Avaliação do grau de maturidade da indústria 4.0 em um Instituto Federal de Ensino Superior do Brasil

Abstract

Industry 4.0 that emerged in Europe in mid-2011 brought significant changes in industry, commerce and society in general, many of these changes are disruptive due to the emergence of new technologies. Cyberphysical Systems (CPS), Radio Frequency Identification (RFID), Internet of Things (IoT), big data, Artificial Intelligence (AI), systems analysis and cloud storage are tools, among others, that allow the emergence of a new era for the manufacturing industry. Companies and businesses that do not adapt quickly to this new environment will face serious difficulties to survive in the market. The main concepts, principles of industry 4.0 were addressed, in addition to the occurrence of this revolution in the main countries of the world. In this context, the present work brought maturity or readiness models that serve as parameters for evaluating an organization against Industry 4.0. The approached models were analyzed regarding their characteristics and elements established by important authors. Then, a comparative analysis was made between the models themselves in order to identify the main dimensions and elements contained in different maturity models. Henceforth, a maturity model was created that would allow the assessment of an IFES; for this, dimensions and parameters that were adherent to a public institution were adopted. The evaluation took place through a survey research, as well as the use of the Capability Maturity Model® Integration – CMMI tool. Therefore, a meta-analysis of the data was performed using Smart PLS software. In this sense, it was found that two dimensions reached the level 3/5 of the maturity level by the CMMI method, and three dimensions reached 2/5 of the maturity level. In evaluating the meta-analysis of the data, it was found that they reached a high level of adherence, reliability and free from bias. Finally, there was also a directly proportional relationship between the dimensions best evaluated in the CMMI method and the level of reliability of the data in the meta-analysis.