Search results

This paper aimed to identify the impact that Industry 4.0 can have on the service sector and how its combination can generate benefits for Sustainability 4.0 in the context of the Triple Bottom Line.

A Systematic Literature Review (RSL) was developed using the following combinations of keywords on the Web of Science and Scopus databases: Industry 4.0 And Sustainability, Industry 4.0 And Service, and Industry 4.0 And Sustainable Development, in which 24 papers were selected.

As a result, the authors found 100 benefits for sustainable development, which were further categorized into 54 advantages for the economic area, 25 for the social segment and 21 for environmental aspects. In this way, the results can help private companies understand the use of these technologies aimed at sustainable growth, plus bolstering the government to conduct public policies to encourage these practices in technology organizations.

As a practical implication, this study offers insight into the evolution of sustainable development. It enables the emergence of works that wish to explore the service sector, providing relevant information for decision-making and influencing managers and policymakers on the importance of applying Industry 4.0 toward the sustainability of the service sector and its applications.

The originality of this study lies in expanding the understanding generated by 4.0 technologies, by diverting attention from the manufacturing and agricultural sectors, which have abundant literature on the subject. With this, this work demonstrated that modern technologies have greater possibilities of an action directed at the service sector, in addition to being able to contribute to Sustainability 4.0 from the perspective of the sustainability tripod.

Existing labor estimation models typically consider only certain construction project types or specific influencing factors. These models are focused on quantifying the total labor hours required, while the utilization rate of the labor during the project is not usually accounted for. This study aims to develop a novel machine learning model to predict the time series of labor resource utilization rate at the work package level.

More than 250 construction work packages collected over a two-year period are used to identify the main contributing factors affecting labor resource requirements. Also, a novel machine learning algorithm – Recurrent Neural Network (RNN) – is adopted to develop a forecasting model that can predict the utilization of labor resources over time.

This paper presents a robust machine learning approach for predicting labor resources’ utilization rates in construction projects based on the identified contributing factors. The machine learning approach is found to result in a reliable time series forecasting model that uses the RNN algorithm. The proposed model indicates the capability of machine learning algorithms in facilitating the traditional challenges in construction industry.

The findings point to the suitability of state-of-the-art machine learning techniques for developing predictive models to forecast the utilization rate of labor resources in construction projects, as well as for supporting project managers by providing forecasting tool for labor estimations at the work package level before detailed activity schedules have been generated. Accordingly, the proposed approach facilitates resource allocation and enables prioritization of available resources to enhance the overall performance of projects.

With Industry 4.0 and the extensive rise of smart technologies, we are seeing remarkable transformations in work practices and workplaces. Scholars report the phenomenal progress of smart technologies. At the same time, we can hear the rhetoric emphasising their potential threats. This study focusses on how and where intelligent machines are leveraged in the workplace, how humans co-working with intelligent machines are affected and what they believe can be done to mitigate the risks of the increased use of intelligent machines.

We conducted in-depth interviews with 15 respondents working in various leadership capacities associated with intelligent machines and technologies. Using NVivo, we coded and churned out the themes from the qualitative data collected.

This study shows how intelligent machines are leveraged across different industries, ranging from chatbots, intelligent sensors, cognitive systems and computer vision to the replica of the entire human being. They are used end-to-end in the value chain, increasing productivity, complementing human workers’ skillsets and augmenting decisions made by human workers. Human workers experience a blend of positive and negative emotions whilst co-working with intelligent machines, which influences their job satisfaction level. Organisations adopt several anticipatory strategies, like transforming into a learning organisation, identifying futuristic technologies and upskilling their human workers, regularly conducting social learning events and designing accelerated career paths to embrace intelligent technologies.

This study seeks to understand the emotional and practical implications of the use of intelligent machines by humans and how both entities can integrate and complement each other. These insights can help organisations and employees understand what future workplaces and practices will look like and how to remain relevant in this transformation.

The paper explores the managerial perceptions in Indian apparel manufacturing firms related to production performance, challenges faced, causes of low efficiency and the government support needed.

A structured survey of Indian apparel manufacturing firms was undertaken in person and through the online mode; the questionnaire was designed to collect data on demographic profile of a firm using categorical questions and perceptions of its top managers using a five-point Likert scale.

