Weather Sensitivity Analysis:
Effect of Weather on Demand
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Causal  Analysis of the Effect of Weather on Demand

If there is a suspicion that sales are affected by unseasonal weather it is a good idea to analyse the weather sensitivity of demand with a causal analysis. At Forecast Solutions we use specialist statistical software to analyse weather effects and to determine if a causal relationship is present. If there is 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 the effect of that hot weather is understood from a causal analysis, the sales history can be adjusted for the effects of weather before running a time series forecast. Thus the future forecast will be made on a weather-neutral basis and will not be ramped up erroneously.

Need for expert help

Although a basic causal analysis using the regression tool in Excel's data analysis toolset can give an initial indication of weather effects, specialist statistical software is necessary for a thorough analysis.  One reason is that the various measures of weather such as mean, minimum and maximum temperature, sunshine hours and rainfall tend to be closely correlated with each other and easy to misinterpret. It may ne necessary to include the possibility of time lags due to supply chain effects and this becomes much easier with specialist software. Also, great care is needed to avoid confusion of the results with natural seasonality or inherent trends in market size or share.

Forecast Solutions has the necessary software and skills for this type of analysis and can help integrate the results into an improved sales forecasting process.