# Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review
## 5 Findings
### Miscellaneous
However, the framework's real-world efficacy remains untested, as its validation is primarily through simulation. Subsequent research trajectories should contemplate the integration of diverse artificial intelligence methodologies to amplify operational robustness and employ performance indices such as stock out rate and shipping accuracy for a more granular evaluative framework. The study conducted by [PERSON] [94] investigates the implementation of a Smart Warehouse Management System (SWMS) based on the Internet of Things (IoT). The authors emphasize the advantages and benefits of incorporating sensors within the warehouse environment. The main benefits they point out are: control over the entry and exit of goods, increases warehouse efficiency, reduces the possibility of errors, decrease in labour and thus costs. The innovation introduced in this work is three-fold. The authors introduced a Smart Warehouse Management System, which is a significant deviation from traditional warehouse management models. They also integrated advanced sensor technology into their system. Finally, the system not only gathers data but also intelligently processes and controls the in/out of storage and cargo handling processes by leveraging internet and cloud computing technologies. This is a significant step forward in ensuring real-time data processing and decision-making in warehouses. However, the study lacks a detailed comparison with other modern systems and empirical validation of its advantages. Future research should delve into comparative analyses, explore integration with evolving technologies, and assess adoption barriers among enterprises. In a compelling case study exploring the utilization of Artificial Intelligence (AI) in e-commerce fulfilment, [PERSON] et al. [95] present their research on resource orchestration at Alibaba's Smart Warehouse. This paper offers valuable insights into the practical implementation and benefits of AI within a real-world warehouse setting, specifically focusing on Alibaba's operations. The conclusions reached by the authors are: (1) Data, AI algorithms, and robots are significant resources in developing AI capabilities. (2) Orchestration of AI resources and other related resources leads to the development of strong AI capabilities. (3) AI capabilities interact and co-evolve with human capabilities to create value. (4) Interactions and co-evolution involving AI and human workers depend on the type of task. (5) AI applications create business value in terms of efficiency (e.g., space optimization, labour productivity) and effectiveness (e.g., error reduction) by automating, extending, and transforming key business processes. The innovation of the research stands out for its resource orchestration perspective, pinpointing that for AI to truly shine, resources such as data, algorithms, and robots need to be orchestrated with other organizational elements. Notably, the study underscores the mutual co-evolution of AI capabilities with human skills, emphasizing their combined potential in enhancing business processes. However, the research is not without limitations. Its case-study nature confines it to specific context and thus may not be statistically generalizable across different industries or settings. The research exclusively zeroes in on certain business processes, omitting others such as goods receiving and outbound logistics. Additionally, its internal focus omits possible external influences on AI integration. Future research avenues are apparent from these limitations: a broader, multi-industry examination to attain statistical generalizability, exploration of AI in other warehousing processes, and a more expansive investigation that takes into account external macrolevel influences such as government policies or societal beliefs. This will pave the way for a holistic understanding of AI's integration across varying contexts and conditions. A paper presented by [PERSON] et al.