John Galt Solutions has introduced an end-to-end probabilistic planning approach to its Atlas Planning Platform to provide a comprehensive model of real-world supply chain probabilities.
The solution is enhanced by AI-powered Q-learning, which offers a dynamic and recursive approach to planning and allows companies to assess value across multiple dimensions, factoring in uncertainty at each step of the decision-making process. Users can measure the value of uncertainty in resource utilization and timing, projecting the benefits and risks of each decision as costs or savings.
The Atlas Planning Platform evaluates alternatives using a mathematical approach, leveraging AI to provide a holistic range of probabilities for end-to-end planning. With a digital supply chain twin at its core, Atlas’ model continuously learns and reoptimizes in response to ongoing risks. Companies can adjust and align risk with their organization’s tolerance, goals and strategies.
