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This paper aims to examine the moderating effect of Industry 4.0 (I4.0) technologies on the relationship between socio-technical (ST) practices and workers' health, quality and productivity performance.

In this paper, 192 practitioners from different manufacturing firms adopting I4.0 technologies were surveyed, analyzed the collected data using multivariate techniques and discussed the results in light of ST theory.

Findings indicate that I4.0 moderates the relationship between ST practices and performance, to an extent and direction that varied according to the focus of the technologies and practices adopted.

The I4.0 movement has triggered changes in the work organization at unprecedented rates, impacting firms' social and technical aspects. This study bridges a gap in the literature concerning the integration of I4.0 technologies into manufacturing firms adopting ST practices, enabling the verification of the moderating effects on workers' performance. Although previous studies have investigated that relationship, the moderating effect of I4.0 on performance is still underexplored, characterizing an important contribution of this research.

The purpose of this paper is to examine the moderating role of Industry 4.0 technologies on the relationship between lean production (LP) and operational performance improvement within Brazil, a developing economy context.

One representative from each of the 147 studied manufacturing companies filled in a survey on three internally related lean practice bundles and two Industry 4.0 technology bundles, with safety, delivery, quality, productivity and inventory as performance indicators. As this study was grounded on the contingency theory, multivariate data analyses were performed, controlling for four contingencies.

Industry 4.0 moderates the effect of LP practices on operational performance improvement, but in different directions. Process-related technologies negatively moderate the effect of low setup practices on performance, whereas product/service-related technologies positively moderate the effect of flow practices on performance.

With the advent of Industry 4.0, companies have been channelling their efforts to achieve superior performance by advancing levels of automation and interconnectivity. Eventually, widespread and proven manufacturing approaches, like LP, will integrate such technologies which may, in turn, impair or favour operational performance. Contrary to previous studies, the contingencies appeared to have a less extensive effect. The authors point to various options for further study across different socio-economic contexts. This study evidenced that purely technological adoption will not lead to distinguished results. LP practices help in the installation of organisational habits and mindsets that favour systemic process improvements, supporting the design and control of manufacturers’ operations management towards the fourth industrial revolution era.

This study aims to investigate the impact of traditional teaching and active learning methods in lean management (LM) on the development of both soft and hard skills.

Through a longitudinal study, team members from two different organisations (the administrative sector of a public higher education institution and a public teaching hospital), each adopting different teaching methods to support their LM trainings, were systematically examined at four moments during an 18-month period. How teaching methods impacted team members’ development and knowledge was then assessed using multivariate data analysis techniques.

Results indicated that LM trainings can provide significant impacts when a combination of traditional teaching methods and active learning is adopted. Traditional teaching methods can be a good choice for learning hard skills depending on resources’ availability. However, it is recommended to include active learning methods to assist in the comprehension of more complex and abstract LM concepts (soft skills).

Although there exists a large number of publications on the relationship between LM implementation and teaching methods, the number of studies that consider the development of both hard and soft skills is rather limited. This study complements the existing literature on LM by identifying which teaching methods can support the development of hard skills and which the development of soft skills. Such identification facilitates the work of both scholars wishing either to begin or to dig deeper into this sphere and practitioners pursuing the best outcomes from LM.

Healthcare supply chains (HSCs) have been adopting Industry 4.0 (I4.0) as a means to boost their resilience. The first objective of this study is to identify the effect of contextual variables of HSCs on resilience development and I4.0 adoption. Second, the paper examines the pervasiveness of the relationship between resilience and I4.0 across different contextual characteristics.

179 organizations from the HSC in Brazil and India were surveyed. Responses were analyzed using multivariate data techniques.

Large HSC agents are more likely to develop resilience abilities and adopt I4.0 technologies when these factors are analyzed independently. However, the joint analysis of resilience and I4.0 displayed a large number of significant correlations among small organizations.

Findings provide managers of HSC arguments to enhance resilience through the digitalization. HSC organizations can identify HSC organizations' context to tailor initiatives on resilience and digitalization.

In this paper, the authors examine the impact of Industry 4.0 (I4.0) technologies on the relationship between total productive maintenance (TPM) practices and maintenance performance.

Data collection was carried out through a multinational survey with 318 respondents from different manufacturing companies located in 15 countries. Multivariate data techniques were applied to analyze the collected data. Diffusion of innovations theory (DIT) was the adopted theoretical lens for our research.

The authors’ findings indicate that I4.0 technologies that aim to process information to support decision-making and action-taking directly affect maintenance performance. Technologies oriented to sensing and communicating data among machines, people, and products seem to moderate the relationship between TPM practices and maintenance performance. However, the extent of such moderation varies according to the practices involved, sometimes leading to negative effects.

With the advances of I4.0, there is an expectation that several maintenance practices and performance may be affected. Our study provides empirical evidence of these relationships, unveiling the role of I4.0 for maintenance performance improvement.

