Resumo:
Discrete Event Simulation (DES) stands as one of the primary and most significant simulation techniques to assist decision-making in various areas of Industrial Engineering. Industrial enterprises of various sizes can significantly benefit from DES as it can assist in comprehending and analyzing systems, decision-making, improving operations and designing changes in the real system with less costly errors. However, despite a broad and growing literature on DES applications, it has been observed that industrial companies may encounter certain constraints in utilizing this technique in its traditional mode. These constraints include financial limitations that hinder the adoption of mechanisms for collecting extensive data, as well as the hiring of qualified personnel to process and explore their data. Additionally, data deficiencies, where limited or even unavailable data are collected, can lead to an inability to engage in simulation model development and scenario creation. There is also hesitancy in using DES due to the substantial time required for design and complexity in its use. Therefore, the objective of this thesis is to create a framework using facilitated modeling in conjunction with DES. In other words, proposing a simpler project management method. Facilitated DES offers advantages that address these aforementioned issues, as it allows for working with data estimated by experts in the process. It also advocates for the use of a simple computational model with few details, yet useful in generating understanding and fostering discussions about the problem situation, aiding in the pursuit of improvements. The FaMoSim (Facilitated Modeling Simulation) framework was developed following the steps of the Action Research method, and its implementation took the form of remote applications through hybrid meetings. With this meeting format, it is understood that the use of facilitated DES can be expanded beyond in-person meetings. Thus, FaMoSim brings unique features to the conduct of facilitated DES studies in industrial enterprises. By applying FaMoSim to four different case studies, its effectiveness in providing stakeholders with a better understanding of the studied processes using a simplified computational model with fewer data and fewer details was evident. It also assisted stakeholders in decision-making and identifying improvements.
