Many organizations have redesigned their measurement systems to ensure that they reflect their current environment and strategies. Thus, it is extremely important that the responsible manager knows all the strengths and weaknesses of his organization, having all the maturity axes mapped, highlighting his strengths and weaknesses, to anticipate problems, becoming a company with greater potential competitiveness, because the failure is not to ignore the problem, but to ignore it. Given this, when measuring the Maturity Level Index, you can get an overview of the organization, becoming a radar to know the strengths and weaknesses, thus providing a basis for formulating a decision making and strategy to implement actions to improve performance and organizational maturity. The Acatech Industrie 4.0 (AI4MI) + AHP maturity index has the principle of providing companies with a guide for this transformation, based on the assessment of weaknesses or disagreements with the objective in the action plans, thus obtaining a continuous improvement in the evaluated stages, generating knowledge from the data, to transform the company into an agile organization, with quick decision making and adaptation in multiple business scenarios and different areas of the company. This article presents a preliminary discussion on the benefits of this proposed model for analyzing the measurement of the ACATECH + AHP Maturity Level Index, as to its advantages, results, added value.

The Industry 4.0 smart factories allow both optimization and integration of internal processes, utilizing the predictability of failure elements/components in a manufacturing process to prevent reprovals at the end of the process for quality control. The Supervised Machine Learning (ML) methods could be useful to detect anomalies and gain even more value throughout the entire supply chain. The ML approaches face barriers since it demands a changing in the production plant mindset to a more digital production and in the organization's structure for a more advanced data security. The paper aims to propose a smart inconsistency and fail prediction system for manufacturing systems of an automakers supplier assembly process based on the applications of ML techniques. The data provided for the training showed significant deviations and non-linearity allied to only 5 attributes as input variables, which is considered a small number of features for similar problems in the literature. The trained model was then applied to the assembly line with unobserved data of new products, with its result compared with similar previous productions. The results of the tests showed that the proposed stacking model lessens the possibility of rework in the next stages of assembly and creates a more precise process control for the supervisor. The implementation's results pointed out the potential of the stacking model proposed to be a useful tool in the context of Industry 4.0 since the reductions mean greater availability of production time and lower costs with quality control.

At a time when the competitive market is operating rapidly, manufacturing industries need to stay connected, have interchangeability and interoperability in their factories, ensuring that there is heterogeneous communication between sectors, people, machines and the client, challenging the manufacturing industry to discover new ways to bring new products or improve their manufacturing process. Precisely because of the need to adjust to these new market demands, factories pursue complex and quick decision-making systems. This work aims to propose applications of Machine Learning techniques to develop a decision-making platform applied to a manufacturing line reducing scrap. This goal will be achieved through a literature review in the fields of Artificial Intelligence (AI) and Machine Learning to identify core concepts for the development of a failure prediction system. This research has demonstrated the problems and challenges faced by manufacturing daily, and how, through the application of AI techniques, it is possible to contribute to assist in these problems by improving quality, performance, scrap rates and rework, through connectivity and integration of data and processes. This paper contributes to evaluate the performance of machine learning ensembles applied in a real smart manufacturing scenario of failure prediction.

Industry 4.0 has brought innovative principles to the entire world, especially for the manufacturing industry. The adaptation to a technological era showed limitations in the current processes, of which we can highlight the divergence between software and machinery technologies, cloud data processing, difficulty for the information to circulate within a manufacturing environment, so that it flows clearly and objectively, without ambiguity. These limitations end up generating errors between operations in the manufacturing process resulting in costs, customer dissatisfaction, low product quality, and reduced competitiveness. Thus, problems related to the semantic web, semantic interoperability, horizontal and vertical integration are responsible for such limitations in manufacturing processes. To resolve such restrictions and improve the final quality of the product, it is possible to apply Machine Learning techniques. Through the use of ensemble models of machine learning algorithm techniques, techniques with specific characteristics can be grouped, complementing each other, thus providing better prediction results during the manufacture of products, reducing costs, increasing the reliability and quality of the final product. In this way, it is expected to improve the final quality of the product and minimize the impacts that detract from the performance indicators, such as scrap, cost, rework, labor. This research will contribute scientifically to the creation of a system, which can be applied in different manufacturing production processes.