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3D printing has been warmly welcomed by clothing enterprises for its customization capacity in recent years. However, such clothing enterprises have to face the digital transformation challenges brought by 3D printing. Since the business model is a competitive weapon for modern enterprises, there is a research gap between business model innovation and digital transformation challenges for 3D-printing garment enterprises. The aim of the paper is to innovate a new business model for 3D-printing garment enterprises in digital transformation.

A business model innovation canvas (BMIC), a new method for business model innovation, is used to innovate a new 3D-printing clothing enterprises business model in the context of digital transformation. The business model canvas (BMC) method is adopted to illustrate the new business model. The business model ecosystem is used to design the operating architecture and mechanism of the new business model.

First, 3D-printing clothing enterprises are facing digital transformation, and they urgently need to innovate new business models. Second, mass customization and distributed manufacturing are important ways of solving the business model problems faced by 3D-printing clothing enterprises in the process of digital transformation. Third, BMIC has proven to be an effective tool for business model innovation.

The new mass deep customization-distributed manufacturing (MDC-DM) business model is universal. As such, it can provide an important theoretical reference for other scholars to study similar problems. The digital transformation background is taken into account in the process of business model innovation. Therefore, this is the first hybrid research that has been focused on 3D printing, garment enterprises, digital transformation and business model innovation. On the other hand, business model innovation is a type of exploratory research, which means that the MDC-DM business model’s application effect cannot be immediately observed and requires further verification in the future.

The new business model MDC-DM is not only applicable to 3D-printing garment enterprises but also to some other enterprises that are either using or will use 3D printing to enhance their core competitiveness.

A new business model, MDC-DM, is created through BMIC, which allows 3D-printing garment enterprises to meet the challenges of digital transformation. In addition, the original canvas of the MDC-DM business model is designed using BMC. Moreover, the ecosystem of the MDC-DM business model is constructed, and its operation mechanisms are comprehensively designed.

This study addresses the production optimization of a cellular manufacturing system (CMS) in magnetic production enterprises. Magnetic products and raw materials are more critical to transport than general products because the attraction or repulsion between magnetic poles can easily cause traffic jams. This study needs to address a method to promote the scheduling efficiency of the problem.

To address this problem, this study formulated a mixed-integer linear programming (MILP) model to describe the problem and proposed an auction and negotiation-based approach with a local search to solve it. Auction- and negotiation-based approaches can obtain feasible and high-quality solutions. A local search operator was proposed to optimize the feasible solutions using an improved conjunctive graph model.

Verification tests were performed on a series of numerical examples. The results demonstrated that the proposed auction and negotiation-based approach with a local search operator is better than existing solution methods for the problem identified. Statistical analysis of the experiment results using the Statistical Package for the Social Sciences (SPSS) software demonstrated that the proposed approach is efficient, stable and suitable for solving large-scale numerical instances.

An improved auction and negotiation-based approach was proposed; The conjunctive graph model was also improved to describe the problem of CMS with traffic jam constraint and build the local search operator; The authors’ proposed approach can get better solution than the existing algorithms by testing benchmark instances and real-world instances from enterprises.

In today's fast developing era, the volume of data is increasing day by day. The traditional methods are lagging for efficiently managing the huge amount of data. The adoption of machine learning techniques helps in efficient management of data and draws relevant patterns from that data. The main aim of this research paper is to provide brief information about the proposed adoption of machine learning techniques in different sectors of manufacturing supply chain.

This research paper has done rigorous systematic literature review of adoption of machine learning techniques in manufacturing supply chain from year 2015 to 2023. Out of 511 papers, 74 papers are shortlisted for detailed analysis.

The papers are subcategorised into 8 sections which helps in scrutinizing the work done in manufacturing supply chain. This paper helps in finding out the contribution of application of machine learning techniques in manufacturing field mostly in automotive sector.

The research is limited to papers published from year 2015 to year 2023. The limitation of the current research that book chapters, unpublished work, white papers and conference papers are not considered for study. Only English language articles and review papers are studied in brief. This study helps in adoption of machine learning techniques in manufacturing supply chain.

This study is one of the few studies which investigate machine learning techniques in manufacturing sector and supply chain through systematic literature survey.

1. A comprehensive understanding of Machine Learning techniques is presented.

2. The state of art of adoption of Machine Learning techniques are investigated.

3. The methodology of (SLR) is proposed.

4. An innovative study of Machine Learning techniques in manufacturing supply chain.

A comprehensive understanding of Machine Learning techniques is presented.

The state of art of adoption of Machine Learning techniques are investigated.

The methodology of (SLR) is proposed.

An innovative study of Machine Learning techniques in manufacturing supply chain.

