From Forecasting to Adapting

Posted on by mat

Steve Player of Beyond Budgeting fame recently blogged about moving from forecasting to adapting. Idea being, rather than focusing solely on increasing forecast accuracy (and building ever more complex — and fragile! — models), why not get better at adapting to evolving circumstances?

I really like this notion. It fits nicely with the tenets of collective intelligence and social business intelligence approaches to decision making. In order to be able to adapt, a business has to be able to have an accurate and up-to-date read on what’s really going on, which is exactly the insight such applications deliver.

Thing is, forecasting is still vital. General Patton‘s famous quote comes to mind: “A good plan, violently executed now is better than a perfect plan next week.” So you start executing to a plan. At the same time, you enable everyone involved to continuously share information about how things are tracking. So long as the collective wisdom of the people in the trenches lines up with the master plan, life’s (probably) good. But when the two start to diverge, it may be time to adapt.

This entry was posted on Wednesday, April 28th, 2010 at 3:58 pm and is filed under Enterprise, forecasting. You can follow any responses to this entry through the RSS 2.0 feed. Both comments and pings are currently closed.

One Response to “From Forecasting to Adapting”

Donald Tiffany Jr. • April 30th, 2010 at 7:47 pm

From my bog on 10/31/2007 – replaced “decision support systems” with “systems that provide forecasting”
Feedback in systems that provide forecasting –
We monitor cell phone communications coming from certain parts of the world with the hope of detecting terrorist plans or intentions. They learn of this so they switch to other forms of communications. The question this raises is an old one.
How does or can the act of observing affect the behavior of the things being observed?
This question can be re-framed with regard to systems that provide forecasting in which case the question becomes, in a much more indirect sense, how are the predictions inherent in generating data presented to users as forecasts be affected by the ultimate decisions that are made based on them as those same predictions and decisions are made repeatedly over time?
In simpler terms, when does, if it does, the tail begin to wag the dog?
If it does, then how can such systems that provide forecasting compensate or dynamically adapt such that predictions remain valid and in fact become more accurate over time?