# The Role of AI in Warehouse Digital Twins: Literature Review +
## 6 Discussion and Further Research Perspectives
By analyzing vast amounts of data, AI algorithms understand and model the behavior of the system, leading to the generation of various case specific scenarios for the decision-making process. In rapidly changing environments such as warehouses, it is imperative that the digital twin's structure permits continuous adaptation to avoid false predictions and incorrect assumptions, a point that has yet to be thoroughly explored in the literature. Keeping realistic expectations is crucial when discussing DTs and AI. Some ML algorithms are mostly black box models that we do not fully comprehend, making the technique untrustworthy. However, it can help us discover patterns we did not initially consider. Data-centric engineering, a field that leverages the best of physics, simulation, and data science, has been discussed by [20], which helps to ground AI and make it more predictable. Another potential research gap is continuous learning (CL) in a DT framework. CL represents the model's ability to continue evolving and regenerating from a data stream, achieving a degree of autonomy without requiring human intervention. However, the concept of autonomy is poorly covered in scientific literature. Autonomous systems pose more safety hazards in the workshop as we lose control over the actions and their timing. Another angle that is still uncharted when discussion both DTs and AI together is full lifecycle management. DTs have been associated with product lifecycle management since the very beginning. The first time the notion was ever explicitly used was by [PERSON] during a PLM lecture [18]. However, none of the papers from this literature review covered the entire lifecycle from a general perspective or focused on the end of life of a warehouse. None of the applications use artificial intelligence or simulation to model the beginning of the life of a warehouse, revamping and redesigning the building, or discuss what would become of the warehouse by the end of its life. IoT aside, warehouse operations can easily reach many records and data used for management or assurance purposes. Ref. [56] used machine learning models to predict warehouse component design based on data and metrics collected through the life of another storage system. The algorithms could be used to assess the effectiveness of the current, up-and-running warehouse to either duplicate or avoid making similar mistakes. This brings us back to the ultimate research question of all time: What came first, the digital twin or the physical system? If we consider digital twins as a simulation-based concept, then the making of the digital twin does not necessarily need the physical counterpart. It is irrelevant whether the real counterpart already exists in the physical world or is about to be constructed. Simulation will allow to test if the soon-to-be warehouse can handle different parameters such as the envisioned inventory, if resources are enough for ramp-up, test if we can get away with a traditional manual warehouse or if we ought to invest in a mechanical one... All the previous attributes are to be consolidated and evaluated, if not changed as both twins grow and evolve. Figure 6 showcases the warehouse twins' evolution through time. This model was inspired by [PERSON] et al. 's (2020) representation of the lifecycle of twins for building construction. The model ensures information saving and visibility in a structured and evolutive configuration. Our vision for WDT will chronologically follow the natural evolution detailed in Figure 6, from a digital model to a digital shadow, and lastly a twin. The concept is to be constructed based on simulation software allowing us to control the environment and run experiments in the first place, import datasets, and use machine learning-based optimization and forecasts, depending on the problem. In addition, depending on the problem, we need to define the degree of synchronization required for the optimization to make sense. This way, the \"digital shadow\" will be equipped with automatic data exchange and become a twin.