Counting inventory of non-countable products, such as materials cut from roles or spools, or taken from containers or tanks, can be time-consuming and disruptive to operations if wall-to-wall counting is conducted.

At the cut-order fulfillment center, we addressed this challenge by developing a machine learning model based on a probabilistic hidden Markov chain. This model, trained using reinforcement learning with an evolutionary algorithm, significantly reduced inventory errors by over 50%.