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Although training is essential to continuous improvement, scant literature examines post-training facilitators for continuous improvement. The study aims to explore the relationship between training and continuous improvement, the mediating role of self-efficacy and the moderate role of training transfer climate.

This study utilizes the questionnaire survey of 455 Vietnamese employees to test the link between continuous improvement training and continuous improvement, the moderate role of the training transfer climate and the mediating role of self–efficacy.

Research results reveal that training positively influences continuous improvement. Furthermore, self-efficacy fully intervenes in the link between training and continuous improvement. Finally, the training transfer climate positively moderates this link.

Although the link between training and continuous improvement is suspicious, there is scant research on post-training facilitators of continuous improvement applications. To the best of the author's knowledge, this study is one of the first to explore the moderation role of transfer climate and the mediation role of self-efficacy in the relationship between training and continuous improvement.

Continuous improvement in an integrated approach is a philosophy developed over decades based on a set of management practices. It comprises enhancement methodologies that escalate success as well as diminishes letdowns. The state-of-the-art literature finds a variety of practices for the execution of continuous improvement (CI) system. However, it is rare to find an empirical study with an inclusive system that considers various practices in one frame for improving competitiveness. With this outlook, therefore, this study aims originally to identify drivers, motivations and barriers for the application of integrated CI system, and then conduct an empirical investigation within the context of Ethiopian manufacturing industries.

Justification of the problem, extensive review of literature, identification of practices, developing research framework, investigational analysis of the empirical study using reliability and descriptive statistical analysis and identifying leading drivers (unique practices and common factors), motivations and barriers are the research approaches used in this study. Furthermore, the primary data were collected through a self-administered questionnaire and were analyzed using a statistical package for social science (SPSS) 23 and the findings were triangulated to relate to the existing state-of-the-art literature.

The findings indicate 17 common factors associated with human, strategic, operational, technology, structure, resource and information factors; 21 unique practices of just-in-time (JIT), lean-six sigma (LSS), supply chain management (SCM), total productive maintenance (TPM), and total quality management (TQM) methodologies; 20 barriers connected with internal and external issues and 17 motivations linked to operational, innovation and business results are identified. In general, the empirical analysis discovers the practices are noteworthy and commonly supported by a least of 52% of the respondents. As a result, the first five prominent common factors, barriers, unique practices and motivations are also supported by a minimum of 72%, 73.9%, 65.8% and 75% of the respondents, respectively.

Even though this is the first-ever study in the Ethiopian manufacturing sector with the focus on integrated CI practices of JIT, TQM, TPM, SCM and LSS initiatives, some limitations have existed and the major limitations of the study are, targeted merely small number manufacturing companies despite the fact that there are hundreds of companies implementing CI system in Ethiopia, and the data collected were only based on the perception of the respondents, and other information’s like annual reports was not employed to support the findings.

The findings of this study underlined that the implementation of drivers in relation to common factors and unique practices supported by exterminating barriers in manufacturing industries of Ethiopia enables to enhance competitiveness through attaining operational, innovation and business results though it requires a practical case application to prove this perception-based analysis.

Though there are a number of studies published on integrated continuous improvement, currently it is found that there is no literature focused on the identification and empirical investigation of drivers (common factors and unique practices), motivations and barriers for application of integrated CI system with the focus of JIT, TQM, TPM, SCM and LSS initiatives. Therefore, this is the first-ever study, and the empirical analysis of the study discovered that identified practices are providing valuable insights for manufacturing industries which will be on board on this voyage including institutions, practitioners and other sectors.

The purpose of this paper is to explore how process management (PM) can support small- and medium-sized enterprises (SMEs) in pursuing sustained competitive advantage. For this purpose, a dynamic capabilities (DC) lens was used.

A narrative literature review and a multiple case study with an action research approach at two road freight transport companies were used.

PM provides structure and system thinking to support the development of competitive advantage. Concerning PM, management of knowledge, management style and process orientation are key factors for the generation of competitive advantage for SMEs.

This study contributes to PM literature by studying its support for and implementation at SMEs. Furthermore, the study contributes to the literature on DC by providing concrete examples of activities linked to such capabilities.

