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The purpose of this study was to improve the overall equipment effectiveness (OEE) of the production process of the shredder operation of ABC company, an industrial waste management company which supplies pre-processed industrial waste as alternative fuel to a cement plant.

This case study investigated all possible availability and performance losses that caused the shredder system’s OEE and various problem-solving techniques, such as root cause analysis and Pareto analysis, were used to find the root cause of the reduced OEE.

After analysing this case study, three significant loss factors were identified from all the availability and performance losses, which caused the shredder system’s OEE losses. Practical solutions were found for the effect of those loss factors to improve the machine’s OEE and productivity.

This case study has been concentrated on only analysing of losses and improvement of OEE in the production process and not about cost analysis between loss and improvements.

This paper shows how to improve the OEE of a production process through various problem-solving techniques by identifying its losses and how to achieve the best solutions for those losses in a practical manner.

The purpose of this paper is twofold: first, a case study on applying lean principles in manufacturing operations to redesign and optimize an electronic device assembly process and its impact on performance and second, introducing cardboard prototyping as a Kaizen tool offering a novel approach to testing and simulating improvement scenarios.

The study employs value stream mapping, root cause analysis, and brainstorming tools to identify root causes of poor performance, followed by deploying a Kaizen event to redesign and optimize an electronic device assembly process. Using physical models, bottlenecks and opportunities for improvement were identified by the Kaizen approach at the workstations and assembly lines, enabling the testing of various scenarios and ideas. Changes in lead times, throughput, work in process inventory and assembly performance were analyzed and documented.

Pre- and post-improvement measures are provided to demonstrate the impact of the Kaizen event on the performance of the assembly cell. The study reveals that implementing lean tools and techniques reduced costs and increased throughput by reducing assembly cycle times, manufacturing lead time, space utilization, labor overtime and work-in-process inventory requirements.

This paper adds a new dimension to applying the Kaizen methodology in manufacturing processes by introducing cardboard prototyping, which offers a novel way of testing and simulating different scenarios for improvement. The paper describes the process implementation in detail, including the techniques and data utilized to improve the process.

This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”

The research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.

This research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.

This research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.

This paper aims to explore a framework for implementing Lean Construction (LC) to provide corrective actions for quality defects, customer dissatisfaction and value creation during the construction of megaprojects.

This paper presents a case study involving the construction of the Mohamed VI Tower in Morocco. It is the tallest tower in Africa, with 55 floors and a total height of 250 m. This study of the quality of the work and the involvement of the LC was carried out using the Define–Measure–Analysis–Improve–Control approach from Lean six sigma. It describes the Critical to Quality and analyses the root causes of quality defects, customer dissatisfaction and variation in the quality process.

Firstly, the results of this study map the causal factors of lack of quality as established in the literature. Secondly, the LC tools have reduced non-value-added sources of quality waste and, consequently, improved critical quality indicators.

This document focuses on one part of the tower’s construction and is limited to a project case in a country where LC is rarely used.

This study reinforces the literature reviews, surveys and the small number of case studies that have validated the potential of LC and further clarifies future directions for the practical emergence of this quality improvement approach, especially for large-scale projects.

This review paper, using a systematic literature review (SLR) approach, aims to unravel the various system-wide waste in the construction industry and highlight the connectivity to construction phases, namely men, materials, machines, methods and measurement (5M) and impacting factors.

This study used an SLR approach and examined articles published since the 2000s to explore the connectivity of system-wide waste to construction phases, 5M and impacting factors. The results are given in table forms and a causal loop diagram.

Results show that the construction and demolition (CD) waste research carried out from various perspectives is standalone. The review identified ten types of system-wide waste with strong interlinks in the construction industry. The finding highlights connectivity between wastes other than material, labour and time and the wastes' impacting factors. Further, the review results highlighted the solid connectivity for construction phases, 5M, and impacting factors such as productivity (P), delay (D), accidents (A), resource utilisation (R) and cost(C).

SLR methodology limitations include not keeping in phase with the most updated field knowledge. This limitation is offset by choosing the range for literature review within the last two decades. This literature review may not have captured all published articles because the restriction of database access and search was based only on English. Also, fruitful articles hiding in less popular journals may not be included in the well-known database that was searched. Researcher bias of the authors and other researchers that authored the articles referred to is a limitation. These limitations are acknowledged.

This article unravels the construction system-wide waste and the waste's interlinks, which would aid industry understanding and focus on eliminating the waste. The article highlights the connectivity of system-wide wastes to 5M, which would help better understand the causes of the waste. Further, the paper discusses the connectivity of system-wide waste, 5M and P, D, A, R and C that would aid the organisation's overall performance. The practical and theoretical implications include a better understanding of waste types to help capture better data for waste reduction and productivity improvement. The operating managers could use the tracking of wastes to compare estimated and actual resources at every process stage. This article on system-wide waste, 5M and P, D, A, R and C, relationships and their effects can theorize that the construction industry is more likely to identify clear root causes of waste now than previously. The theoretical implications include enhanced understanding for academics on connectivity between waste, 5M and P, D, A, R and C that the academics can use and expand to provide new insights to existing knowledge.

