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This paper aims to propose a learning evaluation model for Green Belts and Black Belts at the training level. A question bank has been developed on the basis of Bloom’s learning classification and applied to a group of employees who were being trained in Six Sigma (SS). Their results were then used to decide on the students’ approval and to guide the instructor’s plan of teaching for the next classes.

An action research has been conducted to develop a question bank of 310 questions based on the revised Bloom’s Taxonomy, to implement the evaluation model, and to apply it during the SS training.

The evaluation model has been designed so that the students do not proceed unless they have acquired the conceptual knowledge at each step of the DMAIC (Define, Measure, Analyze, Improve and Control) roadmap. At the end of the evaluation process, the students’ results have been analyzed. The number of mistakes in all stages of DMAIC was equal, implying that the training was uniform the entire roadmap. However, the opposite happened in each of the Bloom’s Taxonomy levels, showing that some skills need to be better stimulated by the instructor than others.

The learning evaluation model proposed in this paper has been applied to a group of 70 employees who were being trained in SS at a Brazilian aircraft manufacturer. The data have been analyzed using Microsoft Excel^® and Minitab^® 17 Statistical Software.

Despite the abundance of courses offering the SS Green Belt and Black Belt certifications, there is no standard evaluation to ensure the training quality. Thus, this paper proposes an innovative learning evaluation model.

The purpose of this paper is to examine the mediating effect of learning organization dimensions on the relationship between the implementation of total quality management practices and companies’ operational performance improvement.

The authors carried out a cross-sector survey with 135 Brazilian manufacturing companies that have been implementing total quality management as an organizational strategy for at least five years. Collected data were analyzed using multivariate data analysis techniques.

The findings provide guidelines for manufacturers to increase their learning capability by reinforcing the implementation of total quality management practices, whose synergistic effects may be currently neglected. Results show that an enhanced organizational learning capability can significantly impact the improvement level of operational performance through the application of total quality management practices.

Several authors have investigated the relationship between total quality management implementation and learning organization aspects. However, most studies examined their relationship from a narrow perspective or under specific contexts, lacking empirical validation of their concurrent effect on operational performance improvement. The study aims at bridging this gap.

The purpose of this paper is to investigate the effect of practitioners’ experience and generational differences on the adoption level of lean production (LP) principles in their daily decisions and behaviors.

The authors carried out a cross-sector survey with 135 practitioners from different Brazilian manufacturers that have been implementing LP for at least five years. Collected data were analyzed based on multivariate techniques.

The results show how different combinations between practitioners’ LP experience and generational characteristics may entail a higher likelihood of adopting LP principles. While members from generation X do not seem to be associated with the adoption of LP principles, the ones from generations Y and Z are differently related with LP based upon their experience levels.

The understanding of individual differences for adopting LP principles allows the establishment of proper expectations with respect to each practitioner’s openness to change. Studies that address the adoption level of LP principles based on certain individual characteristics, such as practitioners’ experience with lean implementation and their generational values and beliefs, are scarce in the literature.

This study aims to examine the impact of practitioners’ generational differences on the development of a learning organization (LO) in companies undergoing a lean production (LP) implementation.

The authors gathered information from 135 leaders from different manufacturers that have been implementing LP and analyzed the data set through multivariate data techniques.

The results indicate the practitioners from Generations X, Y and Z contribute differently to LO capabilities even if their companies present similar maturities in terms of LP implementation.

Understanding how generational differences interact with LP implementation to allow an extensive development of an LO is fundamental for companies, as values, beliefs and working characteristics of the upcoming generations may significantly influence the effectiveness of current management approaches and firms’ competitiveness.

The purpose of this paper is to report the first practical applications of FCAUP (Framework for Conformity Assessment inspired by the Uncertainty Principle) carried out using a pilot implementation in a boat trailer manufacturer company in Maryville, Tennessee, USA and a practical application in a metrology company in São José dos Campos, São Paulo, Brazil.

Field studies have been conducted in two different companies: a pilot implementation during the preparation activities of the acceptance inspection of finished products and a practical application during the internal audit related to the quality management system.

(1) partial double inspection is advantageous in conformity assessment; (2) a structured procedure tends to reduce the risks arising from the use of partial double inspection; (3) the recommended amount of double inspections is directly proportional to the uncertainty involved in the process and it is inversely proportional to the degree of trust in a presumed conformity status.

