New IBM Weather Signals uses AI to enable predictive weather-based business forecasting

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IBM, and its subsidiary The Weather Company, today announced IBM Weather Signals, a new AI-based tool to help companies predict how fluctuations in weather will impact business performance, even months in advance. With this insight, businesses can proactively adjust supply chains to ensure accurate inventory, staffing and promotional activities aligned with anticipated changes in local weather conditions.

IBM Weather Signals uses Watson AI to merge weather data with a company’s operational data to create a model that predicts how anticipated seasonal weather conditions, or even minor fluctuations in temperature, wind chill or humidity, are expected to impact business performance, right down to sales of individual product categories at specific locations.

Integrating this insight into supply chains, companies can then redirect inventory to meet anticipated changes in demand – for example a fashion retailer can time the introduction of seasonal clothing lines to the start of the seasonal weather or a tourist attraction can anticipate how humidity might impact visitors’ willingness to stand in line and adjust staffing or pricing. These insights increase productivity and reduce waste to potentially add millions to the bottom line.

To help companies better integrate weather into their business planning process, IBM Weather Signals will be integrated with popular analytics platforms like Tableau. This provides interactive capabilities to model the correlation between weather and business performance within the context of overall business forecast planning, and within an intuitive dashboard environment, without having to migrate data to a new platform.

IBM Weather Signals has applications across a broad range of industries and is particularly relevant to industries that are sensitive to changes in daily or seasonal weather conditions such as retail, consumer packaged goods, services, hospitality, entertainment and travel and transportation.

IBM acquired The Weather Company in 2016 and has since been helping clients from travel and transportation, to logistics and supply chain, to better understand and mitigate the cost of weather on their businesses. IBM Weather Signals and IBM Watson Decision Platform for Agriculture are the latest innovations in IBM’s efforts to make weather a more predictable business consideration. The combination of rich weather forecast data and IBM’s AI and Cloud technologies provides a unique capability, which is being leveraged by agriculture, energy and utility companies, airlines, retailers and many others to make informed business decisions.

Case study

One World Observatory is one of the first IBM clients to pilot Weather Signals.

Millions of tourists flock to the Big Apple each year, with many choosing to take in New York City’s iconic skyline from One World Observatory (OWO) – but on days where atmospheric conditions provide great visibility, the number of visitors surges. OWO uses precision forecasting data from The Weather Company, an IBM Business, to help it fine-tune operations to give visitors the best experience.

“The results of the Weather Signals project were genuinely eye-opening,” said  Raymond Bianco, managing director of marketing at Legends. “We identified strong patterns in the datasets that not only validated our own estimates, but also provided brand-new insights […] Because we are so much more than just an observation deck, our ratio of personnel to potential guests is high—we have observatory ambassadors, tour guides, professional catering and wait staff, security and so on. We need to ensure that we always have the right number of staff on site to provide the best experience for visitors, so knowing how the weather will affect our guests’ choices is a crucial factor in our decision-making.”

In the past, OWO relied on publicly available meteorological data to help it plan work schedules, but it lacked predictive long-term weather insights. This not only made day-to-day operational planning difficult, it also increased variability in financial projections and business forecasts.