Purpose: Although prior studies have identified several technologies related to Industry 4.0 and their individual potential, it is still unclear how these technologies could be integrated to achieve better results. Based on this, we propose several collaborative networks combining technologies associated with Industry 4.0.Design/methodology/approach: A literature review was performed using a research model to support the evaluation and identification of key and collaborative technologies related to Industry 4.0. We examined these technologies using hierarchical cluster analysis and principal components analysis, based on their characteristics.Findings: The study identified big data, cloud computing, the internet of Things and cyber-physical systems as key technologies for Industry 4.0, and a further eight collaborative technologies that are strongly related to industrial performance. We found five collaborative networks with distinct goals in the context of Industry 4.0: (1) smart manufacturing; (2) technological platforms; (3) market reactiveness; (4) smart products and (5) flexibility.Practical implications: The findings allowed us to create five pathways for future work on Industry 4.0 technologies via collaborative networks. In practice, this will help managers to improve their focus on priorities regarding the implementation of Industry 4.0 technologies.Originality/value: This study provides insights into how to establish links between technologies through collaborative networks for certain purposes. In addition, we propose five future directions for these collaborative networks that require further investigation by researchers.

The purpose of this study is to identify how open innovation variables influence the competitive capability of manufacturing small and medium enterprises (SMEs) in a regional territory in Southern Brazil. The research method is a survey of 67 SMEs in Southern Brazil that provided data for an ordinary least squares (OLS) regression model encompassing seven innovation variables and five competitiveness constructs. The results indicate that most innovation initiatives have a low impact on creating a competitive edge in the surveyed companies. The most remarkable positive impacts are (i) technology trends influencing the shop floor productivity, (ii) flexibility influencing internal aspects, and (iii) customer satisfaction and innovative ideas and customized supplies influencing the market orientation. The study also reports that innovation processes are not safe from failures. Some ill-defined innovation initiatives may jeopardize the competitive edge.

Industry 4.0 and its technologies can potentially increase business competitiveness in the age of digital transformation through the implementation of its technologies. These digital technologies are increasingly present in the food industry, characterizing the concept of Agriculture 4.0. This digital transformation is a reality; however, it is unclear which digital technologies are most useful for each industry sector. Therefore, this paper seeks to explore the current state of implementation of digital technologies in different industrial sectors and which digital technologies should be leveraged to increase the performance of the agribusiness system. To do so, we used secondary data from a large-scale survey of 28 industrial sectors, representing 2225 companies in the Brazilian industry. Analyzing the different industrial sectors allowed us to present a framework of digital transformation to boost food industry competitiveness towards Agriculture 4.0. The results show that the food industry usually uses only one digital technology, showing the need for simultaneous and joint investments in the other technologies presented in this research. Public policies must be directed to encourage the expansion of digital technologies in the food industry.

This paper aims to present a competitiveness scale, outlining the variables that influence competitiveness in small and medium-sized enterprises (SMEs) in Southern Brazil. Using survey data from 72 industrial SME managers, the Multi-Attribute Utility Theory (MAUT) was applied to a model to measure Brazilian SMEs’ competitiveness rates. The variables that influence their competitiveness were identified and structured in a decision tree with three levels: key performance indicators (KPIs), critical success factors (CSFs), and fundamental points of view (FPVs). It was possible to identify the KPIs, CSFs, and FPVs that are more important for SMEs’ competitiveness and measure their competitiveness rates. The managerial decision-making processes in SMEs can be defined following the competitiveness standards in this research.