For the first time, this article categorised and highlighted the ten types of waste in construction industries and the industries' connectivity to construction phases, 5M and impacting factors.

This study aims to quantify and prioritize the main causes of lean wastes and to apply reduction methods by employing better waste cause identification methodologies.

We employed fuzzy techniques for order preference by similarity to the ideal solution (FTOPSIS), fuzzy analytical hierarchy process (FAHP), and failure mode effect analysis (FMEA) to determine the causes of defects. To determine the current defect cause identification procedures, time studies, checklists, and process flow charts were employed. The study focuses on the sewing department of a clothing industry in Addis Ababa, Ethiopia.

These techniques outperform conventional techniques and offer a better solution for challenging decision-making situations. Each lean waste’s FMEA criteria, such as severity, occurrence, and detectability, were examined. A pairwise comparison revealed that defect has a larger effect than other lean wastes. Defects were mostly caused by inadequate operator training. To minimize lean waste, prioritizing their causes is crucial.

The research focuses on a case company and the result could not be generalized for the whole industry.

The study used quantitative approaches to quantify and prioritize the causes of lean waste in the garment industry and provides insight for industrialists to focus on the waste causes to improve their quality performance.

The methodology of integrating FMEA with FAHP and FTOPSIS was the new contribution to have a better solution to decision variables by considering the severity, occurrence, and detectability of the causes of wastes. The data collection approach was based on experts’ focus group discussion to rate the main causes of defects which could provide optimal values of defect cause prioritization.

This paper aims to design a hybrid model of knowledge-based performance management system (KBPMS) for facilitating Lean Six-Sigma (L6s) application to increase contractor productivity without compromising human safety in Indonesian upstream oil field operations that manage ageing and life extension (ALE) facilities.

The research design applies a pragmatic paradigm by employing action research strategy with qualitative-quantitative methodology involving 385 of 1,533 workers. The KBPMS-L6s conceptual framework is developed and enriched with the Analytical Hierarchy Process (AHP) to prioritize fit-for-purpose Key Performance Indicators. The application of L6s with Human Performance Modes analysis is used to provide a statistical baseline approach for pre-assessment of the contractor’s organizational capabilities. A comprehensive literature review is given for the main pillars of the contextual framework.

The KBPMS-L6s concept has given an improved hierarchy for strategic and operational levels to achieve a performance benchmark to manage ALE facilities in Indonesian upstream oil field operations. To increase quality management practices in managing ALE facilities, the L6s application requires an assessment of the organizational capability of contractors and an analysis of Human Performance Modes (HPM) to identify levels of construction workers’ productivity based on human competency and safety awareness that have never been done in this field.

The action research will only focus on the contractors’ productivity and safety performances that are managed by infrastructure maintenance programs for managing integrity of ALE facilities in Indonesian upstream of oil field operations. Future research could go toward validating this approach in other sectors.

This paper discusses the implications of developing the hybrid KBPMS- L6s enriched with AHP methodology and the application of HPM analysis to achieve a 14% reduction in inefficient working time, a 28% reduction in supervision costs, a 15% reduction in schedule completion delays, and a 78% reduction in safety incident rates of Total Recordable Incident Rate (TRIR), Days Away Restricted or Job Transfer (DART) and Motor Vehicle Crash (MVC), as evidence of achieving fit-for-purpose KPIs with safer, better, faster, and at lower costs.

This paper does not discuss social implications

This paper successfully demonstrates a novel use of Knowledge-Based system with the integration AHP and HPM analysis to develop a hybrid KBPMS-L6s concept that successfully increases contractor productivity without compromising human safety performance while implementing ALE facility infrastructure maintenance program in upstream oil field operations.

This study aims to investigate the relationship between individual-level psychological resources and team resilience in the context of expatriate project management teams. It seeks to understand how personal psychological resources contribute to team resilience and explore the dynamic evolution mechanism of team resilience. The goal is to enhance team resilience among expatriates in a BANI (Brittle, Anxious, Nonlinear, and Incomprehensible) world, where organizations face volatile and uncertain conditions.

An online survey was applied for data collection, and 315 valid samples from Chinese expatriates in international construction projects were utilized for data analysis. A structural equation model (SEM) examines the relationships between personal psychological resources and team resilience. The study identifies five psychological factors influencing team resilience: Employee Resilience, Cross-cultural Adjustment, Self-efficacy, Social Support, and Team Climate. The hypothesized relationships are validated through the SEM analysis. Additionally, a fuzzy cognitive map (FCM) is constructed to explore the dynamic mechanism of team resilience formation based on the results of the SEM.

