# The Role of AI in Warehouse Digital Twins: Literature Review +
## 3 Bibliometric Analysis  
Digital twin as a concept is tightly knit with all of industry 4.0 technology, primarily simulation, machine learning, hyperphysical systems, IoT, etc. Some research questions the novelty of digital twins, given how it is very comparable with simulation [20]. To counter that, ref. [8] presents an evolution of simulation to digital twins, showcasing how the two concepts could be distinguished. The authors mention that the first simulations, dating back to 1960, were for applications limited to very specific topics such as mechanical parts. Around 1985, computer simulation tools appeared to support the conception and design of parts. Simulation-based system design dates from the 2000s with the introduction of complementary disciplines such as model-based systems engineering. Simulation has enabled a systems approach for multi-level and disciplinary systems with a wide range of applications. Digital twins are often considered the second coming of simulation as they are built from the construction of a virtual environment emulating the physical system and constantly interacting with it. This is why DTs often require IoT to collect and transfer data to the virtual replica and then realize actions in the physical space. Given the duality of the technology from both the physical and cyber components, it is easily mistaken for cyber-physical systems. Figure 1: The search strategy used to capture the scientific literature. There seems to be no clear boundary between these terms, so a bibliometric analysis was performed to assess the chosen keywords. The study was done using VOSviewer 1.6.19, a software the University of Leiden developed to draw insights from the scientific literature. The bibliometric analysis followed a simplified methodology of that used to choose the final article sample (cf. Figure 1). At first, a general search using only \"digital twin\" was targeted in titles, abstracts, and keywords. The query was performed on 10 January 2023 during which no title nor abstract review was done as this could introduce a bias into the results due to the authors' influence. The bibliometric analysis focused on the keywords defined by the authors for all the papers resulting from the search query. The objective was to visualize and identify concepts related to DTs that might have needed to be included in the main literature review query. Secondly, two research queries were introduced to Scopus:  
* \"Digital Twin\" AND (\"warehouse\" OR \"warehousing\" OR \"material handling\" OR \"inventory\" OR \"packing\" OR \"store\" OR \"storage\"). * \"cyber\" AND\" physical\" and \"system\" AND (\"warehouse\" OR \"warehousing\" OR \"material handling\" OR \"inventory\" OR \"packing\" OR \"store\" OR \"storage\"). To represent the results, the network visualization from VOSviewer was employed. In such a network, the nodes represent the keywords, their sizes reflect the keyword importance determined by the number of occurrences, and the links between the nodes represent their co-occurrence. Furthermore, the relatedness between two terms is represented through their spatial distance in the network: two keywords closely related will be spatially closer. The networks are presented in Figures 2-4. The first network represents all concepts in the scientific literature related to DTs alone (Figure 2). The number of occurrences for the showcased items has been set to more than 100 times. The network is made of three primary cohesive clusters related to artificial intelligence in yellow; industry 4.0 and CPS in red, and simulation in green. Results from the bibliometric study suggest that DT as a concept is related to simulation and cyber-physical systems, with a stronger link towards simulation. Figure 2: Network visualization for “Digital Twin” alone. Results from the bibliometric study suggest that \"Simulation\" may not be a common keyword to find in cyber-physical systems in warehousing research.