Of all the external raw data sources now available to enterprises, weather is significantly the most important. For sectors such as agriculture and supply chain, in particular, it is a vital consideration, fed into their models to predict everything from potential traffic issues to likely surges in demand for certain food items.
In a recent survey of supply chain professionals by the UK Met Office, 47% cited weather as one of the top three factors external to their business that drives consumer demand, yet just 16% use commercial weather data. The results they achieve are persuasive, with 57% of those that use paid-for data saying they had better sales forecast accuracy, 51% that they had better on-shelf availability, and 43% that they had reduced waste.
Weather is notoriously hard to predict, and people have been using various primitive methods for centuries, from predicting rain by looking at whether cows are sitting down to determining low air pressure by a smell of compost in the air. Since the advent of computers and the internet, the technology and algorithms used to make accurate predictions have become increasingly sophisticated, and now benefit from a wealth of data picked up by millions of sources across the world. Last year, IBM acquired The Weather Company to make the best use of this data, applying cognitive technology to it for optimal forecasting.
Together, IBM and The Weather Company developed ‘Deep Thunder’, a hyper-local forecasting tool for business customers. Deep Thunder processes more than 100 terabytes of third-party weather and location data a day taken from hundreds of weather stations, combining this with real-time news information streamed from social feeds and news reports, as well as historical weather data. They then apply forecasting models and machine learning algorithms from its Watson program to the data sets to discover insights. These systems produce far more reliable weather forecasts, including the kind of location-specific information about impacts of storms, hurricanes and typhoons that is vital for supply chains to know.
IBM sends the information to its own supply chain managers, as well as enterprises which they can leverage to better anticipate extreme weather as soon as possible and put in place strategies to minimize the impact. Bad weather can disrupt supply chains globally on many levels. You only need to look at the havoc wreaked by a day of snow in the UK to see how devastating it could be, with the entire transport network often effectively grinding to a halt, not to mention the impact it has on demand when people stay indoors. Hot weather can cause spikes of 30-40% in demand for specific products overnight, and this needs to be anticipated and supplied for. Meanwhile, any unplanned downtime is extremely expensive for businesses and the economy. Heat waves and drought in Russia in 2010, for example, resulted in economic losses of $15 billion.
Climate change and increasing temperatures are now seen by experts as inevitable, but the size of their impact and actual implications remains uncertain. El Nino is also set to strike with a vengeance this year, meaning weather patterns will undoubtedly get even more unpredictable, and likely have an even greater impact on supply chains. Advanced weather data is a significant breakthrough in anticipating these, and presents a tremendous opportunity for organizations to improve supply chain efficiencies.