Demand forecasting is not an easy task. For a modern business, a lot depends on an accurate forecast. Combine that with the challenge of choosing the relevant variables, and it only gets more demanding. A good forecast supports many decisions without which you may be left pretty uncertain. Whether you want to introduce a new product line, do an audit of an existing one, or optimize your supply chain–an accurate demand forecast is your first step to effective planning. With accuracy, you can not only decide how much to order and how much to do away with it but also fine-tune logistics, develop products, and eliminate unnecessary markdowns. Without it, you’re just taking risks.
In the 90s, Cisco had ridden the technological wave very smoothly. However, heading into the new millennium, the market showed strong symptoms of a collapse. In 2001, Cisco could not perceive slowing demand and announced a $2.2 billion inventory write-down.
Only some situations result in such ramifications. But it drives the point home. Without accuracy, a demand forecast doesn’t quite hold on its own.
Accuracy is not easily achieved in demand forecasting. Traditional forecasting models that rely on extending historical patterns into the future often turn out to be incorrect. The reason is that external factors influencing a peak or trend in the past may only sometimes be impactful. Or maybe there is a new driving force altogether that’s being ignored. It’s up to the forecaster to factor in the most relevant external variables that show a high correlation with demand.
Perhaps, there is none more relevant than the weather.
The weather clearly affects consumer behavior, purchase intentions, and human health. Whether directly or indirectly impacts industries, even those that don’t seem to be linked to the weather. While the weather doesn’t change your product, it does change the willingness of people to buy it.
A report titled ‘profit of 1 degree’ posits that the slightest change of 1°F in a year across the United States can change the weekly revenue of various products. It showed how categories like infant apparel and hedge trimmers rose by 4% due to an increase of one degree, while lip care sold +5000 units owing to a one-degree drop. Similarly, in the UK, Sainsbury–a supermarket chain, sought to understand this correlation in a particular category of products. They discovered that a rise of a mere few degrees in the early spring increased BBQ sales by 200%.
The consequences of not involving weather while creating forecasts are also concerning. In grocery retail, factoring weather reduces up to 75% of forecast errors–not a number to be taken lightly.
One of the reasons why weather-adjusted forecasts tend to be so much more accurate is that nobody is beyond its impact. Even if businesses don’t perceive the weather with a strict meteorological guide, they plan a lot of their lives around ‘how it feels.’ Especially around the outlier temperatures. A heat wave, a sudden dip, or a day that seems hazier than usual can drive up or down sales, footfall, and engagement because it influences people's moods.
A sunnier day can even make your audience willing to spend more. If your offerings have any aspect of seasonality, then the weather has an even more prominent role. This study used ten years’ worth of data collected from various winter sports sales in Switzerland and Finland. They concluded that forecasting errors go down by 45% when including the previous year’s meteorological data.
It’s interesting to note that this isn’t a new practice. Organizations have been acutely aware of the environment's effect on their operations for many years.
All in all, it’s not easy.
The idea of forecasting demand by using weather requires a multi-dimensional and objective understanding because the weather has a varied impact over different locations and correlates differently with specific products.
The last few decades have also seen the rise of ML and AI in building statistical models that are programmed to learn and become better with each run. As a result, the forecasting cycle has become shorter–as it's now being carried out more and more frequently to achieve greater accuracy. To complement this, weather forecasts and data collection have also seen advancements. At Ambee, that is what we aim to deliver as well.
So the more granular, hyperlocal, and accurate the data is, the better the results will be.
Ambee’s weather API results from vigorous research, multiple algorithms, and numerous rounds of scientific validations. Ambee’s accurate weather data gives you the power to take weather considerations into making accurate forecasts from over 150+ countries. User-friendliness is also a significant priority here at Ambee, which is why the weather API is designed to make it easy to integrate with any platform or software.
This makes Ambee your most reliable partner when it comes to demand planning. If you want to learn more about how weather data affects your industry, head over to https://www.getambee.com/api/weather.
For any more queries and discussions, feel free to get in touch with us here: https://www.getambee.com/contact-expert