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This work aims to develop a web platform for inspecting roof structures for technical assistance supported by drones and artificial intelligence. The tools used were HTML, CSS and JavaScript languages; Firebase software for infrastructure; and Custom Vision for image processing.

This study adopted the design science research approach, and the main stages for the development of the web platform include (1) creation and validation of the roof inspection checklist, (2) validation of the use of Custom Vision as an image recognition tool, and (3) development of the web platform.

The results of automatic recognition showed a percentage of 77.08% accuracy in identifying pathologies in roof images obtained by drones for technical assistance.

This study contributed to developing a drone-integrated roof platform for visual data collection and artificial intelligence for automatic recognition of pathologies, enabling greater efficiency and agility in the collection, processing and analysis of results to guarantee the durability of the building.

This paper aims to highlight how the six sigma methods helped the medical equipment manufacturing company in finding and analysing the root causes that lead to the reduction in production rate, rejection rates, quality and other major causes that lead to the reduction in productivity of the blood bags manufacturing unit.

Given the critical nature of blood bag manufacturing Six Sigma was chosen as the primary methodology for this research since Six Sigma’s data-driven approach provides a structured framework to identify, analyse and rectify inefficiencies in the production processes. This study proposes the Six Sigma DMAIC (D-Define, M-Measure, A-Analyse, I-Improve, C-Control) encompassing rigorous problem definition, precise measurement, thorough analysis, improvement and vigilant control mechanisms for effectively attaining predetermined objectives.

The paper demonstrates how the Six Sigma principles were executed in a blood bag manufacturing unit. After a detailed and thorough data analysis, it was found that a total of 40 critical-to-quality factors under the five drivers such as Machine, Components, Inspection and Testing, People and Workspace were influential factors affecting the manufacturing of blood bags. From the study, it is identified that the drivers such as inspection and testing, components and machines contribute significantly to increasing productivity.

The paper offers valuable strategic insights into implementing Six Sigma methodologies within the specific context of a blood bag manufacturing unit. The Six Sigma tools and techniques used by the project team to solve issues within the blood bag manufacturing unit can be used for similar healthcare organizations to successfully deploy Six Sigma. The insights from this research might not be directly applicable to other manufacturing facilities or industries but can be used as a guiding reference for researchers and managers.

The current state of scholarly literature indicates a significant absence in the examination of Six Sigma methodologies designed specifically to improve production output in healthcare equipment manufacturing. This paper highlights the application of Six Sigma principles to enhance efficiency in the specific context of blood bag manufacturing.

While the COVID-19 pandemic has impacted the construction industry, it is still unclear from prior studies about adequately positioning the quality assurance (QA) for the post-pandemic era and future pandemics, especially cross-border construction logistics and supply chain (Cb-CLSC). Thus, this study aims to develop a managerial framework to position the QA of Cb-CLSC during pandemics and post-pandemics by taking lessons from how COVID-19 has impacted the existing QA systems and has been managed successfully.

This is achieved pragmatically through an embedded mixed-method design involving a literature review, survey and interview from experts within the Hong Kong SAR–Mainland China links, typically known as the world’s factory. The design is further integrated with the partial least squares structural equation modelling (PLS-SEM) approach.

The study revealed 10 critical managerial practices (MPs) to position the QA to be adequate for the post-pandemic and during future pandemics, with the top three including “strict observance of government regulations (MP1)”, “planning ahead the period of quality assurance with the quarantine days in host countries (MP6)” and “modification of contract to cater for uncertainties (MP4)”. This attained a relatively good percentage agreement of 53% between the industry and academia. However, the top four MPs regarded as very effective include “implementing digital collaborative inspections with subcontractors and trades (MP8)”, “implementing a digital centralized document and issue management system (MP7)”, “strict observance to government regulations, including vaccination of workers, social distancing, use of prescribed nose masks, etc. (MP1)” and “planning ahead the period of quality assurance with the quarantine days in host countries (MP6)”. Two underlying components of the MPs were revealed as policy-process (PP)-related practices and people-technology-process (PTP)-related practices, and these can be modelled into a managerial framework capable of effectively positioning the QA to be adequate during pandemics through to the post-pandemic era.

