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Industry 4.0 is exacerbating the need for offsite construction (OSC) adoption, and this rapid transformation is pushing the boundaries of construction skills towards extensive modernisation. The adoption of this modern production strategy by the construction industry would redefine the position of OSC. This study aims to examine whether the existing skills are capable of satisfying the needs of different OSC types.

A critical literature review evaluated the impact of transformative technology on OSC skills. An existing industry standard OSC skill classification was used as the basis to develop a master list that recognises emerging and diminishing OSC skills. The master list recognises 67 OSC skills under six skill categories: managers, professionals, technicians and trade workers, clerical and administrative workers, machinery operators and drivers and labourers. The skills data was extracted from a series of 13 case studies using document reviews and semi-structured interviews with project stakeholders.

The multiple case study evaluation recognised 13 redundant skills and 16 emerging OSC skills such as architects with building information modelling and design for manufacture and assembly knowledge, architects specialised in design and logistics integration, advanced OSC technical skills, factory operators, OSC estimators, technicians for three dimensional visualisation and computer numeric control operators. Interview findings assessed the current state and future directions for OSC skills development. Findings indicate that the prevailing skills are not adequate to readily relocate construction activities from onsite to offsite.

To the best of the authors’ knowledge, this research is one of the first studies that recognises the major differences in skill requirements for non-volumetric and volumetric OSC types.

Industry 4.0 has put forward a smart perspective on managing supply chain networks and their operations. The current manufacturing system is primarily data-driven. Industries are deploying new emerging technologies in their operations to build a competitive edge in the business environment; however, the true potential of smart manufacturing has not yet been fully unveiled. This research aims to extensively analyse emerging technologies and their interconnection with smart manufacturing in developing smarter supply chains.

This research endeavours to establish a conceptual framework for a smart supply chain. A real case study on a smart factory is conducted to demonstrate the validity of this framework for building smarter supply chains. A comparative analysis is carried out between conventional and smart supply chains to ascertain the advantages of smart supply chains. In addition, a thorough investigation of the several factors needed to transition from smart to smarter supply chains is undertaken.

The integration of smart technology exemplifies the ability to improve the efficiency of supply chain operations. Research findings indicate that transitioning to a smart factory radically enhances productivity, quality assurance, data privacy and labour efficiency. The outcomes of this research will help academic and industrial sectors critically comprehend technological breakthroughs and their applications in smart supply chains.

This study highlights the implications of incorporating smart technologies into supply chain operations, specifically in smart purchasing, smart factory operations, smart warehousing and smart customer performance. A paradigm transition from conventional, smart to smarter supply chains offers a comprehensive perspective on the evolving dynamics in automation, optimisation and manufacturing technology domains, ultimately leading to the emergence of Industry 5.0.

Prefabricated technology is gradually being applied to the construction of subway stations due to its characteristic of mechanization. However, the prefabricated subway station in China is in the initial stage of development, which is prone to construction safety issues. This study aims to evaluate the construction safety risks of prefabricated subway stations in China and formulate corresponding countermeasures to ensure construction safety.

A construction safety risk evaluation index system for the prefabricated subway station was established through literature research and the Delphi method. Furthermore, based on the structure entropy weight method, matter-element theory and evidence theory, a hybrid evaluation model is developed to evaluate the construction safety risks of prefabricated subway stations. The basic probability assignment (BPA) function is obtained using the matter-element theory, the index weight is calculated using the structure entropy weight method to modify the BPA function and the risk evaluation level is determined using the evidence theory. Finally, the reliability and applicability of the evaluation model are verified with a case study of a prefabricated subway station project in China.

The results indicate that the level of construction safety risks in the prefabricated subway station project is relatively low. Man risk, machine risk and method risk are the key factors affecting the overall risk of the project. The evaluation results of the first-level indexes are discussed, and targeted countermeasures are proposed. Therefore, management personnel can deeply understand the construction safety risks of prefabricated subway stations.

