Simplest forecast is a quantitative extrapolation of the past. This assumes that the future is somehow a logical function of the past. The simplest extrapolation is a straight line, but it can also be a bended curve or even an S-curve. We can still call it extrapolation. One may add trends, seasonality and cyclical variations. Major advantage of extrapolation is that it is simple and easy to understand. The disadvantage is not that Black swans are not included, because that is true for all forecasting systems. Nay, the major disadvantage is that all non-linear effects are omitted. For example the inventory effects of the supply chains are certainly non-linear and thus can be surprisingly counter-intuitive. 

The simple extrapolation has been made more complex over time by using a weighted moving average,  by Exponential smoothing, by Autoregressive moving average (ARMA), or by Autoregressive integrated moving average (ARIMA). It all remains the same: extrapolation of the past. 

Many companies enter expected sales on product, customer or even product-customer level into their ERP system. Some companies do this up to 18 months into the future. When the whole crew of sales managers is monthly filling up a database, bottom up, it seems that the combined level must be reliable because it is based on very careful thinking by people who know their customers best, a bit like crowdsourcing. Problem is that the sales managers do not know what their customers will order, because their customers don’t know either. And the people involved often adapt their detailed forecast to the expected total, then split it up until it fits. Another problem is that it is a lot of work, and it gives the false impression that it is accurate and well supported by the team. And it is only linear, and it does not take the supply chain effects into account. Main problem is that it is sentimental. Advantages of this system? Don’t know. It sounds solid because it is expensive. I’d say that in most cases this can only be used to break up a forecast on total level into customer/product combinations for production planning. Don’t you ever base your capacity expansions on this. I expect that many companies use this because everybody is doing it, it is massive, expensive and sounds as if everything is under control. “Nobody ever got fired for properly implementing a huge and expensive ERP system. “ Or were they?

Life cycle analogy (LCA) is a qualitative forecasting method based on the idea that similar products will have a similar life cycle pattern, so if you know how a previous product did in the past, you have a fair assumption how a new product will do. The main disadvantage is immediately clear: this is a very rough approximation. Results from the past never give a guarantee for the future. Times change. Preferences change. Competing technologies change. In reality a lot of people unconsciously use LCA and call it “experience”. What can be valuable however is the shape of the product life cycle. This shape can be used in combination with other, more quantitative methods, as a sort of trend line representing Market Share development and the penetration of a product into an end market. The Flostock models can work with this.  

This seventh alternative to Flostock modeling is used especially in companies that have a lot of new product introductions.  To estimate the success of a new product, companies consider what happened last time this kind of product was introduced, and adjust the estimate for any plausible differences. This can be based on their own product line or on product introductions from other companies. The obvious disadvantage is that success in the past does not guarantee the future, because circumstances differ and –hopefully-  certainly for new product. Also competition is evolving, customer preferences change, technological progress does not stop, etc. So all in all this forecasting method is pretty limited in applicability and usefulness.  

This method is about getting a higher quality forecast by averaging the forecasts from different experts, made with different methods. Big advantage is that some biases and systematic errors will be averaged out; in that sense it is similar to crowdsourcing.  One example is the Delphi method, in which there are several rounds, with result sharing after each round, so the group works towards consensus.  An obvious problem with such open method is that the loudest voices will dominate, so it will not avoid herd behavior.  Another, more basic problem is that it is sentiment based, which makes it ‘linear’ by definition, thus overshooting at each ‘turn of the road’. This is because all variants of sentimental forecasting miss the non-intuitive effects of the Stocks as discussed in Law 19.