# A Novel Methodology for Assessing and Modeling Manufacturing Processes
## 1 Introduction  
Manufacturing currently plays a key role in the global economy [1]. The market for industrial goods is changing rapidly. Therefore, companies must face the challenges of the transition from mass production to mass customization, as well as low prices for products with shorter product lifecycles [2, 3]. Furthermore, turbulences in markets are no longer the exception, and they occur due to fast-changing technologies, environments, politics, societies, pandemics, and changes in the economy, such as those resulting from Industry 4.0, COVID-19, and global economic crises. Thus, the adaptability and changeability of production systems have become key features for manufacturing [3]. In this regard, Industry 4.0 pursues autonomous decision making, interoperability, agility, flexibility, efficiency, and cost reductions [4]. Moreover, in order to improve their competitiveness and abilityto make optimal decisions, companies need to effectively adapt to new market requirements by rethinking their production process with digital technologies [5]. In this context, with the rapid development of electric and electronic technology, information technology, and advanced manufacturing technology, production systems can be transformed from digital to intelligent [6], where an integration of products and machines in interconnected physical and digital processes combined with computing capabilities can be realized [7]. Therefore, in the future, it is to be expected that cyber-physical production systems (CPPS) will dominate manufacturing, providing new functionalities to the management processes, as well as to manufacturing and management systems supporting organizations in their decision-making capabilities [7]. As a result of this enormous potential, the smart factory concept is widely discussed [6] by academia, industry, and governments, and it has been widely applied in manufacturing due to its potential benefits to society, economy, energy, and environment, especially manufacturing [8]. However, the standards for smart factory implementation have not yet been established [6]. With feedback loops, CPPSs enable close interaction between the cyber and physical components through human-computer interaction interfaces [8]. The joint effect of decisions belonging to various production areas which are traditionally considered in isolation, such as logistics, maintenance, and quality, is difficult to predict [9]. For this purpose, digital twins (DTs) have been proven to be useful in supporting the evaluation and control of manufacturing entities as for supporting decision making together with predictive engineering leading to the capability to choose the best available option that optimizes target objectives for a given set of potential future scenarios [9]. Despite the great developments in technology over the last few decades, industrial processes present limitations in modeling and simulating manufacturing systems to assess process feasibility [10]. During the last two decades, several modeling methods and methodologies have been used for modeling, analyzing, and designing different aspects of manufacturing systems. Until recently, there has been no modeling method or methodology that can be used to model, analyze, and design complex manufacturing systems completely or support most phases of these complex systems [11]. There are many studies referring to models for the assessment of specific operation steps and their influence on the final product; however, there is still no model on how all areas and parameters of each single unit affect and interact within the whole manufacturing system [10]. Most modeling methods available are no more than static graphical representations and are not well defined. Thus, the models produced are open to misinterpretation and inconsistencies. Modeling needs a method that is simple and able to support different levels of abstraction. Analysts and designers need to model the basic manufacturing operations, as well as the relevant management decision and information systems.