Performance metrics, KM and asparagus. The story tells how publishing data about aircraft loading procedures among Peruvian asparagus producers motivated the poor performers to learn from the good performers, for the benefit of all, The authors of the study I quoted claimed
Objective proof of superior performance helps overcome a principal barrier to
convincing experienced professionals to adopt new practices - that is, the
belief that they are already doing the right thing and that their current
results are the best that anyone can expect
I would like to follow that thought - about how benchmark data can motivate people to learn.
One of the biggest barriers to overcome in KM is the lack of desire people usually have for learning from others. It's the old "Not Invented Here" syndrome, and behind "Not Invented Here" are two things
1. People are comfortable and familiar with their own performance, and with the way they currently do things
2. Change involves risk. "If my way works" they think, "why risk changing it? Why change horses in midstream? Why ditch a perfectly good approach, for something unfamiliar?"
The great thing about good performance data, and good benchmark data, is that people then often come to realise that their approach is not "perfectly good", that their way may "work", but it works pretty badly. They become uncomfortable with their own performance, and beconme open to learning. "Not Invented here" disappears, because they realise that "Invented Here" is not actually very good!
We see this very very clearly in our Bird Island exercise, where people were comfortable building an 80cm tower, and think they might be able to stretch it to 120cm. Then we show them benchmark data where the record is over 3m, the mean is 285cm, and even a bunch of Chicago lawyers achieved 250cm. And we show them a picture of the record tower, so they can see this is not a joke.
What happens, is that the people are shaken out of their comfort zone, They realise their own performance was pretty poor. They become very open to learning. And they DO learn, and they also turn in a top quartile performance.
The old motivation, to be safe and secure with a known approach, is replaced by a new motivation. The new motivation is "To do a decent job". (And they often feel that of they are being soundly beaten by Chicago Lawyers, they are't doing a decent job!*)
When you think about it, most people are professionals. They have pride in their work. They don't like to put in a poor performance. It's only the Homer Simpsons of this world** who are happy with shoddy work. So the existence of benchmarking data or performance data makes people aware if their performance is bad, they become dissatisfied with their approach, and are open to learning something better.
This is a very positive motivation. It reminds me of the introduction of technical limit (a very detailed approach to benchmarking and target setting) in the USA, and the motivation of the Ocean America drill crew to try new things and learn new approaches. Here's what the guy in charge of that program said.
"Technical Limit creates a new world; it creates something that says "there is a
big difference between perfection and where we are at today. We really were not
doing it to save the company millions of dollars; we were doing it because we
wanted to be undeniably the best drilling team in the world".
That's a very positive motivation - to be undeniably the best. And that's where the combination of good performance data and KM can really help. The performance data tells you if you're not the best, and it tells you who the best is. It tells you that you need to learn, and it tells you who to learn from. KM enables that learning.
*Apologies to all the Chicago Lawyers out there
**I was thinking of when Homer said "If you don't like your job you don't strike. You just go in every day and do it really half-assed. That's the American way".