The SEM analysis confirms that employee resilience, cross-cultural adjustment, and team climate positively impact team resilience. Social support and self-efficacy also have positive effects on team climate. Moreover, team climate is found to fully mediate the relationship between self-efficacy and team resilience, as well as between social support and team resilience. The FCM model provides further insights into the dynamic evolution of team resilience, highlighting the varying impact effects of antecedents during the team resilience development process and the effectiveness of different combinations of intervention strategies.

This study contributes to understanding team resilience by identifying the psychological factors influencing team resilience in expatriate project management teams. The findings emphasize the importance of social support and team climate in promoting team resilience. Interventions targeting team climate are found to facilitate the rapid development of team resilience. In contrast, interventions for social support are necessary for sustainable, long-term high levels of team resilience. Based on the dynamic simulation results, strategies for cultivating team resilience through external intervention and internal adjustment are proposed, focusing on social support and team climate. Implementing these strategies can enhance project management team resilience and improve the core competitiveness of contractors in the BANI era.

The construction industry has struggled to deliver schemes on time to budget and right-first-time (RFT). There have been many studies into nonconformance and rework through quantitative research over the years to understand why the industry continues to see similar issues of failure. Some scholars have reported rework figures as high as 12.6% of total contract value, highlighting major concerns of the sustainability of construction projects. Separately, however, there have been few studies that explore and detail the views of industry professions who are caught in the middle of quality issues, to understand their perceptions of where the industry is failing. As such, this paper interrogates qualitative data (open-ended questions) on the topic of nonconformance and rework in construction to understand what industry professionals believe are the causes and suggested improvement areas.

A qualitative approach is adopted for this research. An industry survey consisting of seven open-ended questions is presented to two professional working groups within a Tier 1 contractor, and outputs are analysed using statistic software (NVivo 12) to identify prominent themes for discussion. Inductive analysis is undertaken to gain further insight into responses to yield recurrent areas for continuous improvement.

Qualitative analysis of the survey reveals a persistent prioritisation of cost and programme over quality management in construction project. Furthermore, feedback from construction professionals present a number of improvement areas that must be addressed to improve quality. These include increased training and competency investment, overhauling quality behaviours, providing greater quality leadership direction and reshaping the way clients govern schemes.

There are limitations to this paper that require noting. Firstly, the survey was conducted within one principal contractor with varying levels of knowledge across multiple sectors. Secondly, the case study was from one major highways scheme; therefore, the generalisability of the findings is limited. It is suggested that a similar exercise is undertaken in other sectors to uncover similar improvement avenues.

The implications of this study calls for quality to be re-evaluated at project, company, sector and government levels to overhaul how quality is delivered. Furthermore, the paper identifies critical learning outcomes for the construction sector to take forward, including the need to reassess projects to ensure they are appropriately equip with competent personnel under a vetted, progressive training programme, share collaborative behaviours that value quality delivery on an equal standing to safety, programme and cost and tackle the inappropriate resource dilemmas projects finding themselves in through clear tendering and accurate planning. In addition, before making erratic decisions, projects must assess the risk profiling of proceed without approved design details and include the client in the decision-making process. Moreover, the findings call for a greater collaborative environment between the construction team and quality management department, rather than being seen as obstructive (i.e. compliance based policing). All of these must be driven by leadership to overhaul the way quality is managed on schemes. The findings demonstrate the importance and impact from open-ended survey response data studies to enhance quantitative outcomes and help provide strengthened proposals of improvement.

This paper addresses the highly sensitive area of quality failure outcomes and interrogates them via an industry survey within a major UK contractor for feedback. Unique insights are gained into how industry professionals perceive quality in construction. From previous research, this has been largely missing and offers a valuable addition in understanding the “quality status quo” from those delivering schemes.

The need to develop effective approaches for responding to healthcare incidents for the purpose of learning and improving patient safety has been recognised in current national policy. However, research into this topic is limited. This study aims to explore the perspectives of professionals in mental health trusts in England about what works well and what could be done better when implementing serious incident management systems.

This was a qualitative study using semi-structured interviews. In total, 15 participants were recruited, comprising patient safety managers, serious incident investigators and executive directors, from five mental health trusts in England. The interview data were analysed using a qualitative-descriptive approach to develop meaningful themes. Quotes were selected and presented based on their representation of the data.

Participants were dissatisfied with current systems to manage serious incidents, including the root cause analysis approach, which they felt were not adequate for assisting learning and improvement. They described concerns about the capability of serious incident investigators, which was felt to impact on the quality of investigations. Processes to support people adversely affected by serious incidents were felt to be an important part of incident management systems to maximise the learning impact of investigations.

Findings of this study provide translatable implications for mental health trusts and policymakers, informed by insights into how current approaches for learning from healthcare incidents can be transformed. Further research will build a more comprehensive understanding of mechanisms for responding to healthcare incidents.