# Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review
## 5 Findings
### Miscellaneous  
The final subsection of this review encompasses relevant works whose findings and insights are applicable to the subject matter at hand. The research conducted by [PERSON] et al. [88] introduces an approach reliant on location data derived from Ultra Wideband (UWB) technology. This methodology is proposed as a means to enhance safety measures and operational efficiency within warehouse environments. The study's authors point to the benefits of indicating to an employee the specific location of where to perform an operation, leading to reduced errors and increased efficiency. Unlike Automated Storage and Retrieval Systems (ASRS), the authors of the study focus on solving the problem of the movement of all forklifts. The paper proposes a three-step framework, which consists of: (1) Selecting location calculation technology (UWB). (2) Integrating UWB with WMS and FFMS systems. (3) Conducting analysis and making appropriate changes. The authors focused on metrics such as brake severity, congestion identification, route policy involvement, driver behaviour at intersections, speed in zones, forklift accidents, and forklift errors. In a comparison of location accuracy, the authors found that cameras are the most accurate, but also the most expensive. UWBs are the second most accurate solution, and also one of the cheaper positioning methods. This work aligns well with the broader context of improving warehouse safety and operational efficiency. Furthermore, the authors' implementation of the proposed solution in a real-world warehouse setting enhances the relevance and applicability of their findings. The innovation that has been introduced was a novel three-phase framework. Unique insights are offered through heat maps and a data-refining algorithm, enriching warehouse decision-making processes. However, the research faces limitations, including noise interference from RTLS affecting precise location determination and the lack of real-time data analysis. Moreover, synchronization inconsistencies between different warehouse systems were identified. Moving forward, refining RTLS noise handling, focusing on real-time data interpretation, incorporating broader supply chain considerations, and aiming for an automated Industry 4.0-aligned decision support system are potential areas for future exploration. In their research, [PERSON] et al. [89] propose an advanced logistics monitoring system. This system, designed to enhance the efficiency and intelligence of logistics management, incorporates RFID sensor networks and leverages big data technologies. The framework system is built on a wireless sensor network platform and combines inbound and outbound logistics operations, warehouse positioning, and distribution monitoring management. The article tests the monitoring system in a real warehouse and shows that it has practical application value. Furthermore, the use of such a system can improve the level of informatization and intelligence of logistics management. The innovation introduced an innovative logistics monitoring system. Specifically, this system, rooted in a wireless \begin{table}
\begin{tabular}{l l} \hline \hline
**Evaluation Method** & **Papers** \\ \hline Simulation & [PERSON] et al. [79], [PERSON] et al. [81], [PERSON] et al. [83], [PERSON] et al. [84], \\  & [PERSON] et al. [85] \\ \hline Laboratory testing & [PERSON] [80] \\ \hline Real-world case & [PERSON] et al. [82], [PERSON] et al. [86], [PERSON] et al. [87] \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary of evaluation methods used in the papers on algorithm-based methods. sensor network platform, streamlines both inbound and outbound logistics operations, warehouse positioning, and distribution monitoring. When tested in real-world conditions, the system exhibited significant practical application potential and demonstrated its capability to elevate the informatization and intelligence quotient of logistics management.