Forecasting using judgement is very common in practice. There are many cases where judgmental forecasting is the only option, such as when there is a complete lack of historical data, or when a new product is being launched, or when a new competitor enters the market, or during completely new and unique market conditions. For example, in December 2012 the Australian government was the first in the world to pass legislation that banned the use of company logos on cigarette packets, and required all cigarette packets to be a dark green color. Judgement must be applied in order to forecast the effect of such a drastic policy as there are no historical precedents.
There are also situations where the data are incomplete or only become available after some delay. For example central banks include judgement when forecasting the current level of economic activity, a procedure known as nowcasting, as GDP only becomes available on a quarterly basis.
What has been learned from research in this area 1 is that the accuracy of judgmental forecasting improves when the forecaster has (i) important domain knowledge, and (ii) more timely up-to-date information. A judgmental approach can be quick to adjust to such changes, information or events.
Over the years the acceptance of judgmental forecasting as a science has increased and so has the recognition for its need. More importantly the quality of judgmental forecasts has also improved as a direct result of recognising that improvements in judgmental forecasting can be achieved by implementing well-structured and systematic approaches. It is important to recognise that judgmental forecasting is subjective and comes with limitations. However, implementing systematic and well-structured approaches can confine these limitations and markedly improve forecast accuracy.
There are three general settings where judgmental forecasting is used: (i) there are no available data so that statistical methods are not applicable and judgmental forecasting is the only feasible approach; (ii) data are available, statistical forecasts are generated and these are then adjusted using judgement; and (iii) data are available and statistical and judgmental forecasts are independently generated and then combined. We should clarify that when data are available, applying statistical methods (such as those discussed in other chapters of this book), is preferable and should, at the very least, be used as a starting point. Statistical forecasts are in general superior to generating forecasts using only judgement and this is commonly observed in the literature. For the majority of the chapter we focus on the first setting where no data are available, and in the very last section we discuss judgmentally adjusting statistical forecasts. We leave combining forecasts for a later edition of this book.