## 4 Manufacturing Process Case Study
### Simulation Models  
The aim of this study is to use simulation models, which, due to the application of the novel methodology, enable a reduction of complexity to the essential elements as a result are available to be developed in a systematic way, allowing a large number of different models to be calculated simultaneously in a short period of time. This creates a reliable prediction for the production controller, on which they can make reliable decisions. Thus, people are optimally supported in planning and controlling production, and, ultimately, the efficiency of production is increased [43]. The three scenarios can be differentiated by the following characteristics on the basis of the manufacturing process system modeling explained in Figure 1:  
1. Manufacturing process system modeling as explained in Figure 1: 1. Input-output process without controller or regulation: This characteristic implies that the model will not change its behavior depending on the system status or target KPIs. The logical and mathematical formulations that govern the transformation of the inputs into outputs of this model are defined and do not change over the simulation period. 2. First-order cybernetics: The process of controlling the system with an integrated control that changes the model behavior with a determined pattern, i.e., within a given framework or policy for transforming inputs into outputs, it can modify parameters to meet target objectives; however, it cannot adapt the controlling policies of the system behavior. 3. Second-order cybernetics: regulate the system behavior with an integrated control that can change its patterns based on the system status, targets, and self-optimization options, i.e., the model can change its policies in different areas such as maintenance, quality, etc., to adapt itself to achieve the intended objectives. 2. Maintenance policy: Describes the maintenance strategy applied in all production steps of the metallurgical case study: 1. Corrective: defines a model in which maintenance activities are performed when there is a breakdown that implies a production stop in the process. 2. Preventive: describes a model that realizes maintenance activities in fixed intervals to prevent breakdowns from occurring and having an impact on the system. 3. Predictive: a policy in which intervals depend on a forecast of maintenance needs and, therefore, generating activities in variable maintenance intervals depending on the prediction. 3. Quality control: reflects the consideration of product quality output and how the adjustments to achieve the target product quality are performed: 1. Without adjustments: independently of the quality output, the system does not change. * With adjustments: depending on the quality output, model parameters change with a fixed pattern. * Predictive adjustments based on self-regulation: depending on the quality output and prediction of quality rate, model parameters change. * Areas in focus: pursues to compare a classical approach in which the material flow was mainly the only flow affecting the manufacturing system and process availability versus an approach in which all flow areas related to the manufacturing system are considered and monitored:
* Only material flow area is in focus: implies that material is the only flow area with monitoring and action derived from its status. * All areas are in focus: this means that money, information, material, energy, personnel, and capital equipment will be monitored and treated with equal procedures. Based on the previous four characteristics, the following simulation models can be described as shown in Figure 6:  
* Classical simulation model: consists of 1a, 2a, 3a, and 4a;
* Integrated manufacturing simulation model: consists of 1b, 2b, 3b, and 4b;
* Integrated manufacturing simulation model with regulation: consists of 1c, 2c, 3c, and 4b.