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The Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely used for analyzing OEE results and identifying corrective actions. Therefore, the approach proposed in this paper aims to provide a new rule-based Machine Learning (ML) framework for OEE enhancement and the selection of improvement actions.

Association Rules (ARs) are used as a rule-based ML method for extracting knowledge from huge data. First, the dominant loss class is identified and traditional methodologies are used with ARs for anomaly classification and prioritization. Once selected priority anomalies, a detailed analysis is conducted to investigate their influence on the OEE loss factors using ARs and Network Analysis (NA). Then, a Deming Cycle is used as a roadmap for applying the proposed methodology, testing and implementing proactive actions by monitoring the OEE variation.

The method proposed in this work has also been tested in an automotive company for framework validation and impact measuring. In particular, results highlighted that the rule-based ML methodology for OEE improvement addressed seven anomalies within a year through appropriate proactive actions: on average, each action has ensured an OEE gain of 5.4%.

The originality is related to the dual application of association rules in two different ways for extracting knowledge from the overall OEE. In particular, the co-occurrences of priority anomalies and their impact on asset Availability, Performance and Quality are investigated.

Industrialization is a major contributor to pollution and the worsening of some social problems. A change in this context would help in a new industrial model aiming at a viable and sustainable manufacturing system. This research aims to verify the state of the art of sustainability within the industrial production process through a systematic literature review, verifying the main characteristics in relation to industrial sustainability that the literature demonstrates.

The development of the research took place in three stages: a survey of articles with Journal Citation Reports (JCR), the construction of the database and descriptive analysis and text mining analyses of social networks and content. The survey took place through academically endorsed research platforms, totaling a total of 352 scientific articles, which included 18 quality management tools and worked with at least one sustainability indicator (financial, social and environmental).

Lean manufacturing, integrated management system and Six Sigma were the most cited quality tools, and articles containing the three indicators were found more frequently. It was found that most authors treated sustainability only as an environmental contribution. Knowledge of the organization's structural and management issues is essential for implementing sustainability and production process improvement.

This work is the first to develop a systematic analysis regarding the use of sustainability implementation in the industrial production process, considering a wide scope of production process tools, guiding on the characteristics of sustainability relating to the main critical success factors (CSFs), motivations, difficulties and benefits that lead industries in different parts of the world to implement sustainability.

The various quality improvement (QI) frameworks and maturity models described in the health services literature consider some aspects of QI while excluding others. This paper aims to present a concerted attempt to create a quality improvement maturity model (QIMM) derived from holistic principles underlying the successful implementation of system-wide QI programmes.

A hybrid methodology involving a systematic review (Phase 1) of over 270 empirical research articles and books developed the basis for the proposed QIMM. It was followed by expert interviews to refine the core constructs and ground the proposed QIMM in contemporary QI practice (Phase 2). The experts included academics in two academic conferences and 59 QI managers from the New Zealand health-care system. In-depth interviews were conducted with QI managers to ascertain their views on the QIMM and its applicability in their respective health organisations (HOs).

The QIMM consists of four dimensions of organisational maturity, namely, strategic, process, supply chain and philosophical maturity. These dimensions progress through six stages, namely, identification, ad-hoc, formal, process-driven, optimised enterprise and finally a way of life. The application of the QIMM by the QI managers revealed that the scope of QI and the breadth of the principles adopted by the QI managers and their HOs in New Zealand is limited.

The importance of QI in health systems cannot be overstated. The proposed QIMM can help HOs diagnose their current state and provide a guide to action achieving a desirable state of quality improvement maturity. This QIMM avoids reliance on any single QI methodology. HOs – using the QIMM – should retain full control over the process of selecting any QI methodology or may even cherry-pick principles to suit their needs as long as they understand and appreciate the true nature and scope of quality overstated. The proposed QIMM can help HOs diagnose their current state and provide a guide to action achieving a desirable state of quality improvement maturity. This QIMM avoids reliance on any single QI methodology. HOs – using the QIMM – should retain full control over the process of selecting any QI methodology or may even cherry-pick principles to suit their needs as long as they understand and appreciate the true nature and scope of quality.

