When Verizon set about overhauling its supply chain unit for mobile phones and accessories it used AI and machine learning to create a smarter, integrated, more automated system that could anticipate supply chain disruptions more quickly and allow Verizon to optimize inventory and customer experience. Prior to deploying OnePlanning many of Verizon’s supply chain processes were manual and transactional and plans often relied on a large amount of manual assumption and computation, making it difficult to quickly create accurate forecasts, especially given the size of Verizon’s business. Before the introduction of OnePlanning, Verizon’s supply chain was managed via an array of business processes and tools. Business teams had to grapple with decentralized data sources to gain a complete picture of available inventory, spending more time on handling data. They also lacked the right tools for simulation and scenario planning, leaving decision-making up to individual planners, who each possessed different knowledge, skills, and experience. The OnePlanning program, therefore, set out to remove silos and create an integrated system that gave all team members a single source of truth based on accurate data. Verizon took a multi-pronged approach to build an end-to-end, automated supply chain system that gives planners real-time visibility of accurate data and allows them to experiment with different scenarios and adjust outcomes. They transformed and automated business processes across the board, including the key areas of product lifecycle management, inventory planning, forecasting, and supplier integration.  OnePlanning has also dramatically altered Verizon’s ability to perform scenario planning. Planners can now tap into simulations of real-world scenarios and see in real-time how changes would impact demand, supply, or inventory and then use the information to make decisions and when dealing with partners. In addition to enabling automation, the new system draws advanced statistical modeling, predictive analytics , and machine learning algorithms to drive greater accuracy and improve the intelligence that planners have at their disposal.