# The Role of AI in Warehouse Digital Twins: Literature Review +
## 5 Results
### What AI Technics Are Most Used for Warehouse Management under the DT Paradigm?
#### 5.1.2 Data  
With the exception of [25; 35; 38; 45], all DTs and AI/ML algorithms were trained using, or claimed to be connected to, mainly real product data. These studies highlighted the risks of using simulated data, which could result in unexpected outcomes. For instance, RL algorithms may exploit virtual models to terminate inventory ordering closer to the end of the simulation, minimizing holding costs.  
Handler data are primarily used for online location tracking [33; 35; 43]. Operator-related data in general has been scarce and far removed from reality when modeling a DT of manual activities. This is probably because advances in integrating human factors into DT and simulation remain limited [47]. Ref. [48] discussed the integration of digital twins with industry 4.0, specifically from a human perspective. The paper highlights the lack of available solutions that address the human factor within DTs and proposes an approach that involves the use of data science and AI classification techniques in the form of a human skills modeling engine and a human scheduling engine to enhance digital twins of semi-automatic production lines by digitizing operator skills.  
Alternatively, environmental data, although heavily talked about in the literature, are not subject to much application and have yet to be widely applied. Temperature, as an environmental factor, plays an important role in safety monitoring [31; 34], and proper temperature monitoring and environmental control are critical for ensuring the safety and quality of food products in the food industry [49]. Ref. [27] suggests that all data types can be exploited for object identification through AI. DTs can develop auditory and visual signatures based on all kinds of data collected to identify changes in an environment and act accordingly.  
One common goal among all DT adaptations in the literature is to access information in real-time either through IoT or IS. Even if not applied in the specific studies, there is a consensus that WDTs must be connected to the IoT and IS primarily used to describe and manage the physical twin. IS, primarily through ERP or WMS, helps manage and optimize warehouse operations. It typically includes functionalities such as inventory management, order fulfillment, receiving and shipping of goods, and tracking of warehouse activities. IoT is widely used in the literature to access and condition resources, whether they are human or material. Ref. [46] demonstrated how combining digital twin technology, RFID technology, and spatial-operational multivariate simulation can optimize internal transport in a non-stacking warehouse. However, real-time monitoring is not always necessary, depending on the level of abstraction and the objective. More realistic, periodic, and synchronized data updates could be considered, which could be efficient, effective, and consume less energy and calculations for their applications. In other words, the level of detail and frequency of data updates should be tailored to the specific application and objective of the DT.