Weather Sensitivity Analysis - Measuring Weather Effects
Although the phrase 'weather sensitivity' can have various meanings, we are referring here to the effect that weather may have on demand for products or services. It is often a a good idea to carry out a weather sensitivity analysis to measure the impact of weather on demand as this may help in the interpretation of historical sales and in demand forecasting.. At Forecast Solutions we use advanced statistical software to carry out weather sensitivity analysis and this is done using historical data on company sales and weather.
The weather sensitivity analysis needs to evaluate alternative factors e.g. temperature, rainfall, sunshine, wind speed and take account of thresholds that may apply. For example, demand for some products may only increase if the temperature reaches a certain level. There may be delays in response due to to supply chain and other factors, therefore it is usually necessary to evaluate various time lags.
In order not to confuse the findings with normal seasonality, it is sometimes useful to seasonally adjust the weather data and/or demand data as part of the weather sensitivity analysis. Additional factors, for example prices or economic indices, can be included in the statistical analysis if they are relevant.
As an example, Forecast Solutions has carried out weather sensitivity analysis to measure the daily effects of weather on household insurance claims. Using the results of that analysis, we have created short-term call centre forecasting models incorporating daily weather forecasts.
For longer forecast horizons, when a reliable weather forecast can be difficult to obtain, we have used weather sensitivity analysis to better understand historical events and to adjust historical sales data prior to statistical forecasting, such that the forecasts can be based on a 'neutral' weather scenario. One example of this was with peat and compost products sold to growers and garden centres.
Data for Weather Sensitivity Analysis
Historical data on sales and weather, including measures of temperature, rainfall and sunshine hours, is employed in a causal analysis in order to identify the key weather factors that affect demand. Reliable historical data on UK weather can be obtained from the Met Office or other suppliers.
For a forecasting system built around weather sensitivity analysis, data updates will be needed for ongoing weather data. The minimum requirement will be for the latest historical information and, if weather is to be used for short-term forecasting, the short-term weather forecast as well.
Weather Related Demand Forecasting
To make the fullest use of any causal factor in forecasting it is desirable to make use of an accurate forecast of that factor over the forecast horizon. With weather data this is sometimes possible when making a very short term forecast directly of retail sales, but for companies further down the supply chain it can be more difficult. For these companies there is often a need to make a somewhat longer term forecast in larger time buckets e.g. weeks rather than days, and a reliable weather forecast can be hard to find. However, a causal analysis on weather can still improve demand forecasting through a better interpretation of historical effects.
By way of example, consider a business that manufactures horticultural products. Unseasonal weather definitely has an impact on retail sales in this market and there is also an important business-to-business channel in the form of horticultural growers. If there has recently been a period of unseasonally high rainfall, sales will undoubtedly have dipped during the wet weather. If the effect of that extreme weather is understood from statistical analysis, the sales history can be adjusted for the effects of weather prior to running a statistical forecast. Then the forecast can be made on a weather-neutral basis.
See some case studies on analysis we have done to quantify the weather sensitivity.
Need for expert help
Although a basic causal analysis using the regression tool in Excel's data analysis tool set can give an initial indication of weather effects, specialist statistical software is usually necessary for a thorough analysis. One reason for this 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 are very easy to misinterpret.
Also, the likelihood is it will be necessary to include the possibility of time lags due to supply chain effects. For example, for some businesses the effect of high rainfall in the current week might have a distributed effect across this week, next week, following week, etc. This process benefits from the use of specialist software and, preferably, an analyst with a good body of previous experience .
Of course the weather is an integral part of market seasonality for many products. Therefore great care is needed to avoid confusion of the results with natural seasonality or inherent trends in market size or share. It is often necessary to seasonally adjust the historical weather data and/or historical sales to obtain the best understanding from the causal analysis.
Forecast Solutions Planning has the necessary software, skills and experience for weather sensitivity and other causal analysis and can help integrate the results into an improved sales forecasting process. We can also analyse the impact of other drive factors such as price sensitivity or the effect of economic indices, enabling us to build causal models incorporating several variables.