead on tasks that have a distant deadline – certainly long enough to be given a considered redraft with fresh eyes when the fog clears.
The beauty of robots is that they don’t have moods like this: 24/7/365, they just need a steady electrical supply and the work keeps on flowing. This human is quickly realising there are only so many hot drinks one can usefully have in a morning.
A fleeting variation in my mental capacity may not have a huge impact on the rest of the business, but process variations across larger areas can be a headache for larger organizations. ‘Managing process variations still seems to be unnecessarily complex, costly, and inconsistent,’ says Ivan Seselj quoting Steve Stanton, an analyst with FCB Partners: '90 percent of the organizations I know have failed at standardization.'
And yet standardization seems to be the end goal for many of the processes under consideration. This wrestling with standardization versus specialization may not be doing us any favours: the small project doesn’t have the oversight to make the best decisions, and the centralized decision makers will struggle to take into account all of the granular knowledge to ensure that their scheme will work in a particular environment.
A great example of getting the best of both worlds is the company Biwater, a company specializing in water infrastructure.
Their approach, and one we should all think about following, is to be selective about the knowledge applied in a specific area. The truth is that excellence doesn’t look the same from every angle, and the same solution will not fit every problem equally well. Biwater builds in variation at the beginning of their project, rather than leaving it to the last minute and having to fit a solution into a space it wasn’t built for.
It put me in mind of Alex Balbontin, who talked to Seth Adler on the Pex Podcast about the difficulty of getting RPA to work in a fragmented space: ‘It’s early days [of RPA] for banks… the more you see of a process, the more you have to look at it at a key stroke level… and you can see so many variants depending on the type of client, the market the legal entity… so you might find a team of 20 people doing some work with, you map what they do at key stroke level you find naturally you have 200 variants, so how do you robotize a process like that?’ (Skip to minute 27 for this discussion.)
Alex’s strength is the range of businesses he’s worked in and the diversity of his experience in working in different industries in different countries. The easier it is to imagine different circumstances – and experience counts for a lot here – the easier it is to see how a solution that looks brilliant on paper might not go quite so swimmingly in certain situations.
Whether you’re applying a process to a business, or applying a task to yourself, it’s sensible to accept that variance is a part of the picture. Just as AI is learning to be more flexible with information, we humans have to remember that part of being human involves differences of culture, mood and health, and sometimes just accept that for some of us, on some days, getting all of the coffee entirely to its intended destination is more of an ambition than a reality.