This novel framework is for practitioners in manufacturing industries and test laboratories.

The results of these practical applications suggest that FCAUP is a consistent approach to be used in several categories of conformity assessment in manufacturing industries and test laboratories due to the balance between result and time (cost).

This new framework, named FCAUP, for planning and executing conformity assessment activities has been introduced in a theoretical study published by Moraes and Silva (2018) and it is based on an analogy with the Uncertainty Principle of Quantum Mechanics.

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 empirically examine the relationship between the behavioral orientation of leaders from different hierarchical levels and the implementation phase of the lean manufacturing (LM) roadmap. In addition, it also aims at analyzing the influence of inherent contextual variables related to leadership.

The proposed method relies on both qualitative and quantitative approaches, since it combines and numerically assesses the results of semi-structured interviews with lean experts, in addition to evaluating a cross-sectional survey with 225 leaders from different Brazilian companies that are undergoing LM implementation.

The findings support the existence of a transient leadership style along the LM implementation, which is evidenced in terms of task and relation orientation and may vary according to the hierarchical level. Furthermore, the context does matter with regard to leadership style orientation along the LM roadmap phases, although not all variables matter to the same extent.

LM implementation is about changing both technical and socio-cultural aspects, which can be seen as the essence of leadership. The relevant question that arises is whether leaders’ behaviors from different hierarchical levels should be adapted according to the maturity of LM implementation and the contextual variables that surround leadership.

The purpose of this paper is to investigate the reliability analysis of a multi-state manufacturing system with different performance levels. In, fact, reliability assessment of manufacturing systems gives a reasonable demonstration of system performance.

This research utilizes a multi-state system reliability analysis to develop a new metric for evaluating production systems.

The proposed model provides a sensible measure to assess the system situation against the best-case scenario of a production line.

The proposed model incorporates not only failures that stop production but also deals with partial failures where the system continues to operate at reduced performance rates. The analyses are represented in a best-case vs worst-case situation. Each of these cases provides insight for managers with respect to planning operation and maintenance activities.

The purpose of this paper is to propose a contemporary reliability model for lean systems through the development of an innovative lean system reliability model (LSRM).

LSRM models the reliability of lean subsystems as a basis for determining the reliability of lean systems as a whole. Lean subsystems, in turn, consist of reliability measures for lean components. Once principal components analysis techniques are employed to determine critical subsystems, Monte Carlo simulations for lean components, subsystems, and the overall lean system are then compared with historical data to determine the adequacy of the LSRM model. If simulation results are accurate to within the researcher's objective of 2.5 per cent of historical data results, the LSRM model is determined to be a validated model.

A literature search shows limited practical methodologies to assess the reliability of lean systems.

Reliability computations involve many tedious calculations and, hence, require reliability software.

This paper defines lean system reliability, develops a conceptual model to assess the reliability of lean systems, demonstrates how to decompose a lean system into subsystems and components, and compares simulation results versus actual results to validate the LSRM model.

The purpose of this paper is to encourage the integration of Lean principles with reliability models to sustain Lean efforts on a long‐term basis. It seeks to present a modified FMEA that will allow Lean practitioners to understand and improve the reliability of Lean systems. The modified FMEA approach is developed based on the four critical resources required to sustain Lean systems: personnel, equipment, materials, and schedules.

A three‐phased methodology approach is presented to enhance the reliability of Lean systems. The first phase compares actual business and operational conditions with conditions assumed in Lean implementation. The second phase maps potential deviations of business and operational conditions to their root cause. The third phase utilizes a modified Failure Mode and Effects Analysis (FMEA) to prioritize issues that the organization must address.

A literature search shows that practical methodologies to improve the reliability of Lean systems are non‐existent.

The knowledge database involves many tedious calculations and hence needs to be automated.

The paper has defined Lean system reliability, developed a conceptual model to enhance the Lean system reliability, developed a knowledge base in the form of detailed hierarchical root trees for the four critical resources that support our Lean system reliability, developed a Risk Assessment Value (RAV) based on the concept of effectiveness of detection using Lean controls when Lean designer implements Lean change, developed modified FMEA for the four critical resources.