# A Novel Methodology for Assessing and Modeling Manufacturing Processes
## 1 Introduction
Most authors agree that no one technique can model the functional, information, dynamic behavior, and decisions aspects of systems [11]. In this context, for modeling a manufacturing system, one needs to consider its characteristics. In manufacturing processes, personal capacity, material, and infrastructure are transformed into products [12] under consideration of costs, time, and resources [13]. A manufacturing process depends on several flows, and the process depends inevitably on these flows to operate [14]. In the 1960s, [PERSON] proposed a methodology called \"industrial dynamics\" for the modeling and simulation of dynamic models by studying the characteristics of the information feedback of industrial systems based on six types of flows: information, orders, materials, money, staff, and equipment [15]. Moreover, in many cases, production efficiency is described considering four resources: energy resources, material resources in terms of raw materials, auxiliary materials, and supplies, human resources as the required human labor, and financial resources as the operating costs and investments for operating equipment, infrastructure, and buildings [13]. In this regard, the first description of bottlenecks in a business context was done by [PERSON] (1976) [12]. He identified bottlenecks as the resource of a company that only can be utilized until a certain threshold. The resource with the lowest threshold limits the performance of the overall system. Moreover, the American Production and Inventory Control Society (APICS) defines a bottleneck as an asset, a function, a department, or a resource whose capacity is smaller than or equal to the faced demand [16]. These definitions can be applied to all company processes. Another modeling methodology is based on the input, control, mechanism, and output information concepts. With this model, it is then easy to identify the static status and elements with a product interface model (product representation) and a manufacturing data model (manufacturing representation) [17]. Furthermore, the elements of manufacturing systems can be also allocated to different hierarchical levels of the manufacturing systems, as well as structured levels (micro (single machines, work pieces, and processes), meso (process chains), and macro (factory)) for multilevel modeling, allowing interface and relationship analysis between factors [18]. In addition, to be able to control an industrial process, it is necessary to know the relationship between raw materials, process settings, and end-product results. Predictive modeling methodologies such as white-box modeling, black-box modeling, or hybrid models are providing efficient ways to study the complexities of manufacturing interactions with the simultaneous analysis of multiple factors and the development of in-built learning processes [19]. Other models are based on representing the process knowledge by means of elements understandable for any person without process or technology expertise. All the concepts are based on the description of situations that can occur during the execution of a manufacturing process. This model is based on the description of states that can be identified in a manufacturing process (situations), the task to be performed in each situation (actions), and the rules to determine the next situation after a task is executed (decisions) [20]. Furthermore, modeling based on the digital twin concept is limited, due to its novel nature with very few studies applying the use of a digital twin to production systems and manufacturing environments as they are mainly focused on highlighting its potential capabilities and benefits for the manufacturing production system, rather than analysis of the manufacturing process and integration with other dependable features such as production chains and material flows with a holistic approach based on real-time system information [21].