The advent of Internet of Things, cloud computing and advanced computing has endowed smart manufacturing environments with resilience, reconfigurability and intelligence, resulting in the emergence of novel capabilities. These capabilities have significantly reshaped the manufacturing ecosystem, enabling it to effectively navigate uncertainties. The purpose of this study is to assess the operational transformations resulting from the implementation of smart manufacturing, which distinguish it from conventional systems.

A list of qualitative and quantitative smart manufacturing performance metrics (SMPMs) are initially suggested and categorized into strategic, tactical and operational levels. The SMPMs resemble the capabilities of smart manufacturing systems to manage disruptions due to uncertainties. Then, industry and academia experts validate the SMPMs through the utilization of the Delphi method, enabling the ranking of the SMPMs.

The proposition of the SMPMs serves as a metric to assess the digital transformation capabilities of smart manufacturing systems. In addition, the ranking of the proposed SMPMs shows a degree of relevance of the measures in smart manufacturing deployment and managing the disruptions caused due to the COVID-19 pandemic

The findings benefit managers, consultants, policymakers and researchers in making appropriate decisions for deploying and operationalizing smart manufacturing systems by focusing on critical SMPMs.

The research provides a metric to assess the operational transformations during the deployment of smart manufacturing systems. Also, it states the role of the metric in managing the potential disruptions that can alter the performance of the business due to the COVID-19 pandemic.

Additive manufacturing also known as 3D printing is an evolving advanced manufacturing technology critical for the new era of complex machinery and operating systems. Manufacturing systems are increasingly faced with risk of attacks not only by traditional malicious actors such as hackers and cyber-criminals but also by some competitors and organizations engaged in corporate espionage. This paper aims to elaborate a plausible risk practice of designing and demonstrate a case study for the compromised-based malicious for polymer 3D printing system.

This study assumes conditions when a machine was compromised and evaluates the effect of post compromised attack by studying its effects on tensile dog bone specimens as the printed object. The designed algorithm removed predetermined specific number of layers from the tensile samples. The samples were visually identical in terms of external physical dimensions even after removal of the layers. Samples were examined nondestructively for density. Additionally, destructive uniaxial tensile tests were carried out on the modified samples and compared to the unmodified sample as a control for various mechanical properties. It is worth noting that the current approach was adapted for illustrating the impact of cyber altercations on properties of additively produced parts in a quantitative manner. It concurrently pointed towards the vulnerabilities of advanced manufacturing systems and a need for designing robust mitigation/defense mechanism against the cyber altercations.

Density, Young’s modulus and maximum strength steadily decreased with an increase in the number of missing layers, whereas a no clear trend was observed in the case of % elongation. Post tensile test observations of the sample cross-sections confirmed the successful removal of the layers from the samples by the designed method. As a result, the current work presented a cyber-attack model and its quantitative implications on the mechanical properties of 3D printed objects.

To the best of the authors’ knowledge, this is the original work from the team. It is currently not under consideration for publication in any other avenue. The paper provides quantitative approach of realizing impact of cyber intrusions on deteriorated performance of additively manufactured products. It also enlists important intrusion mechanisms relevant to additive manufacturing.

This paper investigates the overall system risk for a foodgrains supply chain capturing the interrelationship among the risk factors and the effect of risk mitigation strategies.

This paper first calculates the weight of risk factors using an integrated approach of failure mode, effects analysis and fuzzy VIKOR technique. Next, the weights are utilized as input for the weighted fuzzy Petri-net (WFPN) approach to calculate the system risk.

Two different WFPN models are developed based on the relationships among the risk factors, and both models demonstrate a higher risk value for the overall system.

The proposed methodology will help practitioners or managers understand the complexity involved in the system by capturing the interrelationship behaviour. This study also considers the concurrent effect of risk mitigation strategies for calculating the overall system risk, which helps to improve the system’s performance.

The purpose of this paper is to investigate, from a thorough review of the literature, the role of metaverse-based quality 4.0 (MV-based Q4.0) in achieving manufacturing resilience (MFGRES). Based on a categorization of MV-based Q4.0 enabler technologies and MFGRES antecedents, the paper provides a conceptual framework depicting the relationship between both areas while exploring existing knowledge in current literature.

The paper is structured as a comprehensive systematic literature review (SLR) at the intersection of MV-based Q4.0 and MFGRES fields. From the Scopus database up to 2023, a final sample of 182 papers is selected based on the inclusion/exclusion criteria that shape the knowledge base of the research.

In light of the classification of reviewed papers, the findings show that artificial intelligence is especially well-suited to enhancing MFGRES. Transparency and flexibility are the resilience enablers that gain most from the implementation of MV-based Q4.0. Through analysis and synthesis of the literature, the study reveals the lack of an integrated approach combining both MV-based Q4.0 and MFGRES. This is particularly clear during disruptions.

