John Galt Solutions reveals partnership with PredictHQ, signifying a pivotal moment for companies worldwide seeking to elevate their decision-making processes through the integration of PredictHQ's unparalleled forecast-grade event impact data and superior AI models with John Galt Solution’s industry-leading Atlas Planning Platform.
As the world grapples with an ever evolving and increasingly more complex marketplace, the need for accurate and real-time information has become paramount. This partnership provides John Galt Solutions’ customers the ability to incorporate verified, enriched and timely event data from PredictHQ and gain valuable business insights, enabling them to understand and predict how events, such as sports, concerts, weather, live TV events and more, impact their end-to-end supply chain directly within the Atlas Planning Platform.
"Predictive demand intelligence is instrumental in supporting businesses master predictability as they strive to accurately forecast, adapt planning, and navigate an increasingly dynamic global market,” states Campbell Brown, CEO at PredictHQ. “We’re thrilled about our partnership with industry leader John Galt Solutions. Together, we enable businesses to minimize demand volatility related to external events, reducing blind spots and creating greater resilience in their supply chains."
- John Galt Solutions’ Atlas Planning Platform connects and orchestrates the end-to-end supply chain to enable companies to make smarter, faster, and more confident supply chain decisions.
- By incorporating intelligent event data into the supply chain model, planners unlock situational awareness and gain more certainty and granularity enabling them to improve forecast accuracy, reduce inventory, fine-tune replenishment strategies and boost customer service levels.
- Deeper insights into factors driving customer demand, combined with a comprehensive view of market dynamics allow for a better assessment of the impact of local, regional and global event impacts for enhanced decision making.