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Connected and automated vehicles (CAV) are increasingly recognized as a critical component of intelligent transportation systems (ITS), contributing to advances in transportation safety and mobility. However, the implementation of CAV in a real-world environment comes with various threats, and cybersecurity is among the most vulnerable. As the technology becomes more advanced and complex, it is essential to develop a comprehensive cybersecurity framework that can address these concerns. This research proposes a novel framework based on complexity theory and employs the fuzzy set qualitative comparative analysis (fsQCA) technique to identify combinations of security attacks that lead to achieving cybersecurity in CAV. Compared to structural equation modelling (SEM), the fsQCA method offers the advantage of demonstrating all possible ways to achieve the outcome. The study’s findings suggest that in-vehicle networks and data storage security are the most crucial factors in ensuring the cybersecurity of CAV. The results can be useful for automotive designers in reducing the potential for attacks while developing secure networks.

This study is an exhaustive investigation into the role of additive manufacturing in achieving sustainable development goals (SDGs). Due to its compatibility with intricate structures, additive manufacturing is widely utilised in a variety of industry sectors. This paper intends to examine the various opportunities and significance of additive manufacturing for the advancement of the SDGs. In addition, a number of engineering materials referred to as metals and alloys, ceramics, polymers, and composites are the subject of extensive research to enhance the role of additive manufacturing in the attainment of SDGs. This study demonstrates that sustainable development via additive manufacturing can be achieved through the route of: (a) circular economy, (b) life cycle assessment, (c) environmental aspects, and (d) recycled materials. Preferred Reporting Items for Systematic Reviews and Meta Analysis (PRISMA) framework is utilized to perform a systematic literature review. Despite the fact that the research demonstrates a clear improvement in waste reduction, environmental friendliness, and energy consumption reduction, it cannot be denied that significant progress remains to be made in these areas.

The fourth industrial revolution, commonly recognized as Industry 4.0, has been ushered by modern and innovative intelligence and communication technologies. Concerns about disruptive technologies (DTs) are beginning to grow in developing countries, despite the fact that the trade-offs between implementation difficulties and realistic effects are still unknown. Hence, prioritization and promotion of such technologies should be considered when investing in them to ensure sustainability. The study aims to provide new critical insights into what DTs are and how to identify the significant DTs for sustainable supply chain (SSC). Understanding the DTs’ potential for achieving holistic sustainability through effective technology adoption and diffusion is critical. To achieve the goal, an integrated approach combining the Bayesian method and the Best Worst Method (BWM) is utilized in this study to evaluate DTs in emerging economies' supply chain (SC). The systematic literature review yielded a total of 10 DTs for SSC, which were then evaluated using the Bayesian-BWM to explore the most critical DTs for a well-known example of the readymade garment (RMG) industry of Bangladesh. The results show that the three most essential DTs for SSC are “Internet of things (IoT)”, “Cloud manufacturing”, and “Artificial intelligence (AI)”. The research insights will facilitate policymakers and practitioners in determining where to concentrate efforts during the technology adoption and diffusion stage in order to improve sustainable production through managing SC operations in an uncertain business environment.

Offshore wind turbine (OWT), a sustainable energy source, has recently gained wide attention. The energy demand for India is soaring high as it is a fast-developing nation in terms of industrialization; however, the interest shown by India toward renewable energy is low, especially for OWTs. This study aims to identify, categorize, and evaluate the criteria needed to be considered in the installation of OWTs and selection of potential locations in India. Based on literature analysis and exploratory interviews with experts, six aspects, namely, climatic conditions, regional features, investments and benefits, environmental impact, economic impact, and social and technical impact, with a total of twenty-six criteria, were identified and evaluated. An integrated approach of data envelopment analysis (DEA) with grey analytical hierarchy process (GAHP) and grey Complex proportional assessment (GCOPRAS) is used to evaluate the criteria and also to identify the locations for OWTs. Soil condition, extreme wind speed, seismic movement, tidal flow, and closeness to the power transmission grid have been identified as the top five criteria to be considered in the installation of OWTs. Gujarat, Tamil Nadu, Odisha, the Lakshadweep Islands, and the Andaman and Nicobar Islands have been identified as potential locations for installing OWTs in India. The outcomes of this study will deliver better insights for the practitioners about the criteria that need to be considered in OWTs. Further, this study sheds light on the importance of OWTs in an Indian context, which can possibly attract more investments.

