Supply Chain Simulation Optimization
Supply chain management and optimization is a complex challenge in a world where unplanned events such as shifts in demand influence each stage of operations. Even with a digital twin in place, determining the best decision paths for performance can be a complicated pursuit. The amount of human interference required to explore what-if scenarios in these simulations can be a major time and resource sink without guaranteed results.
Using supply chain simulations along with Pathmind removes the need to invest hours of labor in trial-and-error testing since our AI does the experimenting for you. Pathmind reinforcement learning can be a game-changing tool across a supply chain, from materials management to customer service improvements. A policy generated by Pathmind is also better equipped to handle variance than more traditional optimizers and is a good match for complex supply chain models.
Plan for Supply Chain Disruptors
Mathematical optimizers can help determine good decisions for static environments, but supply chains operate in unpredictable circumstances. Equipment downtimes, resource shortages, and even global pandemics can make the already complicated task of supply chain management impossible. Being able to remain operational and profitable during these disruptive events can provide an edge over the competition. Pathmind policies outperform optimizers when changes are introduced into a model, making them the superior option for navigating the dynamics of supply chain management.
Find Solutions Unique to Your Needs
Every supply chain is different. Applying generic solutions and practices can be a waste of time and often do not allow a supply chain to operate at maximum efficiency. Pathmind overcomes this challenge completely by using AI to learn the problems unique to your supply chain model and generating a policy tailored to your individual requirements for success. Optimizing models with Pathmind means custom results that can be easily deployed into operations.
Supply Chain Use Cases
Supply Chain Optimization Model
This supply chain optimization model is inspired by a publicy-available AnyLogic model. It features three stages in a supply chain with the goal of figuring out the optimal inventory levels at each location to minimize holding costs while also maintaining enough items to keep customer wait times low.
An optimizaton experiment was completed and compared to the Pathmind results. The Pathmind policy outperformed the optimizer by more than 20%.
Stay Optimized Through Unpredictable Events
Reach out now to see why leading organizations trust Pathmind to get the most from their supply chain models.