On 2007-02-12 22:17:00, Allan Engelhardt wrote in CYBAEA Journal:
Interest in productivity and how to manage innovation and know-how appears to be growing. Our article on employee productivity gets about four times more hits than the next most popular post. I imagine that more and more people in the West are waking up to the fact that innovation and high productivity is the only thing that keeps jobs here (though the link from USA Today probably didn't hurt).
A recent paper from Harvard considers the optimal implementation of knowledge management. With a slight reformatting for clarity:
We derive three main results [about the optimal management of know-how].
- First, information about successes is typically more useful than information about failures, since successful methods can be replicated while failures can only be avoided. This supports firms' focus on 'best practice'.
- Second, recording mediocre know-how can actually be counter-productive, since such mediocre know-how may inefficiently reduce employees' incentives to experiment. This is a strong-form competency trap.
- Third, the firms that gain most from a formal knowledge system are also the ones that should be most selective when encoding information (i.e., the ones that are most at risk from the competency trap); namely, large firms that repeatedly face problems about which there is little general knowledge and that have high turnover among their employees.
Beyond these main principles, we also show that it may be optimal to disseminate know-how on a plant-level but not on a firm-level, and that storing back-up solutions is most valuable at medium levels of environmental change.
I am not sure I am comfortable with the methodology (I prefer practical results and observed measurements to theoretical mathematics, and in any case they could get to the core of the conclusion with much less differential and integral calculus), but their conclusions broadly rings true. Does anybod have any experimental data in this area?
However, on the third point in particular notice the emphasis on formal knowledge systems. I am thinking that a more comprehensive list along their main dimensions would look something like this (click for larger version):
For what we call unknown problems (problems about which there is little general knowledge
) the main focus is on collaboration: working together to define solutions to the organization's problems. For companies that are mainly faced with known problems the issue is one of sharing this information effectively.
On the other dimension, with low turnover you benefit from an unstructured approach. The task is mainly a socal one of finding the right people with the right expertise or attitude. An emerging, bottom-up approach is entirely appropriste. But in the high-turnover situation you need a way of structuring th ework to ensure that your relatively inexpereinced workforce are addressing the right problems. You need to impose more of a top-down structure for the collaboration or sharing.
In terms of technologies, sharing can be achieved with document management systems while collaboration is more suitable for a wiki. The social element comes from some way of expressing onself (e.g. blogs) and a way of connecting people (RSS aggregators and lists of experts).
On 2010-07-13 07:47:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:
I am not sure apeescape’s ggplot2 area plot with intensity colouring is really the best way of presenting the information, but it had me intrigued enough to replicate it using base R graphics.
The key technique is to draw a gradient line which R does not support natively so we have to roll our own code for that. Unfortunately, lines(..., type="l") does not recycle the colour col= argument, so we end up with rather more loops than I thought would be necessary.
We also get a nice opportunity to use the under-appreciated read.fwf function.
Read more (~535 words).
On 2010-06-22 11:45:00, Allan Engelhardt wrote in CYBAEA Journal:
We have a mild obsession with employee productivity and how that declines as companies get bigger. We have previously found that when you treble the number of workers, you halve their individual productivity which is scary.
We now re-do the analysis four years later and, just because we can, we are using the leading companies of the London stock exchange instead of the largest American companies.
The results still hold. We called it the 3/2 rule: treble the number of workers and you halve their individual productivity. Large companies with ten times the number of employees are ¼ as productive as their smaller competitors.
Employee productivity is a big issue. If all the FTSE-100 companies achieved their average profits per employee, then the index would generate almost £1 trn of additional net profits for the economy.
Read more (~245 words).
On 2010-06-22 11:20:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:
We have a mild obsession with employee productivity and how that declines as companies get bigger. We have previously found that when you treble the number of workers, you halve their individual productivity which is mildly scary.
We revisit the analysis for the FTSE-100 constituent companies and find that the relation still holds four years later and across a continent.
Read more (~763 words, 5 comments).
On 2010-06-17 09:05:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:
Following on from my previous post about improving performance of R by linking with optimized linear algebra libraries, I thought it would be useful to try out the five benchmarks Revolutions Analytics have on their Revolutionary Performance pages.
Read more (~300 words, 2 comments).
On 2010-06-15 10:21:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:
Can we make our analysis using the R statistical computing and analysis platform run faster? Usually the answer is yes, and the best way is to improve your algorithm and variable selection.
But recently David Smith was suggesting that a big benefit of their (commercial) version of R was that it was linked to a to a better linear algebra library. So I decided to investigate.
The quick summary is that it only really makes a difference for fairly artificial benchmark tests. For “normal” work you are unlikely to see a difference most of the time.
Read more (~934 words, 1 comments).
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