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Using Weather Data to Increase Forecast Accuracy

Weather

It may not seem intuitive at first, but weather fluctuations, such as a one-degree temperature change or a drop in humidity, can significantly impact your bottom line.  For instance, it should come as no surprise that an unseasonably warm winter will mean fewer sweaters, snow blowers, and countless other cold weather items sold.  But did you know that this effect extends to a whole range of other items that you wouldn’t expect?  Sales of screwdrivers, frying pans, motor oil and playing cards will also be affected by changes in the weather.  If it is too hot, customers might hit the beach.  If it is too cold, they might keep warm at home.  And there is a sweet spot where the temperature’s just perfect for going to the mall.  To enjoy sustained success in today’s world, companies need to take into account how weather affects consumer behavior when building their forecast models.

 

Planning for shifts in demand

Incorporating historical weather data when analyzing past behavior may lead to some surprising insights into how, why, and when consumers buy certain products or visit certain stores.  Ice cream suppliers, for instance, may see an uptick in sales during very hot, humid days, but only in stores located in malls or near tourist attractions.  These kinds of specific insights can, in turn, help companies shift inventory to meet the demand in a given area. 

Reddy Ice, which supplies packaged ice products to grocery and convenience stores across the nation, is another example of a company that is heavily reliant on the weather to drive consumer demand.  The vendors that stock Reddy Ice may also see an increase in demand during a hot summer day, for example.  Yet that increase could be tempered if the day falls on a Monday (as opposed to a Sunday).  Recognizing this, Reddy Ice chose John Galt to help implement a supply chain software solution that incorporates real-time weather data and can provide visibility into changes in demand at a granular level as a result. 

 

Producing a company-wide ripple effect

The decision has proven fruitful, and not just for adjusting the supply of the product itself, but also for all related business operations.  Over the past three years, Reddy Ice has transitioned from using completely manual processes and a hodge-podge of tools to using a comprehensive suite of tools that work together to automate and streamline the company’s workflow.  While there was the immediate benefit of reducing customer out-of-stocks by 50% when Reddy Ice initially implemented the Atlas Planning Suite, the good news didn’t stop with year one.  Reddy Ice has seen a 20% improvement in their delivery productivity numbers in 2018 when compared to the same period in 2017. 

And while sometimes the weather forecast doesn’t always work in the company’s favor — as illustrated when this past April’s cold spell resulted in fewer sales that month than in years past — the ability to accurately predict and then monitor those weather events in real-time allows Reddy Ice to better plan for and allocate their resources and labor accordingly.

 

Moving toward a more agile, automated business model 

The rise of big data and machine learning algorithms enables businesses to more efficiently interpret the vast stores of data at their fingertips and even predict, or sense, future demand.  The value of applying AI in this way is readily apparent in fashion — the Fashion Institute of Technology in New York has even devoted an entire course to weather forecasting and predictive analytics — but other industries can absolutely benefit as well. 

Companies, like Reddy Ice, that have mastered anticipating shifts in demand on a day-by-day basis can now focus on using the newest technologies in ever more precise ways.  For example, they may start to sense when an order for their product is about to be placed (whether by an existing or new customer) and automatically start the process of filling it, or they may focus on timing a particular order so that it’s delivered at 1:00 p.m. instead of simply “in the afternoon.” 

More generally, incorporating weather data into your forecast model facilitates agility.  While weather fluctuations are certainly not the only variables that need to be considered, they are a critical and often-overlooked piece of any successful forward-thinking, predictive solution.  When this information is utilized successfully, companies are better able to identify and adapt to external factors in real time, delivering a more seamless experience to their customers and mitigating potential losses as a result of supply chain hiccups. 

To learn how you can improve your company’s forecast accuracy, request a free demo with one of our business consultants today.




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