The survey findings reveal that most apparel manufacturing firms believe that exporting promotes efficiency and adopt output orientation to production, which may not be suitable in a competitive and uncertain environment. Machines are not used much for value-addition and labour related issues are most pressing challenges. Government support is expected for several aspects such as power supply and skill development.

The paper is limited by the nature of the sampling method and sample size; perceptions should be explored without bias and with good judgement.

The survey findings suggest that government policy should have a firm-specific approach to support improved production performance along with generic policies to build infrastructure and logistical facilities.

To the best of authors’ knowledge, there has been no such exercise to study managerial perceptions related to production performance in Indian apparel manufacturing in the past decade.

Forecasting population movement trends is crucial for implementing effective policies to regulate labor force growth and understand demographic changes. Combining migration theory expertise and neural network technology can bring a fresh perspective to international migration forecasting research.

This study proposes a conditional generative adversarial neural network model incorporating the migration knowledge – conditional generative adversarial network (MK-CGAN). By using the migration knowledge to design the parameters, MK-CGAN can effectively address the limited data problem, thereby enhancing the accuracy of migration forecasts.

The model was tested by forecasting migration flows between different countries and had good generalizability and validity. The results are robust as the proposed solutions can achieve lesser mean absolute error, mean squared error, root mean square error, mean absolute percentage error and R² values, reaching 0.9855 compared to long short-term memory (LSTM), gated recurrent unit, generative adversarial network (GAN) and the traditional gravity model.

This study is significant because it demonstrates a highly effective technique for predicting international migration using conditional GANs. By incorporating migration knowledge into our models, we can achieve prediction accuracy, gaining valuable insights into the differences between various model characteristics. We used SHapley Additive exPlanations to enhance our understanding of these differences and provide clear and concise explanations for our model predictions. The results demonstrated the theoretical significance and practical value of the MK-CGAN model in predicting international migration.

As an advanced manufacturing method, additive manufacturing (AM) technology provides new possibilities for efficient production and design of parts. However, with the continuous expansion of the application of AM materials, subtractive processing has become one of the necessary steps to improve the accuracy and performance of parts. In this paper, the processing process of AM materials is discussed in depth, and the surface integrity problem caused by it is discussed.

Firstly, we listed and analyzed the characterization parameters of metal surface integrity and its influence on the performance of parts and then introduced the application of integrated processing of metal adding and subtracting materials and the influence of different processing forms on the surface integrity of parts. The surface of the trial-cut material is detected and analyzed, and the surface of the integrated processing of adding and subtracting materials is compared with that of the pure processing of reducing materials, so that the corresponding conclusions are obtained.

In this process, we also found some surface integrity problems, such as knife marks, residual stress and thermal effects. These problems may have a potential negative impact on the performance of the final parts. In processing, we can try to use other integrated processing technologies of adding and subtracting materials, try to combine various integrated processing technologies of adding and subtracting materials, or consider exploring more efficient AM technology to improve processing efficiency. We can also consider adopting production process optimization measures to reduce the processing cost of adding and subtracting materials.

With the gradual improvement of the requirements for the surface quality of parts in the production process and the in-depth implementation of sustainable manufacturing, the demand for integrated processing of metal addition and subtraction materials is likely to continue to grow in the future. By deeply understanding and studying the problems of material reduction and surface integrity of AM materials, we can better meet the challenges in the manufacturing process and improve the quality and performance of parts. This research is very important for promoting the development of manufacturing technology and achieving success in practical application.

This study aims to conduct a systematic literature review and bibliometric analysis on the topic of digital labour. The study also identifies the future research directions for the topic.

In total, 118 research papers were identified and reviewed from 11 established research databases and A*, A and B category journals from the ABDC journal list. The papers covered a timespan between 2006 and 2023. Bibliometric analysis was conducted to identify key research hotspots.

The emergent themes and associated sub-themes related to digital labour were identified from the literature. The paper found three significant themes that include digital labour platform, gig economy and productivity. This study also acts as a platform to initiate further research in this field for academicians, scholars, industry practitioners and policymakers. The future research scope in the topic is also presented.

The present study is unique in its nature as it approaches the topic of digital labour from all relevant perspectives.

This research aims to enhance the operational excellence and continuous improvement of the retail supply chain in the Saudi Thobe Factory through an integrated approach of Six Sigma DMAIC (Define-Measure-Analyze-Improve-Control) with artificial intelligence (AI).

