Georgia-Pacific leverages Causal AI technology to refine its order management system, achieving a significant increase in touchless commerce and operational efficiency.
Causal AI: The Next Frontier in AI Technology
Artificial Intelligence (AI) continues to evolve, and Georgia-Pacific (GP) is at the forefront of adopting Causal AI—a sophisticated form of AI that deciphers complex cause-and-effect relationships within business operations. This technology is proving to be a game-changer in achieving seamless touchless commerce.
Georgia-Pacific’s Operational Transformation
GP, a leading manufacturer and distributor of consumer products, operates an extensive network with over 150 facilities and 30,000 employees. The company’s adoption of Causal AI has been instrumental in enhancing its order management process. Mike Carroll, VP at GP, emphasizes the need for a system that can handle the complexities of numerous individual orders with precision, identifying patterns and opportunities for automation in real-time.
The Challenge of Perfect Order Execution
Achieving a perfect order—delivering every order on time and in full—is a daunting task for businesses. GP’s integration of Causal AI into its IT systems has addressed the limitations of traditional enterprise resource planning (ERP) systems, particularly in available-to-promise (ATP) functionality. This has enabled GP to proactively identify potential issues in order fulfillment.
Causal AI in Action
Ron Norris, Director of Innovation at GP, explains that Causal AI can detect and rectify order errors swiftly, thanks to the input from order management experts. The system’s dual capability of Knowledge AI and Data AI allows it to analyze data, recognize patterns, and make informed decisions, a feat beyond the reach of conventional supply chain planning engines.
Fostering User Trust and Adoption
A critical aspect of implementing new technology is user acceptance. GP’s Causal AI system not only operates autonomously but also provides recommendations and rationales to employees, fostering trust and collaboration.
The Mechanics of Causal AI
Causal AI combines domain expertise with data analysis to create causal models that predict outcomes and recommend actions. These models are visualized through knowledge graphs, illustrating the depth of causality and the interplay of various factors.
GP’s Remarkable Results with Causal AI
GP’s implementation of Causal AI has led to a tenfold increase in touchless order processing and rapid resolution of order management issues. The technology’s benefits extend to transportation monitoring, automated replenishment, and improved demand forecast alignment with production plans.
Expanding Causal AI Across the Enterprise
While initially focused on order management, GP recognizes the potential of Causal AI to address complex business challenges across various enterprise areas, including sourcing. The company’s success in this domain suggests a promising future for Causal AI in streamlining business operations.