This study has a significant impact on managers and businesses. It also advances knowledge of the importance of MV-based Q4.0 in achieving MFGRES and gaining its full rewards.

This paper makes significant recommendations for academics, particularly those who are interested in the metaverse concept within MFGRES. The study also helps managers by illuminating a key area to concentrate on for the improvement of MFGRES within their organizations. In light of this, future research directions are suggested.

The purpose of this paper is to determine if companies in the modular and offsite construction (MOC) industry are agile or not and its level of application for agility principles, which allows for quick responses to the increasingly dynamic nature of industry environments.

This paper proposes an agility assessment framework for MOC that uses 48 assessment attributes organized into four categories: metrics, drivers, enablers and capabilities. A questionnaire approach was used to disseminate the framework globally in 19 countries and synthesize its relevance to the MOC industry. The questionnaire had 55 complete responses, majority of respondents work in managerial positions for MOC manufacturing facilities and onsite general contractors.

It was found that the lowest metric score for adapting to change was for cost since controlling cost would be difficult for any changes required after the design freeze stage. The top agility driver was found to be the need to respond to the wide variety of customer expectations, while the lowest driver was the existence of competing priorities. The top agility enabler was vendor partnership, which can be related to current postpandemic supply chain disruptions. Regarding technological capabilities, Europe and the USA acquired better scores compared to Asia, Latin America and Africa.

This study contributes to the MOC body of knowledge by creating an agility assessment tool for MOC firms to analyze their agile approach and environment, identifying the preliminary importance of agility assessment attributes and determining significant agile differences between the main MOC industry groups.

This paper analyses the effect of circular economy practices on sustainable supply chain performance. The study explores the impact of mediating variables such as supply chain flexibility and capabilities and the moderating role of supply chain integration in the relationship between circular economy practices and sustainable supply chain performance in Indian manufacturing firms. The study builds on the stimulus-organism-response (S-O-R) model to conceptualise circular economy practices that influence supply chain capabilities, integration and flexibility, impacting sustainable supply chain performance.

This study adopted an online survey questionnaire distributed to managers of Indian manufacturing firms adopting circular economy practices. The data were analysed using SPSS Amos 25 and PROCESS macros.

The results suggest a positive impact of circular economy practices on sustainable supply chain performance in manufacturing firms. In addition, a supply chain manager's relationship with retailers is improved in the presence of supply chain capabilities and flexibility. Supply chain integration further strengthens this relationship as a moderating variable.

By examining the literature on circular economy practices and sustainable supply chain management, this study contributes to bridging the gap between supply chain capabilities, integration and flexibility using the S-O-R model. This study is possibly among the first to explore and provide empirical evidence on how circular economy practices in manufacturing firms can impact supply chain managers' experiences and thus help to improve environmental well-being. Both academics and business professionals might find these contributions interesting.

Despite the growing attention on the relevance of improved building management systems with cognition in recent years in the architecture, engineering, construction and operation (AECO) community, no review has been conducted to understand the human-environment interaction features of cyber-physical systems (CPS) and digital twins (DTs) in developing the concept of a cognitive building (CB). Thus, this paper aims to review existing studies on CPS and DTs for CB to propose a comprehensive system architecture that considers human-environment interactions.

Scientometric analysis and content analysis were adopted for this study.

The scientometric analysis of 1,042 journal papers showed the major themes of CPS/DTs for CB, and these can be categorized into three key technologies to realize CB in the AECO community: CPS, DTs and cognitive computing (CC). Content analysis of 44 relevant publications in the built environment assisted in understanding and evidently confirming the claim of this study on the integration of CPS and DTs for CB in construction by also involving the CC. It is found and confirmed that CB can be realized with CPS and DTs along with the CC. A CB system architecture (CBSA) is proposed from the three key technologies considering the human-environment interactions in the loop. The study discovered the potential applications of the CBSA across the building lifecycle phases, including the design, construction and operations and maintenance, with the potential promise of endowing resilience, intelligence, greater efficiency and self-adaptiveness. Based on the findings of the review, four research directions are proposed: human-environment interactions, CB for sustainable building performance, CB concept for modular buildings and moving beyond CB.

This study stands out for comprehensively surveying the intellectual core and the landscape of the general body of knowledge on CPS/DTs for CB in the built environment. It makes a distinctive contribution to knowledge as it does not only propose CBSA by integrating CPS and DTs along with CC but also suggests some potential practical applications. These may require expert judgments and real case examples to enhance reproducibility and validation.