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The purpose of this study is to identify the risks that exist in halal meat supply chain activities and to carry out a risk assessment using the fuzzy best-worst method (FBWM) along with mitigating risks using the risk mitigation number (RMN).

The method used is to collect several literature reviews related to the halal meat supply chain, which has information relevant to the risks of the meat industry in Indonesia. Then, a focus group discussion was held with several experts who play a role in the meat industry in Indonesia, and 33 identified risks were identified in halal meat supply chain activities. The proposed methodology uses FBWM and RMN in conducting risk assessment and mitigation in the meat industry in Indonesia.

The analysis reveals that priority risk is obtained by using the global weight value on the FBWM, and then risk mitigation is carried out with RMN. Priority mitigation strategies can mitigate some of the risks to the meat industry in Indonesia. The proposed mitigation strategy is designed to be more effective and efficient in preventing risks that can interfere with product halalness in halal meat supply chain activities in the Indonesian meat industry.

The implications of this study highlight the need for collaboration among stakeholders, improved risk assessment methodologies and the expansion of research into other halal supply chains. By addressing these implications, the halal industry can enhance its integrity, consumer confidence and overall contribution to the global market.

This research provides an integrated approach to identifying, analyzing, assessing and mitigating risks to the meat industry in Indonesia.

This paper analyses the voice of customers (VoCs) using a hybrid clustering multi-criteria decision-making (MCDM) approach. The proposed method serves as an efficient tool for how to approach multiple decision-making involving a large set of countrywide customer complaints in the Iranian automotive sector.

The countrywide data comprising 3,342 customer complaints (VoCs) were gathered. A total of seven determinant complaint criteria were identified in brainstorming sessions with three groups (six each) of experts employing the fuzzy Delphi method. The weights of these criteria were assigned by applying the fuzzy best–worst method (FBWM) to identify the severity of the complaints. Subsequently, the complaints were clustered into five categories with respective customer locations (province), car type and manufacturer using the K-mean method and further prioritised and ranked employing the fuzzy complex proportional assessment of alternatives (FCOPRAS) method.

The results indicated that the majority of complaints (1,027) from the various regions of the country belonged to one specific model of car made by a particular producer. The analyses revealed that only a few complaints were related to product quality, with the majority related to service and financial processes including delays in automobile delivery, delays in calculating monthly instalments, price variation, failure to provide a registration ( licence) and failure to supply the agreed product. The proposed method is an efficient way to solve large-scale multidimensional problems and provide a robust and reliable set of results.

The proposed method makes it much easier for management to deal with complaints by significantly reducing their number. The highest-ranked complaints from customers of the car industry in Iran are those related to delivery time, price alternations, customer service support and quality issues. Surveying the list of complaints shows that paying attention to the four most voiced complaints can reduce them more than 54%. Management can make appropriate strategies to improve the production quality as well as business processes, thus producing a significant number of customer complaints.

This paper proposes a comprehensive approach to critically analyse the VoCs by combining qualitative and decision-making approaches including K-mean, FCOPRAS, fuzzy Delphi and FBWM. This is the first paper that analyses the VoCs in the automotive sector in a developing country’s context involving large-scale decision-making problem-solving.

Block chain technology (BCT) has emerged as a promising solution for the co-ordination and aid mechanism issues in the context of humanitarian supply chain (HSC). However, implementation of BCT in HSC discerns several barriers. Therefore, the purpose of this study is to identify and model the block chain implementation barriers in the context of HSC.

In the present study, 14 potential barriers to BCT adoption in HSC have been identified through literature survey. The survey comprises white papers, pilot studies, conference proceedings and journal articles. Further, the identified barriers were finalised in consultation with a team of experts. The team comprised experienced stakeholders working in the humanitarian domain and BCT development. The barriers were categorised into four (technological, organisational, exogenous and economic) perspectives adopting the kappa statistics. Further, the barriers were prioritised using fuzzy best worst method (FBWM) approach. Later, sensitivity analysis was performed to check the robustness and viability of the model.

The findings from the study indicate that the barriers, such as “data privacy, ownership, and security issues” (B1), “funding issues and cost complexity” (B3) and “technological complexities” (B8), are relatively more influential. The HSC stakeholders and BCT developers are required to identify the safety mechanism against the misuse of victim’s data. The funding issues and technological complexities are interrelated and need synergetic cooperation between blockchain developers, donors, humanitarian organisations (HOs) and other HSC stakeholders. Further, “lack of awareness and understanding among stakeholders” (B6) and “interoperability, collaboration and cross-pollination among HOs” (B5) were identified as least influential barriers to BCT adoption in HSC.

In literature, limited study has been observed on determining barriers to BCT implementation. A more systematic method and statistical confirmation is necessary to establish further new confronting barriers. This study is limited to Indian context.

To the best of the authors’ knowledge, this study is first of its kind to use an FBWM approach for prioritising the barriers to BCT adoption in the context of HSC. The study provides potential barriers to BCT and categorises them into four different perspectives, along with their degree of influence.

The balancing of sustainability dimensions is the prime agenda of supply chain organisations. The supply chain sustainability greatly influenced by its freight transport activities. Most of the previous work discussed the economic and environmental sustainability of freight transport industry; although the social sustainability (SS) dimension paid less attention to researchers and practitioners of emerging economies. The purpose of this study is to investigate the importance of SS indicators in the freight transport industry. The SS assessment framework is validated with the Indian freight transport industry.

The proposed framework considers four SS dimension and 25 indicators. The SS indicators’ importance varies with the individual company's prospect. Therefore the proposed framework is used in multi-company perspective as well as in industry perspective to present more realistic results. The importance weight of SS dimension and indicators are computed with a novel multi-criteria decision-making (MCDM) method, i.e. fuzzy best–worst method (FBWM).