This paper contributes new knowledge by presenting a maturity model with an integrated set of quality principles for HOs and their extended supply networks.

Operational excellence (OpEx) is a proven philosophy focusing on continuous improvement in processes and systems for superior performance and efficiency. It plays a crucial role in the energy sector, acting as a catalyst for safety, customer satisfaction, sustainability and competitiveness. This research aims to assess OpEx methodologies in Oman’s energy sector, examining methods, approaches, motivations and sustainability.

This study applies qualitative analysis methodology, involving interviews with 18 industry experts, from the energy sector in a sizeable energy country.

The analysis revealed a growing demand, particularly, in the oil and gas industry, driven by emerging business needs. Qualitative data analysis has identified 10 themes such as implemented methodologies, motivation drivers, deployment approaches, sustainability factors, benefits and challenges. Additionally, new themes emerged, including influencers to start OpEx, resource requirements, enablers for successful OpEx and systems.

This research was limited to Oman and the findings drawn from Omani energy companies may have limited applicability to energy companies in other regions. Therefore, if these findings were to be used, the validation of the findings in relation to other countries should be conducted, to ensure the validity of the context and outcome.

These findings contribute to understanding OpEx dynamics in the Omani energy sector, offering valuable insights for effective utilisation and organisational goal achievement. Furthermore, the study offers valuable insights on how to effectively employ OpEx initiatives in the energy sector to achieve their goals and create value. It addresses the lack of knowledge, offers a framework for successful OpEx implementation, bridges the theory-practice gap and provides insights for optimal utilisation.

To the best of the authors’ knowledge, this is the first empirical study on assessing OpEx methodologies in the energy sector, and therefore it serves as a foundation for many future studies. The study provides a theoretical foundation for the OpEx methodologies in terms of organisational readiness for successful OpEx implementation.

The purpose of this study is to examine the critical failure factors (CFFs) linked to various types of process improvement (PI) projects such as Kaizen, Lean, Six Sigma, Lean Six Sigma and Agile. Proposing a mitigation framework accordingly is also an aim of this study.

This research undertakes a systematic literature review of 49 papers that were relevant to the scope of the study and that were published in four prominent databases, including Google Scholar, Scopus, Web of Science and EBSCO.

Further analysis identifies 39 factors that contribute to the failure of PI projects. Among these factors, significant emphasis is placed on issues such as “resistance to cultural change,” “insufficient support from top management,” “inadequate training and education,” “poor communication” and “lack of resources,” as primary causes of PI project failures. To address and overcome the PI project failures, the authors propose a framework for failure mitigation based on change management models. The authors present future research directions that aim to enhance both the theoretical understanding and practical aspects of PI project failures.

Through this study, researchers and project managers can benefit from well-structured guidelines and invaluable insights that will help them identify and address potential failures, leading to successful implementation and sustainable improvements within organizations.

To the best of the author’s knowledge, this paper is the first study of its kind to examine the CFFs of five PI methodologies and introduces a novel approach derived from change management theory as a solution to minimize the risk associated with PI failure.

This paper explores Lean Six Sigma principles and the DMAIC (define, measure, analyze, improve, control) methodology to propose a new Lean Six Sigma 4.0 (LSS 4.0) framework for employee occupational exams and address the real-world issue of high-variability exams that may arise.

This study uses mixed methods, combining qualitative and quantitative data collection. A detailed case study assesses the impact of LSS interventions on the exam management process and tests the applicability of the proposed LSS 4.0 framework for employee occupational exams.

The results reveal that changing the health service supplier in the explored organization caused a substantial raise in occupational exams, leading to increased costs. By using syntactic interoperability, lean, six sigma and DMAIC approaches, improvements were identified, addressing process deviations and information requirements. Implementing corrective actions improved the exam process, reducing the number of exams and associated expenses.