Today, supply chain (SC) networks are facing more disruptions compared to the past. While disruptions are rare, they can have catastrophic long-term economic or societal repercussions, and the recovery processes can be lengthy. These can tremendously affect the SC and make it vulnerable, as observed during the COVID-19 pandemic. The identification of these concerns has prompted the demand for improved disruption management by developing resilient, agile, and adaptive SC. The aim of this study is to introduce an assessment framework for prioritizing and evaluating the determinants to supply chain resilience (SCR). To analyze the empirical data, fuzzy criteria importance through intercriteria correlation (fuzzy CRITIC) and fuzzy technique for order of preference by similarity to ideal solution (fuzzy TOPSIS) have been incorporated. Fuzzy CRITIC method was used to identify the critical determinants and fuzzy TOPSIS method was applied for determining relative ranking of some real-world companies. Finally, by developing propositions an interpretive triple helix framework was proposed to achieve SCR. This research stands out for its originality in both methodology and implications. By introducing the novel combination of Fuzzy CRITIC and Fuzzy TOPSIS in the assessment of determinants to SCR and applying these determinants with the help of interpretive triple helix framework to establish a resilient SC, this study offers a unique and valuable contribution to the field of SCR. The key findings suggest that 'Responsiveness' followed by 'Managerial coordination and information integration' are the most significant determinant to achieve SCR. The outcome of this work can assist the managers to achieve SCR with improved agility and adaptivity.

Emergy Accounting (EMA) has gained attention in recent times as an environmental accounting tool. So far, it has been used for analysis at national and regional levels; however, the EMA has not been utilised in a business performance analysis context. When used in a business context, EMA reveals the interface between industrial activity and the environment. EMA analysis can provide opportunities in measuring the social, economic, and environmental performance of the industry in a sustainability context. Unlike other evaluation methods, which are ‘demand-side’ or ‘user-side’ concerned, EMA analysis is a ‘donor-side’ approach. This research work illustrates the selection of suppliers based on the amount of emergy consumption. Emergy indicators, such as Emergy Yield Ratio (EYR), Emergy Sustainability Index (ESI), Environmental Loading Ratio (ELR), Emergy Self-support Ratio (ESR), Renewability percent (%R), Emergy flow Density (ED), and Emergy Investment Ratio (EIR) are estimated. The results show that there is enough room for improving emergy performance in the tannery industry. The result also highlights a significant variance in the input of indigenous renewable and non-renewable resources and imported resources among the investigated tanneries. Based on the outcomes, the study provides insights on the industries’ consumption of renewable and non-renewable resources.

Robust and resilient agri-food supply chain management (AFSCM) is paramount to agribusinesses, given the many challenges and risks that this increased demand will bring in the coming decades. Interruptions caused by various risks to this crucial supply chain network, particularly in emerging economies, can put the lives of millions in danger, not to mention creating devastating impacts on the economy and the environment. Even so, there are only a limited number of quantitative risk management studies in the AFSCM literature. In this study, an integrated modified risk mitigation matrix (M-RMM) is developed to analyze the mitigation strategies for dealing with various risks in the context of the agri-food supply chain. The M-RMM is integrated with the grey multi-objective binary linear programming (GMOBLP) model to obtain the optimal risk mitigation strategies related to the three objective functions of risk, cost, and time minimization. The proposed model is a useful tool for formulating sustainable business policies and reducing food waste, and acquiring a context-specific (i.e., a developing economy), sector-specific (i.e., the agri-food processing sector), and multi-product (i.e., fresh and non-perishable) approach. The findings reveal that continuous training and development and vulnerability analysis of IT systems are the most effective risk mitigation strategies to lessen the impacts of lack of skilled personnel, sub-standard leadership, failure in IT systems, insufficient capacity to produce quality products, and poor customer relationships. The findings assist practitioners in managing risks in supply chains.

Recently, Circular Economy (CE) is implemented by the manufacturing industries since it helps the management to reduce waste and increase productivity. Industries are adopting CE because it helps them gain economic, environmental, and social benefits. This study is primarily aimed at the identification and ranking of the drivers and barriers for the implementation of CE in the automobile industry. For this, an integrated approach of interpretive structural modeling (ISM) and decision-making trial and evaluation laboratory (DEMATEL) is utilized. The results of this study indicate that the share/ benefit and reduction of cost are the most critical drivers while unaware/limited knowledge and cost and financial constraint are the major barriers for the implementation of CE in the automobile industry. This integrated approach will help the decision makers and policy makers to take immediate and effective actions, and focus on the productivity of the company.