This study contributes to practitioners by providing examples of implementing PM and identifying competitive advantage, connected with PM elements.

This study has social and environmental implications for the quality of life of the Swedish people.

This paper contributes to clarifying the connection between the research fields of quality management and DC to explore how PM can support SMEs in pursuing sustained competitive advantage.

This article examines the direct effect of total quality management (TQM) practices on organizational performance (OP) and innovation (INN), as well as their indirect effect through organizational learning (OL) as a mediating variable. In addition, this survey examines company size as a contextual variable in the relationship between TQM and outcome variables.

A conceptual framework is proposed to test causal links between TQM, OP, INN and OL. To empirically test this framework, 110 questionnaires were collected from large Tunisian quality-certified industries, and 167 questionnaires were collected from small and medium-sized industries (SMIs) in order to examine the effect of company size. In this order, a multigroup analysis (MGA) is performed.

Using the structural equation modeling technique, seven hypotheses are investigated. The results reveal that TQM has a direct and significant positive effect on OP and INN. Data analysis shows that there is a significant positive effect between TQM and OL, while OL positively influences OP and INN. Furthermore, the results illustrate a mediating effect of OL between TQM and OP and INN. The results reveal that large industries and SMIs show significant differences in the relationship, including the conceptual model.

The conceptual framework can be used by practitioners for effective implementation of TQM practices to simultaneously improve operational performance, quality performance and INN. This study also focuses on the role of OL in large-scale industries.

While the relationships between TQM, INN and OP have been examined separately in previous studies, this study examines the relationship between these variables in a unique model in Tunisian industries, including OL as a mediating variable. In addition, it is one of the few studies that considers firm size as a contextual variable and provides an analysis of its effect on the relationships between these variables. This study presents new data and empirical insights into the relationship between these variables.

This paper aims to investigate the impact of adopting lean manufacturing principles on operational efficiency by eliminating seven major lean wastes (or Muda) in a Malaysian stationery manufacturer. Much of the research on lean considers its application to larger organisations with stable demand patterns. This research examines a small- and medium-sized enterprise (SME) with a volatile demand pattern.

A process activity mapping (PAM) methodology was utilized to identify the potential for waste elimination. PAM is a visual tool that considers every step in a production process. Value-added and non-value-added activities are therefore examined to understand hidden wastes and their sources.

The results revealed that the adopted lean principles significantly reduce the waiting times. This time reduction resulted in savings (reduction of cycle time) and to a certain extent, can be a crucial driver in continuous improvement sustainability in the production process.

The study focuses on a single case study and provides a springboard for further research. Future studies examining the results across a broader sample of organisations would develop the findings further.

The extant literature cites mixed success for lean implementation programmes. The results demonstrate that lean is still recognised as a powerful approach to improving operations in SMEs.

This paper reflects on the application of lean in a real case study showing the impact of lean on operational performance of an SME.

In the context of the journey toward digital transformation and the realization of a fully connected factory, concepts such as data science, artificial intelligence (AI), machine learning (ML) and even predictive models emerge as indispensable pillars. Given the relevance of these topics, the present study focused on the analysis of customer complaint data, employing ML techniques to anticipate complaint accountability. The primary objective was to enhance data accessibility, harnessing the potential of ML models to optimize the complaint handling process and thereby positively contribute to data-driven decision-making. This approach aimed not only to reduce the number of units to be analyzed and customer response time but also to underscore the pressing need for a paradigm shift in quality management. The application of AI techniques sought to enhance not only the efficiency of the complaint handling process and data accessibility but also to demonstrate how the integration of these innovative approaches could profoundly transform the way quality is conceived and managed within organizations.

To conduct this study, real customer complaint data from an automotive company was utilized. Our main objective was to highlight the importance of artificial intelligence (AI) techniques in the context of quality. To achieve this, we adopted a methodology consisting of 10 distinct phases: business analysis and understanding; project plan definition; sample definition; data exploration; data processing and pre-processing; feature selection; acquisition of predictive models; evaluation of the models; presentation of the results; and implementation. This methodology was adapted from data mining methodologies referenced in the literature, taking into account the specific reality of the company under study. This ensured that the obtained results were applicable and replicable across different fields, thereby strengthening the relevance and generalizability of our research findings.

