Best Practices in Weather-based Sales Forecasting

Weather is a source of significant fluctuations in consumer demand, and because of the bullwhip effect, it can produce unnecessary high fluctuations on the supply side as well. These fluctuations can quickly turn into costs: prepare too extensively and you’ll end up breaching the capacity limitations at every level of your supply chain and increasing your fresh goods spoilage, but failing to prepare sufficiently can lead to significant lost sales. What’s more, the lost sales do not only apply to products that go out of stock – especially during extreme weather conditions when customers are more likely to make their decisions on which store to visit based on the availability of some key product, for example bottled water during hot temperatures.

In our whitepaper, Towards a Weather-Proactive Supply Chain, we’ll look at how retailers can optimally prepare for weather-related fluctuations. 

Here are some best practices in weather-based sales forecasting:

It’s most effective to build weather-based sales forecasting on top of stable baseline forecasts, which automatically consider things like weekday variation, seasonality, trends, holidays and promotions; allowing the weather model to concentrate on the weather effects. Additionally, it gives visibility on what part of the forecast is coming from which forecast model. This leads to several benefits in the actual use of weather models: different models and their outputs can be separated to for example quantify whether the weather model really improves forecast accuracy. On the other hand, different models and their outputs can be compared transparently so that e.g. in the case of upcoming large weather-based forecast uplift, the uplift can be put into context to verify if it makes sense or not, and whether the retailer should act on it.

It’s advisable to build a weather-based sales forecasting process that looks iteratively at models at different levels, so that each product in each store get the model that fits its data best. The SKU-store combination is the lowest level and usually most accurate as well. So, in theory each product in each store can get a separate model, because each product and each store can have different responses towards weather. Especially, store location can make a big difference. For example, when comparing the weather reactivity in a store in some central location with lots of tourists to that of a hypermarket in a location only reachable by car, usually the central city location is much more weather reactive than the hypermarket.

In addition to raw weather data, best results are achieved when some of the more sophisticated weather effects are considered. Weekday variation is something worth considering. On top of normal weekday variation, weather reactivity can differ from weekdays to weekends. Another thing to consider is past and future weather conditions. Responsiveness towards weather is usually stronger for example on the first sunny weekend of the spring than a sunny weekend in the middle of the summer when there has already been plenty of sunny weekends. Consumers also tend to plan ahead based on forecasted weather, and thus shift their purchases to earlier in the week in the case of an upcoming nice weekend. It goes without saying that nonlinearities in the weather effects needs to be considered. For example, it’s possible that high temperatures increase demand, but extremely high temperatures start to decrease the demand, because consumers stay home.

Weather reactivity can also vary significantly at different times of the year. Ice cream for example can be weather sensitive during summer, but not during winter. Figure 4 illustrates this phenomenon. The variation in temperature is approximately the same throughout the year, but the variation in sales is very much different during summer and during winter. During winter we clearly see that there’s absolutely no point on adding any weather-based sales forecasting, as there’s very little variation in sales. But during the summer, the variation in sales grows significantly and the need to incorporate weather into sales forecasts becomes apparent. Therefore, it’s valuable that the weather model can be activated only for some period and consider only this period in model estimation as well. In practice, the weather-model estimation shouldn’t consider the periods where there’s no need for it, for example winter in the Figure 4 case. In that way, the weather-model accuracy is increased as unnecessary noise is left out.

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Read the full whitepaper here: Towards a Weather-Proactive Supply Chain