# Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review
## 1 Introduction  
In a broader sense, a Warehouse Management System (WMS) can be defined as an activity that includes a series of measures including organisational, technical, and economic operations related to the storage of stock [1]. In a narrow view, a Warehouse Management System is a software solution that provides visibility into a company's entire inventory and manages supply chain fulfilment operations from the distribution centre to the store shelf [2]. For the sake of clarity, in this paper, we will consider the latter notion to be relevant as we continue to focus exclusively on software solutions. A modern warehouse is managed by software systems, intended to organize the work and control and administer a set of policies and processes. Nowadays, the most advanced WMSs automate processes by delegating labour-intensive and manual or dangerous tasks to robots, including goods inventory [3] and heavy cargo transfer and loads [4]. Economies around the world have driven various sectors such as agriculture, construction, healthcare, manufacturing, and retail to achieve highly efficient operations in order to increase output and meet consumer demand [5]. To meet these growing demands, logistics companies are constantly evolving to meet the challenges posed by fluctuating product markets and shipping schedules [6]. In 2022, OPEX was the leading vendor in the global warehouse management software market, with a market share of 29.38%, followed by SAP Warehouse Management (17.56%), SAP Extended Warehouse Management (13.37%), Manhattan (7.07%), and Oracle (4.54%), while the remaining share (28.08%) concerned other vendors [7]. Today's WMS solutions offer flexibility and scalability in both throughput and storage, making the most of vertical space to increase throughput and overall efficiency. Due to the significant impact ofCOVID on warehouses on a global scale, organisations are moving to cloud-based solutions, especially small and medium-sized enterprises [8]. Demand for warehouse management systems is expected to rise sharply as product manufacturers change their supply chain models and consumer demand soars. In fact, the global WMS market, valued at USD 3.1 billion in 2022, is expected to reach USD 4.1 billion by 2024 [9], growing at a compound annual growth rate of 16.0% from 2020 to 2024 [10]. In addition, as the WMS market continues to expand [11], with the growing trend towards warehouse automation [12], driven by customer expectations for improved effectiveness and efficiency [13], WMS vendors have offered new capabilities by adopting artificial intelligence solutions, in most cases by adapting existing machine learning techniques [14]. One of the most significant behaviour modelling issues is concerned with motion trajectory prediction [15; 16]. The ability to accurately forecast the future positions of humans, objects, or agents in a dynamic environment is an essential component of numerous advanced technologies such as autonomous vehicles [17], service robots [18], surveillance systems [19], and wearable systems [20]. A particularly important application of motion trajectory prediction lies in ensuring the safety and efficiency of the employees, who simultaneously cooperate with the autonomous devices (e.g., self-driving robots) and dynamically share the common working environments [21]. Among many business and industrial domains, these issues are related to buildings (warehouses), used to store finished goods and raw materials, vital in the supply chain [22]. To sum up, by adequately addressing the issue of Motion Trajectory Prediction (MTP), warehouse operations can be optimized and potential hazards can be mitigated effectively. However, to the best of our knowledge, the current body of literature related to MTP methods seems to be scattered and fragmented. This study seeks to fill this research gap by using the systematic literature review approach.