Deploying a self-learning AI methodology to trigger battery-powered stock-level sensors of an eKanban system

Abstract

Kanban systems are still the most popular method for pull control of material replenishment. In the Industry 4.0 era, the so-called eKanban represents a digital version of the classic Kanban. Thereby, in some eKanban variants, capturing actual stock-level by digital sensors would help or helps already to reduce capturing time and human failures. Currently, such digital sensors are not really beneficial because they are wired and expensive to install. In this paper, we investigate the potential of battery-powered stock-level sensors for eKanban systems using a simulation study. Battery- powered stock-level sensors are easier to install and, therefore, more beneficial than wired sensors if the battery´s lifetime is long enough. Because of the limited energy, it makes sense to trigger such sensor systems dynamically depending on the actual and predicted stock levels and on order points for minimum stock. For example, the sensor systems usually sleep and capture the stock-level only in a fixed interval for example every hour. Besides fixed intervals, it can be useful to trigger such sensors dynamically by reducing the activations in time with no or few material take-outs. To identify the best trigger strategy, this paper investigates how artificial intelligence (AI) methods, in this case, reinforcement learning, can be deployed. Thereby, the used material flow simulation maps the application scenario of a four-stage assembly line and compares the AI-based trigger with a manually operated demand notification. In this way, it can be shown that battery lifetime is increased fourteen times more compared to the manually operated demand notification, and battery runtimes of several months can be achieved by AI-based optimization of the sensor energy consumption. This approach leads to an eKanban solution becoming more economical, and thus smaller companies can also consider digitizing their material flow control