It is important to acknowledge certain limitations, such as the specific context of the case study and the exclusion of certain exam categories.

The practical implications of this research are substantial, providing organizations with valuable managerial insights into improving efficiency, reducing costs and ensuring regulatory compliance while managing occupational exams.

This study fills a research gap by applying LSS 4.0 to occupational exam management, offering a practical framework for organizations. It contributes to the existing knowledge base by addressing a relatively novel context and providing a detailed roadmap for process optimization.

Output from the Irish Dairy Industry has grown rapidly since the abolition of quotas in 2015, with processors investing heavily in capacity expansion to deal with the extra milk volumes. Further capacity gains may be achieved by extending the processing season into the winter, a key enabler for which being the reduction of duration of the winter maintenance overhaul period. This paper aims to investigate if Lean Six Sigma tools and techniques can be used to enhance operational maintenance performance, thereby releasing additional processing capacity.

Combining the Six-Sigma Define, Measure, Analyse, Improve, Control (DMAIC) methodology and the structured approach of Turnaround Maintenance (TAM) widely used in process industries creates a novel hybrid model that promises substantial improvement in maintenance overhaul execution. This paper presents a case study applying the DMAIC/TAM model to Ireland’s largest dairy processing site to optimise the annual maintenance shutdown. The objective was to deliver a 30% reduction in the duration of the overhaul, enabling an extension of the processing season.

Application of the DMAIC/TAM hybrid resulted in process enhancements, employee engagement and a clear roadmap for the operations team. Project goals were delivered, and original objectives exceeded, resulting in €8.9m additional value to the business and a reduction of 36% in the duration of the overhaul.

The results demonstrate that the model provides a structure that promotes systematic working and a continuous improvement focus that can have substantial benefits for wider industry. Opportunities for further model refinement were identified and will enhance performance in subsequent overhauls.

To the best of the authors’ knowledge, this is the first time that the structure and tools of DMAIC and TAM have been combined into a hybrid methodology and applied in an Irish industrial setting.

The aim of this paper is to explore the possibility of using the Define-Measure-Analyze-Improve-Control (DMAIC) cycle, process mining (PM) and multi-criteria decision methods in an integrated way so that these three elements combined result in a methodology called the Agile DMAIC cycle, which brings more agility and reliability in the execution of the Six Sigma process.

The approach taken by the authors in this study was to analyze the studies arising from this union of concepts and to focus on using PM tools where appropriate to accelerate the DMAIC cycle by improving the first two steps, and to test using the AHP as a decision-making process, to bring more excellent reliability in the definition of indicators.

It was indicated that there was a gain with acquiring indicators and process maps generated by PM. And through the AHP, there was a greater accuracy in determining the importance of the indicators.

Through the results and findings of this study, more organizations can understand the potential of integrating Six Sigma and PM. It was just developed for the first two steps of the DMAIC cycle, and it is also a replicable method for any Six Sigma project where data acquisition through mining is possible.

The authors develop a fully applicable and understandable methodology which can be replicated in other settings and expanded in future research.

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.

The purpose of this paper is to investigate how successful lean six sigma (LSS) manifests in the Australasian (Australian and New Zealand) context relative to the context in the USA in terms of LSS project definition, structure and practices.

In-depth investigation through case studies – 12 Australian/New Zealand cases and 4 US cases – on the implementation mechanisms of successful LSS initiatives.

A significant difference was found between Australasian and US definitions of an LSS project. However, firms in both regions followed similar project selection, initiating and execution practices. LSS reporting structures were found to be well-established in US organizations, but none of the Australasian organizations were found to be equipped with such a structure, although the effectiveness of LSS implementation success remained unaffected.

Sufficient uniformity of LSS was found across two regions implying its usefulness/generalizability, but the findings are based only on 12 cases.

The paper provides the groundwork to develop a unique LSS model for Australasian organizations to improve processes in an effective and efficient manner.