I’m going to put “An Operational Definition of ‘Demand’ – Part 3” on hold for a moment, to announce a new favorite article on forecasting, “Living in a world of low levels of predictability,” by Spyros Makridakis and Nassim Taleb (International Journal of Forecasting 25 (2009) 840-844. IJF is a publication of the International Institute of Forecasters, and if not already a subscriber you can purchase the article from ScienceDirect.)
Many of you already know Makridakis as co-author of the standard forecasting text Forecasting: Methods and Applications, and Taleb for his Fooled by Randomness and The Black Swan. Taleb, in particular, has drawn attention to the issue of the un-forecastability of complex systems, and the sometimes disastrous consequences of our “illusion of control, pretending that accurate forecasting was possible” (p.841).
While referring to the (mostly unforeseen) global financial collapse of 2008 as a “prime example of the serious limits of predictability” (p.240), this brief and non-technical article summarizes the empirical findings for why accurate forecasting is often not possible, and provides several practical approaches for dealing with this uncertainty.
So why am I, a vendor of forecasting software, so excited by an article telling us the world is largely unforecastable? Because Makridakis and Taleb are correct – we should not have high expectations for forecast accuracy, and should not expend heroic efforts trying to achieve unrealistic levels of accuracy.
Instead, by accepting the reality that forecast accuracy is ultimately limited by the nature of what we are trying to forecast, we can instead focus on the efficiency of our forecasting processes, and seek alternative (non-forecasting) solutions to the business problem. Methods I have touted, such as Forecast Value Added analysis, can be used to identify and eliminate forecasting process activities that do not improve the forecast (or may even make it worse). Large-scale automated software, such as SAS Forecast Server, can deliver forecasts about as accurate and unbiased as can reasonably be expected – and do this without elaborate processes or significant management intervention. For business forecasting, the objective should be:
To generate forecasts as accurate and unbiased as can reasonably be expected – and to do this as efficiently as possible.
The goal is not perfect forecasts – that is wildly impossible. The goal is to try to get your forecast in the ballpark, so you can plan and manage your business effectively, and not waste a lot of company resources doing it.
And when, because of the nature of demand or other behavior, you cannot forecast with the degree of accuracy needed for effective planning, then seek alternative approaches to address the underlying business problem. In the past I’ve suggested things like demand smoothing (to make the demand forecastable), or supply chain re-engineering (to minimize your reliance on accurate forecasts). You can find more discussion of these in a 2001 article I co-authored with Drew Prince of NCR, “New Approaches to Unforecastable Demand” (Journal of Business Forecasting, Summer 2001, pp. 9-12), available for download from the Institute of Business Forecasting.
8 Comments
Hi Michael - great article! May I question something in your definition: I'd suggest that your definition for the objective (of business forecasts )is useful but it lacks any kind of feedback to the planning cycle. For me forecasts should be the most accurate available given a certain understanding of what the business is planning to do. For this reason, I have my doubts about pure time series methods without causal inputs. For me, one of the great mysteries in many businesses is the failure to ensure demand and supply forecasts are aligned. Of course, this doesn't mean that these methods are invalid (far from it) but a business needs to understand whether their forecasting methods are naive or not. By speeding up the forecasting process to reflect new plans, businesses can really harness the power of statistics to improve their operations.
Another point - I've also advocated that businesses redesign processes to make demand forecastable. However too much predictability often means that they're not trying new things and that's not a viable long term strategy. Variation is good - it's experimentation and needs to be managed.
Final point - unpredictability is a big source of risk. As I discussed with a client last week, some markets are just unpredictable. By understanding this, they can have a much more grown up debate internally. This company (major multinational) essentially assumed that all categories were equally (un)prefictable and ran their supply chain accordingly.
Since accurate forecasting requires more than just inserting historical data into a model, Forecasting adopts a managerial, business orientation. Thanks for sharing the content of your fave article.
Great article, I also believe forecasting requires more than just historical data.
Jenny Newman
It just seems to me that forecasting is an expensive process that still seems to be very infective. You gather loads of data for your business which in itself costs money both in terms of labour and software, then once all the data has been gathered this is where the problem occurs most (all that I have tried) just doesn't make use of this data and the statistics they give you for predicting future trends are just useless. maybe once this obstacle is overcome then businesses will have more faith in spending money on forecasting.
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Nice, well explained commentary. Makes clear the relevance of the article.