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Weather Sensitivity Analysis:
Effect of Weather on Demand
Weather Sensitivity - Analysis of Weather Effects on Demand
If there is a suspicion that sales are affected by the weather it is a good idea to analyse weather sensitivity with a causal analysis. We use specialist statistical software to analyse weather effects and to determine if a causal relationship is present. If so, we can quantify the effect of unseasonal weather on demand. Historical data on sales and weather is analysed, including measures such as maximum, mean and minimum temperatures, rainfall and sunshine hours. After carrying out the weather sensitivity analysis there will be a better understanding of historical weather effects and a more accurate future forecast can be made.
Reliable historical data on UK weather can be obtained through the meteorological office or other suppliers. For example, WeatherNet is a non-governmental provider of weather history and on-line weather applications.
Weather Related Forecasting
In the case of the weather it is particularly difficult to make a satisfactory weather forecast as the input to making a sales forecast. However, a weather sensitivity analysis can still improve the forecast through a better interpretation of the past. For example if there has just been an extreme hot spell and we are in the business of selling ice cream, sales will undoubtedly have surged through the hot weather. If we have quantified the effect of that hot weather, we can avoid ramping up the future forecast erroneously. So it may be useful to 'deweatherise' the history before feeding it into the sales forecasting system.
Need for expert help
Specialist software is invaluable in carrying out causal modelling such as weather sensitivity, but great care is necessary to avoid confusion of the results with natural seasonality or inherent trends in market size or share. The matter should not be left to an automatic process embedded in software and is best carried out by a specialist.
Forecast Solutions can expertly carry out the work and help maximise the benefits in terms of integration into an improved sales forecasting process.