## 2 Literary Review and State of the Art
### Literary Review
In such a case, the researchers use a methodology that integrates natural language processing (NLP) and knowledge graphs (KGs) to improve interoperability between standards, identifying and resolving semantic conflicts (SICs). Within the research methodologies and conceptual macros, Reference [37] proposes a detailed conceptual framework that facilitates the transformation of smart production planning and control (SPPC) systems by integrating the classical functions of a production planner and control (PPC) with emerging technologies such as IoT, CPS and digital twins, but also provides a roadmap for the adoption of RAMI 4.0 axes. However, as we indicatedat the beginning of this section, the lack of concrete case studies or detailed practical implementations limits the immediate applicability of the framework in real-life situations. This is why, although the document offers a systematic and theoretically sound structure, it is not supported by comprehensive simulations or practical validations, indicating that its contribution is mostly conceptual. The authors of [38] created an additional layer based on RAMI 4.0, i.e., they added flexibility to an environment where batch customization and customer satisfaction are increasingly crucial for commercial success, thus managing customer outcomes in smart manufacturing. This was made possible through an IoT system that integrates with blockchain in an IOTA network for handling microtransactions. Instead of ensuring ubiquitous connection, they employ OPC UA; they mention the use of artificial intelligence algorithms and Business Process Management (BPM) for production optimization based on customer-expected outcomes. On the other hand, [PERSON] [39] mentions that there are intelligent systems where their processes operate independently, and therefore it is essential to manage them throughout their lifecycle by means of a system of systems (SoS). With this contribution, the researchers seek to improve interoperability and process optimization through a service-oriented architecture (SOA) for effective orchestration between multiple multi-stakeholder parties. Following this line on the integration of advanced technologies, Reference [40] studied the possibility of integrating Model-Based Engineering (MBE) with Industry 4.0, using Deep Learning and IIoT, with the situation managed by Recurrent Neural Networks (RNNs) on a Software Platform Embedded Systems (SPES XT), thus improving decision-making and task allocation and achieving improved efficiency and cost reduction in the RAMI 4.0. It should be noted that it is an innovative methodology and the study demonstrates the effectiveness of its proposal through simulations and theoretical models; however, it is not clear if an implementation and validation has been carried out in some kind of real industrial plants. The literature review identifies several challenges and proposed solutions that researchers report, such as the case of [41], which developed a specific methodology for SMEs that guides the implementation of referential models, finding different challenges such as the correct implementation of referential models in SMEs, the need for modernization of equipment to comply with communication standards, the lack of resources and technological maturity, the complex integration of new technologies with existing systems and the lack of standardized methodologies. To address these challenges, the researchers proposed a proof-of-concept (PoC)-based methodology to validate solutions at each layer of RAMI 4.0, using an iterative and flexible approach that allows for continuous adaptations. Reference [34] also states that, despite its comprehensive nature, the referential model faces challenges in practical implementation due to a lack of formalization and standardized methodologies. Efforts to address these challenges include the development of the reference model toolbox, which provides a step-by-step guide for model-based production systems engineering.