This research fills the research gap in the field of construction safety risk assessment of prefabricated subway stations. The methods for construction safety risk assessment are summarized to establish a reliable hybrid evaluation model, laying the foundation for future research. Moreover, the construction safety risk evaluation index system for prefabricated subway stations is proposed, which can be adopted to guide construction safety management.

Industry 4.0 has an inimitable potential to create competitive advantages for the apparel industry by enhancing productivity, production, profitability, efficiency and effectiveness. This study, thus, aims to assess the digitalisation level of the Tanzanian apparel industry through the Industry 4.0 perspectives.

A mixed-methods-based approach was deployed. This study deployed semi-structured interviews, document review and observation methods for the qualitative approach. For the quantitative approach, closed-ended questionnaires were used to ascertain the digitalisation levels and maturity level of the textiles and apparel (T&A) factories and small and medium-sized textile enterprises in Tanzania. The sample size was 110, with participants engaged through the purposive sampling technique.

Industry 4.0 frameworks evolved into practices mainly since 2011 in several service and manufacturing industries globally. For Tanzania, the findings indicate that the overall maturity level of the T&A industries is 2.5 out of 5.0, demonstrating a medium level of adoption. Thus, the apparel industries are not operating under the industry 4.0 framework; they are operating within the third industrial revolution – Industry 3.0 – framework. For such industries to operate within the fourth industrial revolution – Industry 4.0 – that is only possible if there is significantly well-developed industrial infrastructure, availability of engineering talent, stable commercial partnerships, demand from the marketplace and transactional relationship with customers.

This study’s limitations include: firstly, Industry 4.0 is an emerging area; this resulted in limited theoretical underpinnings in the Tanzanian perspectives. Secondly, the studied industries may not suffice the need to generalise the findings for the entire country, thus needing another study.

Although Industry 4.0 conceptual frameworks have been on trial in several industries since 2011, this is amongst the first empirical research on Industry 4.0 in the Tanzanian apparel industry that assesses the digitalisation levels.

Process mining has emerged in the last decade as one of the most promising tools to discover and understand the actual execution of processes. This paper addresses the application of process mining techniques to analyze the performance of automatic guided vehicles (AGVs) in one of the Body in White circuits of the factory that Stellantis has in Vigo, Spain.

Standard process mining discovery and conformance algorithms are applied to analyze the different AGV execution paths, their lead times, main sources and identify any unexpected potential situations, such as unexpected paths or loops.

Results show that this method provides very useful insights which are not evident for logistics technicians. Even with such automated devices, where the room for decreased efficiency can be apparently small, process mining shows there are cases where unexpected situations occur, leading to an increase in circuit times and different variants for the same route, which pave the road for an actual improvement in performance and efficiency.

This paper provides evidence of the usefulness of applying process mining in manufacturing processes. Practical applications of process mining have traditionally been focused on processes related to services and management, such as order to cash and purchase to pay in enterprise resource planning software. Despite its potential for use in industrial manufacturing, such contributions are scarce in the current state of the art and, as far as we are aware of, do not fully justify its application.

Rebar is the prime component of reinforced concrete structures, and rebar monitoring is a time-consuming and technical job. With the emergence of the fourth industrial revolution, the construction industry practices have evolved toward digitalization. Still, hesitation remains among stakeholders toward the adoption of advanced technologies and one of the significant reasons is the unavailability of knowledge frameworks and implementation guidelines. This study aims to investigate technical factors impacting automated monitoring of rebar for the understanding, confidence gain and effective implementation by construction industry stakeholders.

A structured study pipeline has been adopted, which includes a systematic literature collection, semistructured interviews, pilot survey, questionnaire survey and statistical analyses via merging two techniques, i.e. structural equation modeling and relative importance index.

The achieved model highlights “digital images” and “scanning” as two main categories being adopted for automated rebar monitoring. Moreover, “external influence”, “data-capturing”, “image quality”, and “environment” have been identified as the main factors under “digital images”. On the other hand, “object distance”, “rebar shape”, “occlusion” and “rebar spacing” have been highlighted as the main contributing factors under “scanning”.