The findings of this study depicted significant theoretical and practical contributions to the proactive management of QA activities during pandemics through to the post-pandemic era. It could empower organisations to pay attention to smartly and innovatively balancing people, processes, pandemic policy and technology to inform decisions to effectively position the QA for the post-pandemic era and survive the risks of future pandemics.

The study contributes to the body of knowledge in that it develops a managerial framework to position the QA of Cb-CLSC during pandemics and post-pandemics by taking lessons from how COVID-19 has impacted the existing QA systems and has been managed successfully. It is original research with invaluable primary data in the form of surveys and interviews from experts within the Hong Kong SAR–Mainland China links, typically known as the world’s factory.

Construction industry faces challenges in making objective decisions due to monetary value attached to quality. Among various quality management techniques available, cost of quality (COQ) is one such method used to address the concern. However, the absence of measurable COQ factors to monitor quality costs hampers the implementation of COQ framework in the construction industry. Therefore, this study aims to identify COQ factors focused on visible factors (VF) and hidden factors (HF) and the current requirements to achieve it.

This study is based on Preferred Reporting Items for Systematic Review and Meta-Analyses protocol guidelines. The present study identified 57 articles published between 1992 and 2023 in peer-reviewed journals.

The findings reveal 22 factors, which are grouped into four categories based on COQ. Through systematic review, the authors observed limited methodological and theoretical diversity. In fact, there are no quantitative frameworks to calculate COQ. The study, therefore, developed a framework comprising four major routes/paths of COQ factors within the framework.

The COQ routes developed through this study will enable the practitioners to meticulously categorise VF and HF, facilitating quantifying of quality throughout the lifecycle of project, which is currently absent from the existing quality assurance/quality control (QA/QC) approach. In addition, these COQ routes stand as essential construction strategies, significantly enhancing outcomes related to time, cost, quality, sustainability and fostering closer relationships within project frameworks.

The current study contributes significantly to the existing body of knowledge by developing various COQ routes and proposing future research directions to address gaps in the literature.

This study aims to develop a system dynamics (SD) simulation model to forecast the performance of the Indonesian halal industry to verify whether decision-making has been properly executed to increase the contribution of the success factors.

This study establishes a SD-based model using three subsystems, namely, the halal assurance and certification process, the government and the export–import subsystem.

The best scenario is the third scenario or the combined scenario of providing facilities to accelerate the micro-, small- and medium-sized enterprises (MSMEs), carrying out halal certification, reducing the time for the certification process, increasing the number of the halal auditor and increasing the awareness from enterprises (both MSMEs and large enterprises) to carry out halal certification because of the internal and external motivations.

First, the demand for certification of the type of food product is acquired using the number of SMEs and large companies. Second, the model does not include the government budget as a constraint.

This study provides essential insights into implementing the best policies that can increase the performance of the halal industry.

This study revealed that relevant policy scenarios could be built after simulating and analyzing each scenario’s effect on the halal industry’s performance.

This study will enrich the scientific insight related to institutional theory and resource-based view, as those theories identify success factors associated with the performance of the halal food industry. This study will also enrich the scientific insight related to system dynamic methodology, as it is used to model the performance of the halal industry.

Global building failures, such as the Grenfell Tower fire in London, UK, emphasised the need for trustworthy building handover information for safety. However, a notable gap remains in understanding how reliable handover information can ensure the safety of occupants. This study aims to investigate the use and essential quality of handover information to understand the effects of the quality of information on the management of commercial buildings.

Ninety-four participants from nine organisations who regularly use handover information to manage multiple commercial buildings participated in the semi-structured interviews. Qualitative thematic coding using interview transcripts was performed to identify the utilisation of handover information and its quality requirements.

This study reveals that as-built drawings and product information are predominately used to fulfil statutory obligations, comply with the organisation’s internal policies, evaluate asset valuation and make informed decisions about capital investment and operating expenses. The quality dimensions of “accuracy”, “timeliness”, and “completeness” are preferred in combination to achieve desired outcomes.

This study focused on using handover information in the management of commercial buildings. However, its results can offer valuable perspectives for improving its application across various sectors in the built environment.

The findings affirm the need for quality handover information for safety, compliance and efficient management in commercial buildings.

