The purpose of this article is to provide an overview of academic research on professional success, using the bibliometric analysis to understand the evolution of this field between the years 1990 and 2020. The information was obtained from the publications indexed in the Scopus database, under a rigorous bibliometric process that comprises five parts: (i) criteria search of the field, (ii) selection of database and documents, (iii) inclusion and selection criteria, (iv) software and data selection, and (v) analysis and results. The results show professional success as a scientific discipline in full exponential growth, which allows us to consider the main contributions of authors, institutions, and international contributions, as well as to consider the main themes that have shaped the intellectual structure of the subject through their visualization using bibliometric maps of co-citation and co-occurrence, which combined showed eight main lines of research. The results obtained allowed us to identify patterns of convergence and divergence in various topics, which allows obtaining current and diverse information on the state of the research field’s art.

Considering the novel concept of Industry 5.0 model, where sustainability is aimed together with integration in the value chain and centrality of people in the production environment, this article focuses on a case where energy efficiency is achieved. The work presents a food industry case where a low-code AI platform was adopted to improve the efficiency and lower environmental footprint impact of its operations. The paper describes the adoption process of the solution integrated with an IIoT architecture that generates data to achieve process optimization. The case shows how a low-code AI platform can ease energy efficiency, considering people in the process, empowering them, and giving a central role in the improvement opportunity. The paper includes a conceptual framework on issues related to Industry 5.0 model, the food industry, IIoT, and machine learning. The adoption case’s relevancy is marked by how the business model looks to democratize artificial intelligence in industrial firms. The proposed model delivers value to ease traditional industries to obtain better operational results and contribute to a better use of resources. Finally, the work intends to go through opportunities that arise around artificial intelligence as a driver for new business and operating models considering the role of people in the process. By empowering industrial engineers with data driven solutions, organizations can ensure that their domain expertise can be applied to data insights to achieve better outcomes.

This article took the case of the adoption of a Machine Learning (ML) solution in a steel manufacturing process through a platform provided by a Canadian startup, Canvass Analytics. The content of the paper includes a study around the state of the art of AI/ML adoption in steel manufacturing industries to optimize processes. The work aimed to highlight the opportunities that bring new business models based on AI/ML to improve processes in traditional industries. Methodologically, bibliographic research in the Scopus database was performed to establish the conceptual framework and the state of the art in the steel industry, then the case was presented and analyzed, to finally evaluate the impact of the new business model on the operation of the steel mill. The results of the case highlighted the way the innovative business model, based on a No-Code/Low-Code solution, achieved results in less time than conventional approaches of analytics solutions, and the way it is possible to democratize artificial intelligence and machine learning in traditional industrial environments. This work was focused on opportunities that arise around new business models linked to AI. In addition, the study looked into the framework of the adoption of AI/ML in a traditional industrial environment toward a smart manufacturing approach. The contribution of this article was the proposal of an innovative methodology to put AI/ML in the hands of process operators. It aimed to show how it was possible to achieve better results in a less complex and time-consuming adoption process. The work also highlighted the need for an important quantity of data from the process to approach this kind of solution.

The advance of digitalization in industry is making possible that connected products and processes help people, industrial plants and equipment to be more productive and efficient, and the results for operative processes should impact throughout the economy and the environment.Connected products and processes generate data that is being seen as a key source of competitive advantage, and the management and processing of that data is generating new challenges in the industrial environment.The article to be presented looks into the framework of the adoption of Artificial Intelligence and Machine Learning and its integration with IIoT or IoT under industry 4.0, or smart manufacturing framework. This work is focused on the discussion around Artificial Intelligence/Machine Learning and IIoT/IoT as driver for Industrial Process optimization.The paper explore some related articles that were find relevant to start the discussion, and includes a bibliometric analysis of the key topics around Artificial Intelligence/Machine Learning as a value added solution for process optimization under Industry 4.0 or Smart Manufacturing paradigm.The main findings are related to the importance that the subject has acquired since 2013 in terms of published articles, and the complexity of the approach of the issue proposed by this work in the industrial environment.