Ernst Max Nielsen continues his series on "Where's the Beef?" (Click
the Category on your right hand side: "Where's the Beef". The articles
are numbered in Roman numbers: I, II, III etc)
Ernst Max Nielsen:
Over the coming months I'm going to dedicate some space to discuss a
methodology I call "Where's the Beef".
Inventors (professor or otherwise) approach the Technology transfer
offices (TOs) of Europe regularly – probably in excess of 100,000 times
every year – so we induce from surveys of university tech transfer
offices
For all players the big question is: is it worthwhile time and effort to try to commercialize (of the professor, the TO etc)?
Can we pick winners?
I’m not suggesting we can ”pick winners”, but we can say something
substantial about the potential if or when our invention hits the
market. I claim that we can predict a probable future of the beef (if
there is any) in 1 to 5 working days depending on the nature of the
invention.
The most important questions to answer are:
Is the Novel part of the invention also good? Does it stand out against the ”art”?
Is the Good part, on the other hand, novel?
Two fundamental approaches: econometrics or multi-criteria decision making
If you have searched the web for hits concerning the keywords
concerning the topic of this topic, you will find some typical
approaches. In general, I see two radically different approaches: one
which takes an econometric
view on the issue of valuation, which is philosophically a
”reductionist” and analytical approach, namely to find or reduce all
the aspects to one common numerical denominator: money, (how fast do I get my investment back?); the other a holistic
approach, which tries to ”operationalise” uncomparable qualitative
indicators, an approach using so-called multi-criteria decision support
tools adding weights or rating to qualitative aspect thus allowing
some sort of numerical ranking.
Let me state immediately that I have seen a lot of econometric methods
employed but never been impressed by their power of prediction; the
number of uncertainties are simply too many to attempt to make a
science out of business decision making. If indeed, we (someone) could
predict ”where’s the beef”, we would be rich and not sit here today ☺.
So, how can we reduce the error margin in valuating inventions? I
re-statedr topic. How to make a business out of an invention, and
reminding ourselves that some businesses are better than others and
that business decisions are not logical/rational not scientifically
proven. To me (e)valuation is an art rather than a science.
In the following essay, I have made some notes about the key approaches
in my clinic. I refer to other documents and diagrams, which you must
look at when reading the following in order to get the greatest benefit.
Examples
A classical -albeit US American approach is the "formula method", as for instance described at this site.
Its drawbacks are many, the worst, however, that it requires a history
of financial data, the more the better. Think about it! What business
has been left basically unchanged for more than 5 years? Especially in
the "hot and hype" areas such as IT, pharmaceuticals and recently
nanotechnology, we have nothing but hope and expectations.
Another interesting (US) concept is the Patent Value Predictor Click to read here,
discussed elsewhere on this weblog. Again, the problem with such
methods is that they require a history of data to be complete. On the
other hand, for the valuation of US patents, Richard Neifeld and Martin
Goffman come closer than most.
Let me say that I'm aware of the more recently used methods, eg., and they don't change my fundamental view:
- 25% Rule: calculation of license fees as 25 % of the gross profit (earnings)
- Market Orientation: actual value of future earnings in the market (consent about the market)
- Return on Sales (profit orientation): net profits in percent of sales
- Cost orientation: replacement cost or cost to design around a technology to be licensed
- Auction: attraction of potential buyers and obtaining of sealed offers
- Money flow: current value of future economic earnings (net sales minus expenses) calculated over life time
Later in this series, I shall discuss the virtues and problems of the multi-criteria decision support tools.
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