Sustainable waste management (SWM) is an important goal for the plastic manufacturing industry, which is aiming toward enhancing its economic, environmental, and social sustainability in recent years. However, plastic manufacturing industries in developing countries like Bangladesh are yet to establish efficient SWM systems despite the amount of solid plastic waste continually rising across all sectors. This paper investigates the underexplored barriers to implementing SWM in the Bangladeshi plastic industry. First, it employs the Delphi method to identify the 16 most relevant barriers to SWM through a literature review and expert feedback. Then, using an integrated grey theory and decision-making trial and evaluation laboratory (DEMATEL) method-based framework, it evaluates these 16 barriers to examine their interrelationships in the context of emerging economies. The findings show that the five most significant barriers are as follows: lack of awareness of and knowledge about plastic waste disposal, lack of compliant waste management, lack of a sense of environmental responsibility on the part of manufacturers, lack of systematic waste collection and effective dumping-ground utilization and lack of proper environmental policies, legislations, and regulations. This study presents valuable insights that can deeply contribute to the literature on SWM, enabling scholars, policymakers, and industrial managers to practice sustainability more effectively in the plastic manufacturing industry.

Recent disruptions in global and local supply chains (SCs) due to manmade and natural disasters have drawn a lot of attention to academics and practitioners. Such disruptions are often characterized by a lack of insufficient data. To tackle such a data scarce supply chain ecosystem, this article examines the potential disruption risks in SCs under insufficient input data. For this, a decision support system (DSS) based on the Bayesian hierarchical approach and value at risk (VaR) reduction analysis is proposed to assess a supplier's disruption risk events probability as well as the supplier revenue impact on a company of interest. Empirical data are used to examine the DSS. The findings show that the DSS is effective in generating the suppliers’ risk profiles. The proposed DSS can be utilized by supply chain managers and practitioners to manage SC disruption risks in a more efficient manner.

The ongoing COVID-19 pandemic has manifold disastrous effect on different service and production sectors, and manufacturing industries are no exception. Emerging technologies (ETs) can play a pivotal role in reviving these ailing manufacturing industries. However, the cost of implementing and operating ETs is a prime concern. Nonetheless, the important attributes that will minimize the cost and harness the advantages of the technology are still to be explored. To address this gap, this research seeks to examine the critical attributes required for the effective and efficient deployment of ETs. At first, a detailed literature review was conducted to identify and sort the attributes influencing the effective use of ETs in manufacturing industries. After that, Fuzzy-TODIM (Portuguese abbreviation of “Interactive and Multi-Criteria Decision Making”) method was used to rate the importance of the attributes. The result reveals that, “Inventory and Resource Management” tops the attribute list responsible for exploiting the optimal usage ETs, followed by “Development of Skilled Workforce” and “Supplier and Service Management Capability,” respectively. This paper will assist industry professionals not only in using ETs but also getting the best yield from them strategically and practically.

The green economy (GE) concept is believed to have the ability to turn natural resources into wealth in a sustainable manner. As a result, the GE concept is viewed as a magic key to the attainment of sustainable development goals (SDGs). This study aims to identify and evaluate the critical factors in GE practices. Based on literature review and industrial interaction, five dimensions of critical factors, namely, economic and market, technical and R&D, policy and regulation, networks and social capital, and public perceptions, with a total of 20 critical factors were identified and evaluated. The Fermatean fuzzy system (FFS)-based decision-making trial and evaluation laboratory (DEMATEL) technique has been used to evaluate the causality among the critical factors. Results reveal that win-win anticipations, the commitment of key stakeholders, the domestic market structure, cost of alternatives, and financial assistance are the top five critical factors in GE practices. Three critical factors are classified under the cause category, and the remaining 17 critical factors come under the effect category. This study contributes to the literature on GE by revealing the causal interrelationship among the critical factors. This will guide industrial management to take appropriate actions in the implementation of GE practices.

This study aims to examine the causes of boiler accidents in the context of the ready-made garment (RMG) industry of Bangladesh as an emerging economy. On the basis of a comprehensive review of the existing literature, previous accident reports, and technical discussion with relevant personnel in the industries and regulating authorities, a total of 14 causes of boiler accidents were identified. This study merged neutrosophic (N) theory with the analytic hierarchy process (AHP) for prioritizing the causes of boiler accidents. Finally, to examine the reliability of the results, a robustness analysis was performed. The findings reveal that the lack of standard legislation, non-standard boiler operation, use of expired, non-registered, and non-certified boilers, faulty design of boilers, and the shortage of skilled boiler operators are the top five notable causes of boiler accidents in the RMG industry. The findings provide valuable insights for industrial managers and policymakers to formulate strategies to reduce boiler accidents.

