Companies using simulation to optimize their operations need to be able to account for variability in data. Businesses don’t operate in static environments, and their simulation models should be able to adapt accordingly. Even minor fluctuations can have a major impact on efficiency and prevent a production line, warehouse operation, or entire supply chain from reaching maximum profitability. 

Optimizers help businesses identify good decision paths when data is stable and the environment is static, but introducing change can quickly result in decreased performance and bad predictions. Suppliers deliver late, equipment breaks down suddenly, demand and prices fluctuate, and staffing availability varies – these are changes optimizers must respond to. Recalibrating an optimizer consumes time and resources with no guarantee that the best path forward will be found.

Since Pathmind uses reinforcement learning to determine the best strategies in a simulation, our AI policies can adapt to change and outperform optimizers in variable situations. Data fluctuations can be handled in less time and our AI agents can even predict when those fluctuations will occur. 

Rows of electricity meters

Areas such as resource and utility management, where costs can change frequently, can see great improvement with reinforcement learning and simulation. A simulation engineering team used Pathmind to show a large metals manufacturer how it could cut utility costs in just such a project. The manufacturer uses a process called electrolysis, which requires high energy consumption. They believed that adjusting their power usage could lead to major cost savings since their electricity prices are highly variable, sometimes doubling in an hour. 

Traditional mathematical solvers could not provide optimal decisions in the face of highly variable data. Pathmind’s reinforcement learning algorithm, on the other hand, were able to learn how to detect signals that prices would surge so that production could be decreased during those times. With Pathmind, the new decision model showed a 10% reduction in electricity spending. 

Read the full case study here.

Simulation models optimized with Pathmind AI can help your company adapt to change and remain efficient amid uncertainty and disruption. Optimization strategies able to adapt to the unknown can offer dramatically improved performance and more reliable planning.

Contact us to learn more.