The achieved results not only demonstrated the ability of ML models to predict complaint accountability with an accuracy of 64%, but also underscored the significance of the adopted approach within the context of Quality 4.0 (Q4.0). This study served as a proof of concept in complaint analysis, enabling process automation and the development of a guide applicable across various areas of the company. The successful integration of AI techniques and Q4.0 principles highlighted the pressing need to apply concepts of digitization and artificial intelligence in quality management. Furthermore, it emphasized the critical importance of data, its organization, analysis and availability in driving digital transformation and enhancing operational efficiency across all company domains. In summary, this work not only showcased the advancements achieved through ML application but also emphasized the pivotal role of data and digitization in the ongoing evolution of Quality 4.0.

This study presents a significant contribution by exploring complaint data within the organization, an area lacking investigation in real-world contexts, particularly focusing on practical applications. The development of standardized processes for data handling and the application of predictions for classification models not only demonstrated the viability of this approach but also provided a valuable proof of concept for the company. Most importantly, this work was designed to be replicable in other areas of the factory, serving as a fundamental basis for the company’s data scientists. Until then, limited data access and lack of automation in its treatment and analysis represented significant challenges. In the context of Quality 4.0, this study highlights not only the immediate advantages for decision-making and predicting complaint outcomes but also the long-term benefits, including clearer and standardized processes, data-driven decision-making and improved analysis time. Thus, this study not only underscores the importance of data and the application of AI techniques in the era of quality but also fills a knowledge gap by providing an innovative and replicable approach to complaint analysis within the organization. In terms of originality, this article stands out for addressing an underexplored area and providing a tangible and applicable solution for the company, highlighting the intrinsic value of aligning quality with AI and digitization.

The purpose of this study is to identify and prioritize capabilities and practices to ensure a resilient supply chain during an unexpected disruption. In addition, this study ranks maturity factors that influence the main capabilities identified.

This paper is conducted in three stages. First, capabilities and practices are extracted through a literature review. Second, capabilities and practices are ranked using the analytical hierarchical process method. Third, a gray technique for order preference by similarity to ideal solution method is used to rank maturity factors influencing capabilities.

The findings indicate that responsiveness, readiness, flexibility and adaptability are the most important capabilities for supply chain resilience. Also, commitment and communication are the highest maturity factors influencing resilience capabilities.

The findings provide a hierarchical vision of capabilities and practices for industries to increase resilience. Limitations of the paper are related to capabilities, practices and number of experts consulted.

This paper highlights the importance of high-maturity practices in resilience capability adoption. The findings of this study will encourage decisions-makers to increase maturity practices to build resilience against disruption.

The paper reveals that developing powerful capabilities, good practices and a high level of maturity improve supply chain resilience.

Competencies are significant predictors of employee outcome. Nowadays, new technologies are changing maintenance processes and workflow. The role of employees and their competencies will therefore undergo decisive changes in the future. Therefore, a well-designed competency model for maintenance departments is important. The purpose of this paper is to develop a maintenance 4.0 competency model applicable to all industrial sectors by adapting it to the specificities of each sector.

The research methods consist of a comprehensive literature review on the main characteristics of the competency model and the individual competencies needed for the maintenance 4.0 employees. Interviews were conducted in order to validate and prioritize the required competencies for maintenance 4.0 employees identified in the literature.

The maintenance 4.0 competency model combines the required competencies in maintenance 4.0 and crosses the three hierarchical levels: managers, engineers and technicians. These competencies are organized in terms of four categories: technical, personal, social and methodological. In addition, a degree of importance for each competency is assigned as very important, moderately important and slightly important. As a result, this study identified the essential competencies for maintenance 4.0 stakeholders, where 12 competencies are considered very important for maintenance 4.0 technicians, 19 for engineers and 18 for managers.

This work has some limitations. First, although the articles related to competencies and their classification were selected very carefully, it is difficult to eliminate the probability of overlooking publications. Second, the limitation of the study is based on the difficulty of implementing the model in a case study, given that a minority of industrial companies have implemented maintenance 4.0 technologies in Morocco.

