In my last blog entry I introduced the notion that optimal decision making wasn’t ‘on the radar’ for many clients in Australasia, and laid out a couple of ideas why. I too once focussed on Monte Carlo simulation rather than decision evaluation, but last year the most obscure event changed that.
Call me a nerd of you will, but I like modelling problems in Excel. There is skill involved in setting up a problem such that the model assumptions aren’t too gross, and an art to making the model elegant. This elegance can be very important to optimisation problems, but more on that later. My first homemade optimisation problem was generated by motorcycle racing! MotoGP, to be precise. A friendly tipping competition with friends was formed at the start of the 2009 season with the following structure:
- Entrants played the role of Team Manager.
- Team Managers had a fixed budget to spend on riders.
- Either a few good riders could be purchased, or many lesser riders, or something in between.
- The team that had accumulated the most points at the end of the season was the winner and received kudos!
Although the future results could not be known of course so I set up and ran the optimisation with Evolver after the event to see what the optimal team selection would have been. Historical data could have been used to discover the type of rider mix that tended to be optimal and thus make an informed decision for this competition. The risk in having only a few riders was that any misfortune would have a big negative impact on the points won, whereas a team consisting of many (cheaper) riders was less likely to suffer such a fate. This downside scenario will be modelled into the 2010 MotoGP Team Manager predictive, optimised model (currently in production)!
What has this to do with the corporate world? Replace “team” with portfolio and “riders” with “assets”, “shares” or “projects” and you have a classic portfolio optimisation model. I hadn’t created this model with business applications in mind but I realised that was precisely what I was doing. An instant later I realised just how useful Evolver would be in many decision scenarios even though it doesn’t incorporate uncertainty (RISKOptimizer does).
In the next instalment I will further explore some practical applications for Evolver and you’ll see just how universally appropriate it can be.