The Internet of Things (IoT) provides technical solutions for monitoring assets in facility layouts, and this is further strengthened by the development of sophisticated software tools for intralogistics operations. The present research provides a taxonomy for the existing tracking technologies and a comparison matrix for supporting decision making when selecting the most suitable technology for real-time tracking in indoor areas. Although numerous tracking technologies exist, ultra-wideband (UWB) technology has gained significant attention in recent years due to its exceptional positioning accuracy and its ability to operate effectively in challenging environments with numerous obstacles, even under non-line-of-sight (NLOS) conditions. Specifically, this research focuses on a real-time location system (RTLS) that is designed and implemented to monitor assets based on UWB technology. Additionally, a new algorithm is introduced to reduce localization errors by attempting to exclude NLOS measurements from the tag’s position calculations. The experiments showcased that the proposed algorithm improves the overall positioning error by 24%, reporting an RMSE of 0.124 m in comparison to the 0.163 m of the normal trilateration method. The experimental results highlight the efficiency of the proposed solution for fast and accurate localization and tracking in real-world environments.

Industry 4.0 has revolutionized the way companies manufacture, improve, and distribute their products through the use of new technologies, such as artificial intelligence, robotics, and machine learning. Autonomous Mobile Robots (AMRs), especially, have gained a lot of attention, supporting workers with daily industrial tasks and boosting overall performance by delivering vital information about the status of the production line. To this end, this work presents the novel Q-CONPASS system that aims to introduce AMRs in production lines with the ultimate goal of gathering important information that can assist in production and safety control. More specifically, the Q-CONPASS system is based on an AMR equipped with a plethora of machine learning algorithms that enable the vehicle to safely navigate in a dynamic industrial environment, avoiding humans, moving machines, and stationary objects while performing important tasks. These tasks include the identification of the following: (i) missing objects during product packaging and (ii) extreme skeletal poses of workers that can lead to musculoskeletal disorders. Finally, the Q-CONPASS system was validated in a real-life environment (i.e., the lift manufacturing industry), showcasing the importance of collecting and processing data in real-time to boost productivity and improve the well-being of workers.

Assessing the performance of intralogistics operations and real-time process optimization in manufacturing and warehouse facilities are crucial for promoting lean manufacturing in the fourth industrial revolution (Industry 4.0) context. Excessive travelling, out-of-schedule activities for picking, and inefficient routing are just shortfalls that should be optimized with the use of Information and Communication Technology (ICT) tools. The Internet of Things (IoT) provides technical solutions for monitoring the movement of assets at the facilities, and the development of sophisticated software tools enables the monitoring of intralogistics operations. This research contributes to the digital transformation of intralogistics operations and the interconnection with the physical world using ICT and Business Process Management tools. Specifically, the proposed framework analyzes how real-time locating systems can positively impact critical manufacturing flows: i) material; ii) process; and iii) information. Furthermore, the framework enhances transparency and sustainability. From the perspective of transparency, it interconnects the physical world and the processes of the company while focusing on the sustainability of the social, environmental, and economic dimensions. The social dimension is enhanced by creating a safer working environment for the employees while the environmental and economical dimensions are enhanced by minimizing the travelling distance of the moving vehicles and therefore the total energy consumption.