The global shift toward digital commerce, accelerated by the COVID-19 pandemic, places immense pressure on logistics operations. Order-picking, the process of locating, collecting, and transporting items to fulfill customer orders, emerges as an area requiring optimization. This task accounts for over 55% of warehouse operational costs, making it a focal point for improving efficiency and reducing errors.
Our study addresses these challenges by developing a system that integrates augmented reality (AR) and the Internet of Things (IoT) into warehouse operations. By providing real-time guidance, seamless navigation, and intuitive interfaces, we aim to streamline the order picking process, reducing errors, operator fatigue, and operational costs. This system aligns with the broader goal of enhancing service levels for customers while maintaining adaptability for diverse warehouse environments.
Order-picking methods have evolved significantly, yet each approach presents limitations. Traditional systems like pick-by-paper are cost-effective but prone to errors, while digital advancements such as pick-by-display and pick-by-light improve accuracy but require high upfront investments. Pick-by-voice systems offer hands-free operation but struggle with noise interference in warehouse environments.
Emerging technologies, particularly pick-by-vision systems, employ AR to transform order picking processes. These systems utilize head-mounted displays to provide visual guidance for operators, enabling real-time item localization and hands-free operation. However, pick-by-vision systems face adoption barriers, including the lack of standardization in hardware and software, challenges in indoor positioning, and high implementation costs.
Our study focuses on addressing these limitations. Through modular design and the integration of AR and IoT, we create a system that adapts to various warehouse layouts and processes while enhancing operational efficiency and accuracy.
Study Details
Using Unity's AR Foundation, we design an intuitive interface that provides visual guidance to operators. This interface overlays digital instructions onto the physical environment, enabling operators to locate items and complete tasks with minimal cognitive load. Bluetooth Low Energy beacons help with real-time indoor positioning. These devices communicate with the AR system to triangulate operator locations, optimize routing, and enhance task accuracy. The system also incorporates ZXing libraries for fast and accurate barcode and QR code scanning. This capability ensures correct item identification and minimizes errors in the order picking process.
Our study focuses on optimizing the order-picking process in warehouses by incorporating augmented reality and Internet of Things technologies. The primary objectives are to reduce errors, enhance efficiency, and alleviate operator fatigue while ensuring the system’s adaptability to diverse warehouse layouts and processes. To meet these goals, we design a solution capable of integrating with existing Warehouse Management Systems (WMS) and delivering real-time guidance to operators.
Our system uses Microsoft HoloLens for AR visualization and Minew i10 BLE beacons for indoor localization. Unity's AR Foundation supports the AR interface, and a modular .NET 6 framework ensures robust WMS integration.
Operators report an average reduction of 30% in task completion time, thanks to the AR-based navigation system that provides real-time visual guidance and streamlined workflows. The BLE beacon network achieves an 80% accuracy rate in tracking operator positions. While effective, we identify opportunities to improve accuracy by optimizing beacon density and placement. The system’s real-time guidance and barcode scanning capabilities significantly reduce errors in item selection and retrieval, enhancing overall task reliability. Operators express a preference for the first AR interface prototype, citing its minimalist design and intuitive navigation features. While most operators note improved confidence and task precision, only half report a reduction in fatigue, likely due to the short duration of the testing period.