This work has practical implications for both individuals and institutions (companies and academies) to cope with new competency requirements in maintenance 4.0. Organizations can use the model in the recruitment process and for the identification of training needs. The results of the research will also contribute to identifying the scope of competencies of the maintenance 4.0 actors (engineer, manager and technician), which, in practice, contributes to the creation of requirements for the candidates applying for a job in the maintenance department. Additionally, educational institutions should make the necessary changes to their curricula to suitably prepare students for the required maintenance 4.0 competencies.

The social implications of the article result from the contribution to the development of maintenance competencies. Individuals can use this model for their own personal development. Furthermore, companies can use this model to define job profiles for vacancies in M4.0. Therefore, using the model for training program implementation has a positive effect on employee job satisfaction and employees ’morale.

This research develops a novel maintenance 4.0 competency model by categorizing the maintenance workforce into three hierarchical levels: managers, engineers and technicians. In addition, the competency requirement is prioritized to three degrees: very important, moderately important and slightly important. According to the previous studies conducted on maintenance 4.0 and employees' competencies, this study revealed that no research has developed a competency model for maintenance 4.0. Hence, this model is unique, generic and integrative since it presents the most relevant competencies for the three hierarchical levels. Moreover, this work combines the results of the literature review and the experts' returns. This model can be useful in the recruitment of new maintenance employees, the evaluation of their performance and the identification of training needs to cope with new changes in maintenance competencies.

Lean construction (LC) is an innovative approach in the construction industry that has shown significant success in developed countries. Although LC has potential in the construction sector of Pakistan, it has not been extensively explored. This study aims to address this knowledge gap by identifying and predicting current lean practices and assessing the strengths and weaknesses of LC implementation in Pakistan.

Using robust statistical methods to analyze 92 valid responses, the study reveals that approximately 54% of lean practices are currently in use in the construction industry of Pakistan, with a population mean ranging from 52.7% to 55.6%.

Surprisingly, the research identifies instances where some construction firms in Pakistan are implementing LC practices, even though they have only a limited understanding of its underlying principles. Notably, certain subprinciples, such as visual management, top management commitment to change, employee training, process cycle time reduction and production optimization, are less integrated within the construction industry. Exploring the possibility of implementing LC, recommendations for strategies to implement LC in Pakistan are suggested, aligning with the conceptual model proposed by the researchers.

The novelty of this work offers insights that can serve as a comprehensive guide for developing nations. It provides a structured approach to assess and benchmark LC practices, which, in turn, can contribute to a more efficient and effective construction industry. Moreover, the strategies proposed in this research can aid developing countries in the efficient implementation of LC. This will have a positive implication for both economic and developmental outcomes.

This paper proposes an approach for prioritizing Risk Mitigation (RMTG) approaches in perishable food Supply Chains (SCs).

An integrative approach has been proposed, based on the risk typology and Supply Chain RMTG (SCRMTG) approaches literature review, integrating trending Failure Modes and Effects Analysis (FMEA), Quality Function Deployment (QFD) and Quadrant Analysis (QA). Risks are prioritized using Trending FMEA. SCRMTG approaches are prioritized by considering the prioritized risks using QFD and also based on their strategic importance and ease of Benchmarking via QA. The proposed approach has been examined in a dairy-manufacturing company.

Findings indicated supplying the imported machine parts, old machines and delayed new product introduction, respectively, as the most prominent supply, process and demand risks and multiple sourcing, upgraded machinery, hiring skilled staff and training, collaboration with downstream partners as the highly prioritized SCRMTG approaches based on the strategic importance and ease of benchmarking.

The results of this study increase the awareness of SC managers and provide the company with a framework of risk management and the insights to manage SCRs in the dairy industry more effectively and efficiently.

While the literature review indicates that only a few studies have been focused on prioritizing SCRMTG approaches concerning each type of SCRs, SCRMTG approaches are prioritized based on the SCRs type. Other innovations include QFD development based on the FMEA and SCRMTG approaches, considering the probability of risk occurrence, severity-impact cost and risk recovery duration in trending FMEA instead of the three risk factors in traditional FMEA.