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The purpose of this study is to provide a robust model to bridge the influence of national culture (NC) on lean production (LP), identifying relationships among their elements and investigating the degree of influence.

The culturally sensitive lean production model (CSLPM) is used as a framework to develop a survey questionnaire that captures the information to analyze using partial least squares structural equation modeling (PLS-SEM). A case study to validate the model is presented from a subsidiary with operations in Mexico (MX) and the USA.

The CSLPM provides a robust framework for the measurement of the interaction between LP and NC. Multi-group analysis facilitated the comparison among the different groups (MX and USA), highlighting the differences of culture and the corresponding LP implementation within the same company.

The instrument was validated only within two countries and one organization. A higher sample of respondents, countries and organizations could validate the relationships established and the replication of the model.

The CSLPM can be used for assessment of the evolution of organizations, highlighting strengths and weaknesses of a particular culture when implementing LP, providing focal points for research and training efforts.

The CSLPM is a robust instrument to measure expectations for LP practices according to their company’s geographical context. The incorporation of higher order variables and mixed directionality within the variables presented a novel approach to PLS-SEM.

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.

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.

The purpose of this paper is to examine the digitalization of operational processes and activities in lean manufacturing firms and explore the associated learning implications through the lens of cumulative capability theory.

Adopting a multiple-case design, we examine four cases of digitalization initiatives within lean manufacturing firms. We collected data through semi-structured interviews and direct observations during site visits.

The study uncovers the development of learning capabilities as a result of integrating lean and digitalization. We find that digitalization in lean manufacturing firms contributes to the development of both routinized and evolutionary learning capabilities in a cumulative fashion.

The study adds nuance to the limited theoretical understanding of the integration of lean and digitalization by showing how it cumulatively develops the learning capabilities of lean manufacturing firms. As such, the study supports the robustness of cumulative capability theory. We further contribute to research by offering empirical support for the cumulative nature of learning.

The purpose of this paper is to understand how the association between leadership styles (task or relation orientation) and lean manufacturing (LM) implementation changes due to two contextual variables, team size and the leader’s age.

The authors carried out a survey with 225 leaders from different Brazilian companies that are implementing LM. Research constructs were validated through rigorous procedures using confirmatory factor analysis. Hypotheses were tested using ordinary linear least squares regression.

The results suggest that larger teams and more senior managers were negatively associated with LM implementation. Task-orientation style makes leaders more likely to achieve higher levels of LM than relation-orientation style leaders. Finally, the influence of relation-oriented leaders on LM implementation is contingent upon the size of the team.

Regarding study’s limitations, sample size and respondents’ location restrict results to this contextual condition, indicating that increasing the sample would help provide wider and more generalizable results. It is also worth noting that results are based on respondents’ (leaders) perspective. Hence, future studies may collect data from multiple perspectives, such as leaders and their followers, in order to compare results so as to verify the convergence or divergence among different respondents.

The results suggest that leaders should have different behaviors according to the context in which they are inserted. Therefore, such behavioral prescriptions are useful for managers since they are pressured to achieve high operational performance in short time periods and with few resources. Further, companies undergoing lean implementation may also be able to stimulate proper leadership behaviors and promote development programs accordingly, which is extremely relevant since behavioral changes usually take time.

The evolutionary process for achieving a successful lean enterprise requires different leadership styles according to the context in which leaders are inserted. This research provides arguments to help better understand the recommended leadership behaviors for lean implementation, complementing existing roadmaps by considering the proper leadership style as a contingency issue during lean implementation. Moreover, identifying the effect of contextual variables helps specify the contexts in which lean practices are more likely to be implemented.

This paper aims at identifying the main contributions of Industry 4.0 (I4.0) technologies to the enhancement of knowledge management (KM).

A scoping review based on two stages was carried out. In the first stage, a numerical and descriptive analysis was conducted. In the second stage, the latent content of those papers was explored through a qualitative analysis, in which papers were assessed regarding the outcome of the implementation of ten I4.0 technologies on 14 KM abilities and 6 KM processes.

Six technologies (e.g. Internet of Things (IoT), big data, cloud computing, simulation, visualisation and industrial robot) were claimed to have a significant contribution on KM, especially for creation, organisation, dissemination and application processes. Overall, results indicated that technologies can significantly contribute to KM, although the extent of such contribution varies across technologies, learning levels, KM processes and abilities.

Existing studies in I4.0 fall short in exploring how embodied knowledge is articulated in practice, especially regarding opportunities to use new technological alternatives in favour of knowledge and learning development. This study complements the existing literature on I4.0 by identifying which technologies can contribute to KM. Also, a framework was proposed to examine the contribution of I4.0 technologies to KM at individual, team, and organisational levels.

This paper aims to understand the patterns of lean production implementation, and the relationship between three context factors (i.e. firm size, positions within the supply chain and time length of the lean initiative) and the adoption of lean production practices in firms of the automotive supply chain in Brazil.

The authors collected data from 65 companies of the automotive supply chain in Brazil. For data analysis, first a cluster analysis was performed to identify common characteristics in the companies’ context factors when considering patterns of lean implementation. Then, multivariate analysis of variance was used to investigate the differences between the context factors and the degree of use of lean practices.

High lean adopters had better performance than low lean adopters in terms of lead time, inventory and turnover. Firms at the first and second tier of the automotive supply chain were “leaner” than firms at the third tier. Large-sized firms were more likely to have a higher degree of use of lean practices than medium and smaller ones. Some, but not all, lean practices followed these patterns. Results also showed that some lean practices were most commonly adopted at the beginning of the lean journey, whereas others took more time to mature.

This paper demonstrated how lean practices were implemented at different positions within the supply chain, and the patterns of implementation often followed. It also considers lean in the context of developing countries such as Brazil.