The study provides a base guideline for the construction industry stakeholders to gain confidence in automated monitoring of rebar via vision-based technologies and effective implementation of the progress-monitoring processes. This study, via structured data collection, performed qualitative and quantitative analyses to investigate technical factors for effective rebar monitoring via vision-based technologies in the form of a mathematical model.

Recently, powerful instruments for biomedical engineering research studies, including disease modeling, drug designing and nano-drug delivering, have been extremely investigated by researchers. Particularly, investigation in various microfluidics techniques and novel biomedical approaches for microfluidic-based substrate have progressed in recent years, and therefore, various cell culture platforms have been manufactured for these types of approaches. These microinstruments, known as tissue chip platforms, mimic in vivo living tissue and exhibit more physiologically similar vitro models of human tissues. Using lab-on-a-chip technologies in vitro cell culturing quickly caused in optimized systems of tissues compared to static culture. These chipsets prepare cell culture media to mimic physiological reactions and behaviors.

The authors used the application of lab chip instruments as a versatile tool for point of health-care (PHC) applications, and the authors applied a current progress in various platforms toward biochip DNA sensors as an alternative to the general bio electrochemical sensors. Basically, optical sensing is related to the intercalation between glass surfaces containing biomolecules with fluorescence and, subsequently, its reflected light that arises from the characteristics of the chemical agents. Recently, various techniques using optical fiber have progressed significantly, and researchers apply highlighted remarks and future perspectives of these kinds of platforms for PHC applications.

The authors assembled several microfluidic chips through cell culture and immune-fluorescent, as well as using microscopy measurement and image analysis for RNA sequencing. By this work, several chip assemblies were fabricated, and the application of the fluidic routing mechanism enables us to provide chip-to-chip communication with a variety of tissue-on-a-chip. By lab-on-a-chip techniques, the authors exhibited that coating the cell membrane via poly-dopamine and collagen was the best cell membrane coating due to the monolayer growth and differentiation of the cell types during the differentiation period. The authors found the artificial membrane, through coating with Collagen-A, has improved the growth of mouse podocytes cells-5 compared with the fibronectin-coated membrane.

The authors could distinguish the differences across the patient cohort when they used a collagen-coated microfluidic chip. For instance, von Willebrand factor, a blood glycoprotein that promotes hemostasis, can be identified and measured through these type-coated microfluidic chips.

By focusing on Sustainable Development Goal 12 (SDG 12) and tourism automation, this perspective paper aims to investigate how tourism and automation will work to create a world in which tourism has more sustainable production and consumption patterns.

This perspective paper reviews the past developments of automation in tourism in the context of sustainable production and consumption patterns, the lessons learned from the COVID-19 pandemic and looks at the future of tourism and how automation will help it be more sustainable in terms of consumption and production patterns.

The insights from this analysis suggest that automation technologies will play a major role in both the supply and demand sides of the tourism and hospitality industry, encouraging increased tourism sustainability. While automation technologies will have the greatest impact on the supply side in the near future, as such technologies will be used to minimise waste and energy usage, creating large gains for environmental protection, the technologies will also benefit responsible consumption. Big data and analytical technologies will work in ways to ensure that consumers are nudged into consumer practices that are increasingly sustainable.

This perspective paper synthesises the literature on the subjects, namely, automation and SDG 12 in tourism, and points to important new future research agenda. This is one of the first papers in tourism to blend automation and SDG 12 literature to shed light on the use of automation in sustainable consumption and production in tourism.

通过聚焦于可持续发展目标12和旅游自动化, 本前瞻性文章旨在探讨旅游业和自动化如何共同创造一个让旅游产业拥有更可持续的生产和消费模式的世界。

本文回顾了旅游自动化在可持续生产和消费模式背景下的发展, 从COVID-19大流行中学到的教训, 并展望旅游业的未来以及自动化如何帮助其在消费和生产模式方面变得更加可持续。

根据分析, 自动化技术将在旅游和酒店业的供求两侧发挥重要作用, 促进旅游业的可持续性发展。虽然自动化技术在近期内将对供应侧产生最大影响, 因为这些技术将被用来最小化废物和能源使用, 为环境保护创造巨大收益, 但这些技术也将惠及负责任消费。大数据和分析技术将以确保消费者被引导向越来越可持续的消费实践。

