Everybody knows that no one can accurately predict the future, at least that’s my view. However, companies depend on accurate forecasts to survive. In the current economic decline, forecasts about how long and deep the decline will be are required to take the appropriate measures. In the Netherlands, several companies that depend heavily on the tendency of the market, like ASML, TNT Express, Corus, need to act quickly in order to survive. They have to (temporarily) lay off people, close down plants or reduce costs very fast. Getting this right reduces the impact of the measures that need to be taken, also it reduces the probability that too harsh measures are applied, setting back the company’s ability to take advantage when the economy booms again. Can Operations Research be of any help?
In forecasting, Operations Research offers techniques that can offer some assistance. These techniques come from the area of Econometrics and have a statistical background. They can be divided into two groups, time series analysis and regression analysis. In time series analysis a formal pattern in a sequence of observations is identified and used to predict future values. In regression analysis a formal relationship is determined between a dependent variable and one or more explanatory variables or predictors. Note that both assume that the pattern that was identified prevails in the future. A lot of research has been done in developing and improving these statistical techniques. Major problem is that these techniques require enough reliable data and should be applied by specialists.
I have encountered many examples in projects in which time series or regression analysis was applied without a sound theoretical fundament, leading to ill defined relations and therefore bad forecasts. One of the examples was in the travel & leisure business. In that particular project statistical techniques were used to identify factors that determined the demand for hotels and residential leisure parks. The idea was to use these factors to predict the future demand, based on which the price was determined to maximise the firm’s revenue. The error in the approach was that to forecast future demand, each of the identified predictors of demand needed to be forecasted. No data to support that was available. So, instead of forecasting the future demand alone, the company ended up with having to forecast several unknowns.
In forecasting I have obtained the best results when combining the statistical techniques with judgemental methods. Judgemental methods are subjective, but allow you to incorporate intuition, expert opinion and experience (so still a bit of palm-reading is required). By interviewing experts in different areas of expertise a qualitative vision on the future can be developed. Combining these views with what has been identified with statistical techniques leads to better forecasts. When constructing different (expert) views of the future, scenario analysis helps you to even better determine the strategy for the future. This way you can support companies to identify the best way forward, but also show them what the future can possible bring them (not just a point estimate, but a range of possibilities) improving the quality of the strategy.