Enhancing Noisy Inventory Data Accuracy in Perishable Supply Chains Using a Fast and Robust Observer Algorithm

Reliable inventory data is an essential prerequisite for an effective management of supply chains. The purpose of this paper is to propose an efficient, cost-effective alternative to the widely used conventional methods based on high-cost sensor technologies. The proposed new approach relies on integrating a physical sensor with a custom-built estimation algorithm designed to derive accurate inventory data from inherently noisy sensor readings. The two key advantages are: an accurate, low-cost inventory estimation despite measurement noise, the capacity to regulate the convergence speed of the inventory estimation error toward zero.