# The Role of AI in Warehouse Digital Twins: Literature Review +
## 4 Analysis Framework
### Digital Twin  
DTs are comprehensive digital representations of physical assets, comprising their design, configuration, state, and behavior [17]. A more grounded interpretation of the concept considers DT as the effortless data integration between a physical and virtual machine in either direction [18]. Instead of focusing on the definition of DT, ref. [18] made the distinction between what it is not and why, which led to the identification of the following levels of DTs:  
1. Digital Model: There is no automatic data exchange between the physical and digital worlds. Once the model is created, a change made to the physical object has no impact on it.
2. Digital Shadow: A digital shadow is a digital model with a one-way data flow from the physical to the digital objects. A change in the state of the physical object leads to a change in the digital representation.
3. Digital Twin: the data flow between the two counterparts is bidirectional. A change to the physical object automatically changes its virtual replica and vice versa.  
To further distinguish the maturity levels of the papers under study, an analysis of the characteristics of DTs detailed in the existing literature put the shortlisted article to the test. Ref. [19] described the characteristics of cognitive digital twins as being DT-based, cognition, full lifecycle management, autonomy capability, and continuous evolving.  
Alternatively, ref. [17] described digital twins as autonomous, context-aware, and adaptive virtual replicas. Considering the works mentioned above, relevant DT characteristics were identified for this study, reflecting a combination of the two visions above. Additionally, AI's potential integration and use in fulfilling these characteristics were investigated. The definition of each characteristic is provided below:  
* Context-awareness (CA) is the ability to distinguish incoming stimuli meaningfully. It encompasses more than just IoT and information systems (IS), extending to representing diverse situations in a virtual copy.
* Autonomy (Auto) is the DT's ability to function independently without human intervention. This capability empowers the system to take action and make decisions based on pre-determined rules or learned behaviors, streamlining the decision-making process without human assistance or a minimum level of human intervention.
* Continuous evolving (CE) is the ability of a DT system to grow and evolve with the real system throughout its lifecycle. DT systems should continuously update themselves based on changing data, information, and knowledge from the real system and all other interconnected software. This feature allows the DT system to adapt to new environmental conditions and changes, ensuring that it remains relevant and effective over time.
* Full lifecycle management (FLM) allows the model to cover different phases across the entire system lifecycle. FLM includes the beginning of life (BOL), such as design, building, and testing; the middle of life (MOL), such as operating, usage, and maintenance; and the end-of-life (EOL), such as disassembly, recycling, and remanufacturing. By addressing all lifecycle phases, FLM enables the DT system to be more sustainable, efficient, and effective over the long term.  
The characteristics of DT discussed above are considered essential requirements for a twinning paradigm, enabling higher levels of accuracy and a more realistic representation of the system. The specific levels and characteristics of DT may vary depending on the model's objectives and its intended application. Nonetheless, this literature review aims to assess how far these characteristics can be established and their interplay with data and artificial intelligence (AI).