Reworking the risk equation

It is hard to argue with the old project management equation:

Risk exposure = Risk impact x Risk probability

Risk impact is often take to be money, say $1,000,000 and probability a percentage, say 50%. So exposure is also in money, $500,000. There is something rather obvious about it. So, while I’ve long disliked the equation I’ve not taken it to task, I never quite put my finger on it, or perhaps, I couldn’t really articular a winning argument.

That changed last week when I took “Papa” Chris Matt’s dog for a walk, or rather, he walked the dog I just tagged along and discussed the state of the world, and the thing we call agile.

Chris has a better equation:

Risk exposure = Money invested x Time to payback

Probability is out, after all, if something fails then it all fails. You were never going to loose $500,000, although you might loose the whole million. Probability is something of a red herring.

Chris’ insight here is that there is risk until the moment when you start seeing payback, i.e. until the investment starts pay is proven. In retrospect I should have realised this before. I read How Big Things Get Done a few months back and Bent Flyberg has the same idea. He points out that the time of biggest risk starts when the big money gets spent and continues until the project is delivered. Flyberg uses this logic to argue that long, detailed, upfront planning is good – because it is cheap and allows problems to be identified and avoided). Once you start the real work then do it as fast as possible; keep moving fast even if it costs you more because until the window is closed everything is as risk. (A highly recommended book by the way.)

The lesson many people take from this is: spend along time in planning and make sure you are certain about what you are doing before you spend the big money. I suggest a more important lesson is: once you start spending the money get to the point of return as fast as possible. Slowing spending does not reduce risk or save money, it increases risk and reduces benefit.

I’ve pointed out before that one way to increase the return on investment (significantly) is to make sure work starts paying back sooner. For example, make sure that the team deliver something every month (which itself reduces risk because something – anything! – is actually being delivered) and have that thing earn some revenue even if it is less than the full amount you want. When you do return on investment calculations correctly (i.e. account for time) then bringing forward the point where some (even a little) money is returned causes ROI calculations to jump.

Although it might seem too good to be true, the same logic will reduce risk. Both on paper and in practice.

Everyone who has ever argued for measuring and reducing cycle time will be happy right now. But Chris has a second point: today’s endeavours often involve multiple teams, and until all those teams deliver there is no return.

Think of it like ships in a convoy: they move at the speed of the slowest ship. None of the ships arrive until they all do, until they do they are all at risk, and since they are moving slow there is more risk. It also means, that reducing risk, speeding delivery, increasing ROI is going to depend on speeding up the slowest ship. If that sounds familiar its because it is what theory of constrains says: the system is constrained by its most significant bottleneck.

Verified by MonsterInsights