本前瞻性论文综合了关于旅游中的自动化和可持续发展目标12的文献, 并指出了重要的新的未来研究议程。这是旅游业中第一批结合自动化和可持终发展目标12文献以阐明旅游中可持续消费和生产的自动化使用的论文之一。

Al centrarse en el ODS12 y la automatización del turismo, este artículo de perspectiva pretende investigar cómo el turismo y la automatización trabajarán para crear un mundo en el que el turismo tenga unos patrones de producción y consumo más sostenibles.

Este artículo de perspectiva revisa los desarrollos pasados de la automatización en el turismo en el contexto de los patrones de producción y consumo sostenibles, las lecciones aprendidas de la pandemia COVID-19, y examina el futuro del turismo y cómo la automatización le ayudará a ser más sostenible en términos de patrones de consumo y producción.

Las conclusiones de este análisis sugieren que las tecnologías de automatización desempeñarán un papel importante tanto en la oferta como en la demanda de la industria del turismo y la hotelería, fomentando una mayor sostenibilidad del turismo. Mientras que las tecnologías de automatización tendrán el mayor impacto en el lado de la oferta en un futuro próximo, ya que dichas tecnologías se utilizarán para minimizar los residuos y el uso de energía, creando grandes ganancias para la protección del medio ambiente, las tecnologías también beneficiarán al consumo responsable. Los macrodatos y las tecnologías analíticas funcionarán de manera que se incite a los consumidores a adoptar prácticas de consumo cada vez más sostenibles.

Este documento de perspectiva sintetiza la bibliografía sobre los temas, a saber, la automatización y el ODS12 en el turismo, y apunta a una nueva e importante agenda de investigación futura. Se trata de uno de los primeros trabajos sobre turismo que combina la literatura sobre automatización y ODS12 para arrojar luz sobre el uso de la automatización en el consumo y la producción sostenibles en el turismo.

This paper aims to identify the different system approach using Building Information Modelling (BIM) technology that is equipped with decision making processes. Maintenance planning and management are integral components of the construction sector, serving the broader purpose of post-construction activities and processes. However, as Precast Concrete (PC) construction projects increase in scale and complexity, the interconnections among these activities and processes become apparent, leading to planning and performance management challenges. These challenges specifically affect the monitoring of façade components for corrective and preventive maintenance actions.

The concept of maintenance planning for façades, along with the main features of information and communication technology tools and techniques using building information modeling technology, is grounded in the analysis of numerous literature reviews in PC building scenarios.

This research focuses on an integrated system designed to analyze information and support decision-making in maintenance planning for PC buildings. It is based on robust data collection regarding concrete façades' failures and causes. The system aims to provide appropriate planning decisions and minimize the risk of façade failures throughout the building's lifetime.

The study concludes that implementing a research framework to develop such a system can significantly enhance the effectiveness of maintenance planning for façade design, construction and maintenance operations.

This paper recommends a method entitled “SMED 4.0” as a development of conventional single minute exchange of die (SMED) to avoid defect occurrence during production and improve sustainability, besides reducing setup time.

The method builds upon an extensive literature review and in-depth explorative research in SMED and zero defect manufacturing (ZDM). SMED 4.0 incorporates an evolutionary stage that employs predict-prevent strategies using Industry 4.0 technologies including the Internet of Things (IoT) and machine learning (ML) algorithms.

It presents the applicability of the proposed approach in (1) identifying the triple bottom line (TBL) criteria, which are affected by defects; (2) predicting the time of defect occurrence if any; (3) preventing defective products by performing online setting on machines during production as needed; (4) maintaining the desired quality of the product during the production and (5) improving TBL sustainability in manufacturing processes.

The extended view of SMED 4.0 in this research, as well as its analytical approach, helps practitioners develop their SMED approaches in a more holistic way. The practical application of SMED 4.0 is illustrated by implementing it in a real-life manufacturing case.