The study identified the tailoring department as the department with maximum defects by using voice of customer and critical to quality tools. An AI-integrated Six Sigma approach was applied to identify and eliminate nonproductive stages, and a new facility layout was designed to enhance productivity and customer satisfaction.

The use of the factor rating method and simulation using Arena software led to an improved sigma level from 1.597 to 2.237, representing an increment of about 40%. Additionally, the defects per million opportunities reduced from 461,538 to 230,769. The study can help production industry management to optimize facility layouts and improve overall production line efficiency.

This study addresses the lack of published research on the use of an integrated approach of Six Sigma DMAIC with AI in the retail and distribution sector of Saudi Arabia, particularly for small and medium-sized enterprises (SMEs). The study demonstrates how this approach may significantly boost SMEs’ performance and provides a basis for future research in this area.

This study provides a practical example of how an integrated approach of Six Sigma DMAIC with AI can be used in the retail and distribution sector of Saudi Arabia to enhance operational excellence and continuous improvement. The study highlights the potential benefits of this approach for SMEs in the region and provides a framework for future research.

Precast wall lifting during prefabricated building construction faces multiple non-lean problems, such as inaccurate lifting-time estimation, unreasonable resource allocation and improper process design. This study aims to identify the pathways for improving lifting performance to advance lean construction of prefabricated buildings.

This study developed a methodological framework that integrates the discrete event simulation method, the elimination, combination, rearrangement and simplification (ECRS) technique and intelligent optimization tool. Two schemes of precast wall lifting, namely, the enterprise's business as usual (BAU) and enterprise-leading (EL) schemes, were set to benchmark lifting performance. Furthermore, a best-practice (BP) scheme was modeled from the perspective of lifting activity ECRS and resource allocation for performance optimization.

A real project was selected to test the effect of the methodological framework. The results showed that compared with the EL scheme, the BP scheme reduced the total lifting time (TLT) by 6.3% and mitigated the TLT uncertainty (the gap between the maximum and minimum time values) by 20.6%. Under the BP scheme, increasing the resource inputs produces an insignificant effect in reducing TLT, i.e. increasing the number of component operators in the caulking subprocess from one to two only shortened the TLT by 3.6%, and no further time reduction was achieved as more component operators were added.

To solve non-lean problems associated with prefabricated building construction, this study provides a methodological framework that can separate a typical precast wall lifting process into fine-level activities. Besides, it also identifies the pathways (including the learning effect mitigation, labor and machinery resource adjustment and activities’ improvement) to reducing TLT and its uncertainty.

With the development of science and technology, more creators are trying to use new crafts to represent the cultural trends of the social media era, which makes cultural heritage innovative and new genres emerge. This compels the academic community to examine craft from a new perspective. It is very helpful to understand the hidden representational structure of craft more deeply and improve the craft innovation system of cultural and creative products that we deconstruct the craft based on Complex Network and discover its intrinsic connections.

The research crawled and cleaned the craft information of the top 20% products on the Forbidden City’s cultural and creative products online and then performed Complex Network modeling, constructed three craft representation networks among function, material and technique, quantified and analyzed the inner connections and network structure of the craft elements, and then analyzed the cultural inheritance and innovation embedded in the craft representation networks.

The three dichotomous craft representation networks constructed by combining function, material and technique: (1) the network density is low and none of them has small-world characteristics, indicating that the innovative heritage of the craft elements in the Forbidden City’s cultural and creative products is at the stage of continuous exploration and development, and multiple coupling innovation is still insufficient; (2) all have scale-free characteristics and there is still a certain degree of community structure within each network, indicating that the coupling innovation of craft elements of the Forbidden City’s cultural and creative products is seriously uneven, with some specific “grammatical combinations” and an Island Effect in the network structure; (3) the craft elements with high network centrality emphasize the characteristics of decorative culture and design for the masses, as well as the pursuit of production efficiency and economic benefits, which represent the aesthetic purport of contemporary Chinese society and the ideological trend of production and life.

The Forbidden City’s cultural and creative products should continue to develop and enrich the multi-coupling innovation of craft elements, clarify and continue their own brand unique craft genes, and make full use of the network important nodes role.