Companies are usually overloaded with data that they may not know how to take advantage of. On the other hand, artificial intelligence (AI) techniques are known to “keep learning” as the data increase. In this context, our research question emerges: what AI-based methods, in the literature, could be used to automatize business processes and support the decision-making processes of companies? To fill this gap, in this paper, we performed a review of the literature to identify these techniques. We ensured the usage of methods since they allowed reproducibility and extensions. We applied our search string in the Scopus and Web of Science databases and discovered 21 relevant papers pertaining to our question. In these papers, we identified methods that automated tasks and helped analysts make assertive decisions when designing, extending, or reengineering business processes. The authors applied diverse AI techniques, such as K-means, Bayesian networks, and swarm intelligence. Our analysis provides statistics about the techniques and problems being tackled and point to possible future directions.

Purpose: This paper aimed to identify the impact that Industry 4.0 can have on the service sector and how its combination can generate benefits for Sustainability 4.0 in the context of the Triple Bottom Line.Design/methodology/approach: A Systematic Literature Review (RSL) was developed using the following combinations of keywords on the Web of Science and Scopus databases: Industry 4.0 And Sustainability, Industry 4.0 And Service, and Industry 4.0 And Sustainable Development, in which 24 papers were selected.Findings: As a result, the authors found 100 benefits for sustainable development, which were further categorized into 54 advantages for the economic area, 25 for the social segment and 21 for environmental aspects. In this way, the results can help private companies understand the use of these technologies aimed at sustainable growth, plus bolstering the government to conduct public policies to encourage these practices in technology organizations.Practical implications: As a practical implication, this study offers insight into the evolution of sustainable development. It enables the emergence of works that wish to explore the service sector, providing relevant information for decision-making and influencing managers and policymakers on the importance of applying Industry 4.0 toward the sustainability of the service sector and its applications.Originality/value: The originality of this study lies in expanding the understanding generated by 4.0 technologies, by diverting attention from the manufacturing and agricultural sectors, which have abundant literature on the subject. With this, this work demonstrated that modern technologies have greater possibilities of an action directed at the service sector, in addition to being able to contribute to Sustainability 4.0 from the perspective of the sustainability tripod.

Purpose: This study analyses the factors that can influence the quality of service provided by the bus rapid transit (BRT) system in the state of Pernambuco. It is incipient, considering that the system was implemented in this state in 2014 to the World cup. In this context, the purpose of this paper is to identify the factors considered important for the services provided by this system and their impact on perceived quality. Design/methodology/approach: The authors analyzed the data using exploratory factorial analysis, and linear and quantile regression techniques. Regarding the classification, this research is applied, exploratory, descriptive and in the form of a case study describing the situation experienced by the users of BRT in Pernambuco. Findings: The results showed that it was possible to identify five factors that impact the level of satisfaction of the offered services: fluency in the provision of the service; staff training; physical conditions in the provision of the service; convenience/accessibility; and integration between transport lines. Originality/value: With initial identification, factors 1, 3, 4 and 5 have been found to have an impact on overall service quality in a positive and statistically significant way. Factors 3 and 4 have a different impact with high- and low-quality standard perceived by users, and these should be the guiding elements by companies in establishing actions to improve the services offered.

This paper puts forward a framework that assists managers to prepare action plans using attributes of Kano’s Model and to order them by utilizing multi-criteria decision analysis. The framework was applied in a company, a division of which provides a vehicle tracking service. Data were collected on its top 10 customers, all of which are large companies. The results show the proposed framework contributes to the decision-making process, since it enables the order of the actions to be rationalized, and takes the multiple criteria relevant to the company into consideration. Moreover, the fact that it is difficult to rank the 20 actions needed without the approach proposed in this article reinforces the need for and the importance of the framework and other complementary contributions in order to overcome this difficulty.