This research significantly contributes to the current knowledge of handover information in the building sector. Given the study findings, building owners are equipped to define specific handover information requirements and quality requisites.

This paper covers the development of a novel defect model for concrete highway bridges. The proposed defect model is intended to facilitate the identification of bridge’s condition information (i.e. defects), improve the efficiency and accuracy of bridge inspections by supporting practitioners and even machines with digitalised expert knowledge, and ultimately automate the process.

The research design consists of three major phases so as to (1) categorise common defect with regard to physical entities (i.e. bridge element), (2) establish internal relationships among those defects and (3) relate defects to their properties and potential causes. A mixed-method research approach, which includes a comprehensive literature review, focus groups and case studies, was employed to develop and validate the proposed defect model.

The data collected through the literature and focus groups were analysed and knowledge were extracted to form the novel defect model. The defect model was then validated and further calibrated through case study. Inspection reports of nearly 300 bridges in China were collected and analysed. The study uncovered the relationships between defects and a variety of inspection-related elements and represented in the form of an accessible, digitalised and user-friendly knowledge model.

The contribution of this paper is the development of a defect model that can assist inexperienced practitioners and even machines in the near future to conduct inspection tasks. For one, the proposed defect model can standardise the data collection process of bridge inspection, including the identification of defects and documentation of their vital properties, paving the path for the automation in subsequent stages (e.g. condition evaluation). For another, by retrieving rich experience and expert knowledge which have long been reserved and inherited in the industrial sector, the inspection efficiency and accuracy can be considerably improved.

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 study aims to identify and examine the critical resultant impacts of the coronavirus (COVID-19) pandemic on quality assurance (QA) of cross-border construction logistics and supply chain (CB-CLSC).

This is achieved via embedded mixed-method design pragmatically involving desk literature review, survey and interviews from related experts within the Hong Kong Special Administrative Region (SAR)–Mainland China links. The design is further integrated with descriptive analysis, criticality test, rank agreement analysis, spearman correlation test and sentiment analysis.

The study revealed 10 critical resultant impacts of the COVID-19 pandemic on the QA of CB-CLSC, with the top three including “increased use of digital technologies (M10)”, “worker absence, labour shortage and decrease in work rate (M3)” and “changes to how construction sites operate (M9)”. Three underlying dimensions were discovered among all the critical resultant impacts: “cost-time-worker (CTW)-related impacts”, “work contract and operation (WCO)-related impacts” and “work process (WP)-related impacts”. The critical resultant impacts are reflected in the time, cost, raw materials and work processes, and this could manifest as negative as well as an opportunity to position the QA system to be adequate during the pandemic and post-pandemic era.

The study contributes to the knowledge body as it identifies and examines the critical resultant impacts of the COVID-19 pandemic on the QA of CB-CLSC. This is original research with invaluable primary data collected in the form of surveys and interviews from construction quality experts within the Hong Kong (SAR)–Mainland China links, known as the world’s factory.

Gas transmission pipelines are at constant risk of gas leakage or fire due to various atmospheric environments, corrosion on pipe metal surfaces and other external factors. This study aims to reduce the human and financial risks associated with gas transmission by regularly monitoring pipeline performance, controlling situations and preventing disasters.

Facility managers can monitor the status of gas transmission lines in real-time by integrating sensor information into a building information modeling (BIM) 3D model. Using the Monitoring Panel plugin, coded in C# programming language and operated through Navisworks software, the model provides up-to-date information on pipeline safety and performance.

By collecting project information on the BIM and installing critical sensors, this approach allows facility manager to observe the real-time safety status of gas pipelines. If any risks of gas leakage or accidents are identified by the sensors, the BIM model quickly shows the location of the incident, enabling facility managers to make the best decisions to reduce financial and life risks. This intelligent gas transmission pipeline approach changes traditional risk management and inspection methods, minimizing the risk of explosion and gas leakage in the environment.

This research distinguishes itself from related work by integrating sensor data into a BIM model for real-time monitoring and providing facility managers with up-to-date safety information. By leveraging intelligent gas transmission pipelines, the system enables quick identification and location of potential hazards, reducing financial and human risks associated with gas transmission.