The objective of this research is twofold. First, we aim at identifying the pairwise relationships between Lean Production (LP) practices and Industry 4.0 (I4.0) technologies. Second, based on these results, we propose a framework for Lean Automation (LA) implementation, in which I4.0 technologies are integrated into LP practices.

To achieve that, a cross-sector survey was performed with 147 manufacturers that are undergoing a LP implementation aided by novel information and communication technologies from I4.0. Multivariate data analysis was applied in order to underpin the proposed framework for LA.

Findings indicate that I4.0 technologies are positively correlated with LP practices, providing evidence to bear the proposition of a LA framework that can potentially overcome traditional barriers and challenges of a LP implementation.

As previous studies have approached LA implementation from a narrow perspective or including a limited set of LP practices and I4.0 technologies, the proposition of an integrated framework unfolds a wider range of synergistic implementations that may corroborate to a holistic approach for continuous improvement in the Fourth Industrial Revolution era.

The purpose of this paper is to classify the main barriers related to the implementation of lean supply chain management (LSCM) and prioritize its main practices. A case study approach was conducted to obtain values for the intensity of the relationship between LSCM practices and barriers to mitigate or even anticipate difficulties in its implementation.

The case study in a company in southern Brazil that is in lean implementation is used for conducting this research. The methodology was structured in three steps, namely, classification of the main barriers through risk analysis, selection of LSCM practices appropriate to the company context and ranking and prioritization of the main practices of LSCM related to the barriers to lean implementation.

As a result, with the combination of two approaches, failure mode and effect analysis (FMEA) and analytical hierarchical process (AHP), it was possible to obtain values for the intensity of the relationship between LSCM practices and barriers. From the 12 barriers, 5 reached a high-risk degree that can be mitigated, and among the 18 practices found, 7 have the potential to be implemented.

This research allows direct efforts to continuously improve the supply chain to mitigate or even anticipate difficulties in its implementation. The proposed methodology, combining FMEA and AHP is easy to apply and understand, allowing managers and professionals to replicate it in the context of their supply chain and verify results similar and comparable to those obtained here.

This study seeks to provide a conceptual framework for the classification and selection of Healthcare 4.0 (H4.0) digital technologies affecting healthcare processes.

By examining the literature review, a set of processes of health services based on two axes of interaction and service customization and the axis of labor intensity of the service process matrix was divided into four categories: service factory, mass service, service shop and professional services. Then, using a combination of grey decision-making trial and evaluation laboratory (DEMATEL) and grey weighted aggregates sum product assessment (WASPAS) methods, a framework was presented to compute the impact of each of the H4.0 digital technologies on sub-criteria of the two main axes. Finally, based on the degree of the impact of each technology on the main axes, the technology affecting the four processes was segmented.

Findings show that the customer participation in the service process (C₁), ways to provide customer service (C₆) as well as the speed of service delivery (L₄) are the most important in the classification of digital technologies affecting healthcare processes.

Various other indicators from the behavioral, cultural, political, social and economic fields can be examined and used as a basis for evaluating H4.0 digital technologies.

The proposed framework can help managers select H4.0 digital technologies to prioritize, review and analyze appropriate technologies to improve and support different processes, prioritize appropriate technologies and review and analyze.

So far, no study has examined the link between digital technologies and various service processes. Therefore, this reinforces the originality and value of the present study.

This article identifies the association between two methods for teaching lean manufacturing (LM): problem-based learning (PBL) and classroom lectures, and students' learning styles of a postgraduate course.

Data were collected from graduate students LM courses that present different teaching approaches. Thus, students' learning preferences were gathered through the application of the Index of Learning Style questionnaire, and their performance assessed after each course.

Results indicate that learning styles are indeed associated with LM teaching approaches, and comprehending interaction effects between learning style dimensions is essential for properly adapting the teaching method. However, these interactions have different extensions.

Although teaching LM has significantly evolved over the past decades, the single application of traditional teaching methods jeopardizes learning effectiveness of graduate students because of the practical nature of LM. This study provides evidence to better understand the effect of complementary teaching methods and their relationship with students' preferences, empirically examining that there is not one best approach for understanding LM.

This paper aims at examining how different types of shop floor performance feedback affect employee motivation and engagement. Based on this dataset, the authors analyzed the relationship between five types of performance feedback (safety, quality, productivity, improvement and individual performance) and three types of human-related outputs (motivation, role clarity and engagement) in light of socio-technical systems theory.

The authors designed a survey instrument and collected data from 492 employees of a large beverage distribution company. The authors used robust construct validity tests and multiple regression analysis to test the hypotheses.

The findings indicated significant positive effects of feedback on overall production, improvements and individual performance on all human-related outputs. In turn, safety and quality performance feedback had no statistically significant impact in motivation or engagement. These findings highlight the mixed nature of the impact of performance feedback on human-related outputs that have been neglected in the literature.

The joint analysis of the social and technical portion of performance feedback is unusual despite its clear relevance, which characterizes an original contribution of the authors’ work. Although previous literature supports the effect of performance feedback on motivation and engagement, previous studies have not tested how different types of feedback affect those social constructs.