Appropriate suppliers are essential for optimizing cost in contemporary supply chain decisions. Supplier selection is a challenging issue that requires the evaluation of both quantitative and qualitative criteria based on imprecise and limited information. This study proposes a framework to assess sustainable supplier selection methods using a fuzzy analytical hierarchy process (FAHP) and the preference ranking organization method for enrichment evaluation (PROMETHEE). Based on a literature review and expert opinions, 20 sustainable supplier selection criteria were grouped into economic, environmental, social, and transportation dimensions. FAHP was then used to determine the weights of the criteria and PROMETHEE was used to rank the suppliers. The proposed framework was implemented in a ready-made garment company in Bangladesh. The findings showed that ‘price of product,’ ‘profit on product,’ ‘quality of product,’ ‘environmental management system,’ and ‘green packaging and labeling’ were the top five sustainable supplier selection criteria. Business professionals and managers will benefit from this study when identifying suppliers to ensure sustainability in the supply chain.

The increasing use of Information Technology (IT) has led to many security and other related failures in the banks and other financial institutions in Bangladesh. In this paper, we investigated the factors contributing to the failurein the IT system of the banking industry in Bangladesh. Based on the experts’ opinions and weight on the specified evaluating criteria, an empirical test was conducted using a rough set theory to produce a framework for the IT system failure factors. In this study, an extended approach involving the integration of rough set theory based flexible Failure Mode and Effect Analysis (FMEA) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) has beenapplied to help the managers of the corresponding field to identify the factors responsible for the failure of the IT system in the banking industries and then prioritize them accordingly, for the ease of decision-making.In this research, eleven such failure factors were identified, which were then quantitatively analyzed to facilitate managers in crucial decision-making. It was observed that cyber-attack, database hack risks, server failure, network interruption, broadcast data error, and virus effect were the most significant factors for the failure of the IT system. The framework developed in this research can be utilized to assist in efficient decision-makingin other serviceindustries where IT systems play a key role. To the best of the knowledge, this is the first study thatempirically tested key failure factors of the IT system for the banking sector using an integrated method.

This paper aims to identify, evaluate, and measure the ergonomic factors hampering the production of leather garment-based small and medium-sized enterprises (SMEs). Ergonomic problems faced by the workers largely impact the health of individuals and also the productivity of a firm. Based on experts’ opinions and a literature survey, three emerging categories—namely, occupational disease, personal factors, and the industrial environment—with a total of twenty factors were identified to examine symmetrical impact in five leather garment companies. In this research work, Cronbach’s α was evaluated to check the validity of the ergonomic factors identified through the literature survey. Then, using the fuzzy analytic hierarchy process (FAHP), the identified ergonomic factors were evaluated. A sensitivity analysis was carried out to validate the robustness of the results obtained using the integrated approach. Outdated machinery, vibration, operational setup, fatigue, and poor ventilation and lighting are the top five factors inducing ergonomic-related problems and hampering the production of the leather garment companies in India. These top ergonomic factors are the result of a failure in the provision of an ambient working environment. Providing ergonomically designed working environments may lower the occurrence of ergonomic problems. The findings of this study will assist industrial managers to enhance production rate and to progress towards social sustainability in Indian SMEs. The proposed symmetrical assessment in this study could also be considered as a benchmark for other companies in which human–machine interaction is significant.

Measuring productivity is the systematic process for both inter- and intra-organizational comparisons. The productivity measurement can be used to control and facilitate decision-making in manufacturing as well as service organizations. This study’s objective was to develop a decision support framework by integrating an analytic network process (ANP) and data envelopment analysis (DEA) approach to tackling productivity measurement and benchmarking problems in a manufacturing environment. The ANP was used to capture the interdependency between the criteria taking into consideration the ambiguity and vagueness. The nonparametric DEA approach was utilized to determine the input-oriented constant returns to scale (CRS) efficiency of different value-adding production units and to benchmark them. The proposed framework was implemented to benchmark the productivity of an apparel manufacturing company. By applying the model, industrial managers can gain benefits by identifying the possible contributing factors that play an important role in increasing the productivity of manufacturing organizations.