# A Novel Methodology for Assessing and Modeling Manufacturing Processes
## 1 Introduction
Regulation and control of many different process parameters using digital twins is a future research need [22] as it supports the control, optimization, and security level of manufacturing operations by reducing the variability of products thanks, for instance, to closed-loop response, blockchain, and expert systems [23]. In summary, the scientific community has proposed many manufacturing analyses based on different parameters; however, an integrated model is not provided [17]. In this context, several models have been developed to conceptualize manufacturing processes in a generic form. The goals of these models were always to secure process stability, thereby enabling the production system to reach its goals. Although new technologies are available, their integration with existing manufacturing systems is a challenging issue for developing real applications [24]. Despite the described evolution of industrial processes, the management and control of manufacturing systems have not developed the existing manufacturing models in order to consider all relevant factors in the fourth industrial revolution and the influence of key indicators of manufacturing quality and performance. Therefore, the new elements, relevant factors, and systems of the Third and Fourth Industrial Revolutions are not fully integrated in a model to provide a framework that allows for the management and control of a manufacturing processes in both present-day and future manufacturing systems. Today, the control of manufacturing processes is a challenge for researchers and practitioners; however, many models for the manufacturing process and quality control are based on hardware quantity and quality, without paying attention to information flows, money flows, and energy flows, which have a significant influence on key indicators but are not integrated in a manufacturing model. Areas, parameters, and their study are key for successful planning and control, as well as for the optimization of target indicators. However, researchers often fail to holistically consider all the related factorsand implications of a process within an integrated model. Thus, the aim of this research was to propose a holistic approach for manufacturing processes to assess the status of a manufacturing system, as well as the impact of changes on target indicators. This ensures continuous optimization along the machine lifecycle, providing a USP (unique selling proposition) for equipment producers, service engineering suppliers, and production and assembly companies. In conclusion, the proposed approach enables companies to increase their profitability and customer service level. Thus, we need to model self-organized manufacturing systems, deduce their dynamic behavior, and develop appropriate control methods. However, the theories on self-organized systems are not mature, and the complex system research remains a hot topic [25]. To solve these issues, there is a need for an integrated manufacturing system that can address all interactions and interrelationships [26]. This lack of integration imposes a limitation for developing manufacturing digital twins [10]. Integration is important due to the necessity of quick responses to customer expectations, and it should connect components, enabling their communication [26]. Digital twins can play a key role in combining the related physical and virtual entities into an efficient cyber-physical production system [27]. The novel methodological approach pursues to serve as a basis framework to develop digital twin models for improving the overall performance in manufacturing systems together with simulation techniques, data analysis, and artificial intelligence, as well as machine learning and blockchain technology. The digital twin concept applied to manufacturing systems can enable real-time monitoring and control, higher efficiency and safety, the effective use of predictive maintenance, and the simulation of scenarios and risk assessment for better decision making [27].