The prioritisation of SS indicators in each company perspective is compared with an industry perspective. The contribution to community health and education program is most valuable indicator followed by the prevention of child and forced labour. The model robustness is tested through sensitivity analysis and reported that less variation in indicators’ ranking.

To authors best of knowledge, this is the first study to highlight the importance of SS indicators in the freight transport industry. This study contributes to the sustainability assessment literature by providing a nuanced perception of the SS indicators and put forward managerial implications for improving the SS of the freight transport industry. The proposed framework could be treated as a benchmark for other developing nation's freight transport industry.

Sustainable human resource management highlights the importance of the eighth sustainable development goal, “decent work and economic growth”. Thus, the purpose of this study is to align human resource practices and policies with Industry 4.0 is imperative.

The authors aimed to identify key challenges of sustainable human resource implementation in view of Industry 4.0 and to analyse these identified challenges by prioritising them for effective Industry 4.0 implementation in an emerging economy such as India. A mixed-methods approach was utilised to prioritise identified challenges. Semi-structured interviews were conducted with experts, academicians and industry mangers. Transcribed interviews were run in NVivo to emerge into broad themes/challenges, which were prioritised using fuzzy best–worst methodology.

The performance appraisal challenge holds maximum importance, followed by learning and development. This finding signifies the need for instilling job security and continuous learning opportunities for employees amidst all disruption caused by Industry 4.0.

This work enhances the link between sustainability, disruptive technologies and Industry 4.0 to transform economic outlooks, leading to improvement under economic aspects through the adoption of sustainable human resource practices into workplaces and society.

Sustainable human resource management has mostly focused on employee welfare. However, the major challenges of disruption caused by Industry 4.0 have not been addressed in the literature. The upskilling and reskilling requirements due to disruptions by Industry 4.0 range from recruitment to performance appraisal and every facet that relates to an employee's cycle in a company. Hence, there is a need to identify critical challenges for optimum adaptation to upcoming industry demands.

The purpose of this paper is to develop a composite index (CI) to evaluate the sustainability performance of an aircraft manufacturing firm using Fuzzy Best Worst Multi Criteria (FBWM) decision-making approach. It identifies a wide range of sub-dimensions and their attributes to measure sustainability in a multi-echelon supply chain.

FBWM, a hybrid multi-criteria decision making method, relies on multiple sub-dimensions and attributes for assessment of sustainability. Sub-dimensions and attributes are identified from a detailed literature review and inputs from 17 experts. The weights are evaluated using best worst method (BWM). Quantitative measurements are very difficult for attributes like living conditions, noise emission, etc., so the performance of attributes are assessed using Fuzzy logic. The proposed methodology is validated with the case study of a single unit of an Indian aircraft manufacturing firm.

Economic concerns emerge as the most influential dimension of sustainability in the aerospace sector. The results reveal that the firm considered in the case study is “Very Sustainable.” Firm’s performance ratings are excellent in three out of 79 attributes considered for the study. Further, 24 weaker (least performing) attributes are identified with the help of fuzzy performance index. These require managerial action for improvements.

The present study is based on inputs from a small group of managers in a single firm in India. It can be extended to a large group of executives in other firms like Boeing, Airbus, etc. for testing the validity of the proposed methodology and generalization of the findings.

Managers, consultants and audit agencies can use the proposed CI developed in this paper for evaluating the sustainability performance of a firm. It assists managers to identify weaker attributes for which they may plan and prioritize their activities for improvements.

To the best of the authors’ knowledge, this is the first paper to measure sustainability in an aircraft manufacturing firm. The paper proposes a novel framework based on fuzzy BWM, for assessing sustainability performance.

The study aims to propose a decision-support system to determine the location of a regional disaster logistics warehouse. Emphasizing the importance of disaster logistics, it considers the criteria to be evaluated for warehouse location selection. It is aimed to determine a warehouse location that will serve the disaster victims most efficiently in case of a disaster by making an application for the province of Izmir, where a massive earthquake hit in 2020.

The paper proposes a fuzzy best–worst method to evaluate the alternative locations for the warehouse. The method considers the linguistic evaluations of the decision-makers and provides an advantage in terms of comparison consistency. The alternatives were identified through interviews and discussions with a group of experts in the fields of humanitarian aid and disaster relief operations. The group consists of academics and a vice-governor, who had worked in Izmir. The results of a previously conducted questionnaire were also used in determining these locations.

It is shown how the method will be applied to this problem, and the most effective location for the disaster logistics warehouse in Izmir has been determined.

This study contributes to disaster preparedness and brings a solution to the organization of the logistics services in Izmir.

An emerging technology in the primary stages of its life cycle is the blockchain. This research paper aims to evaluate the preparedness of hospitals in using blockchain technology in their electronic health record (EHR) systems.

In the initial stage, 15 criteria relating to preparedness in using blockchain in EHR systems were identified from the literature and divided into five criteria, namely, technological, legal, financial, environmental and organizational. Then, 17 experts from various specialized fields were invited to form expert panels. After validating the criteria identified by the expert panels, the weights of the criteria were determined through the fuzzy best-worst multicriteria decision-making method. Following that, the preparedness of ten selected hospitals in Tehran to use blockchain in their EHR systems was assessed via the weighted aggregated sum product assessment method. Finally, using sensitivity analysis and examining different scenarios, the robustness of the results of the proposed approach was validated.

According to expert judgments, the legal criterion (32%) was deemed the most important factor in the preparedness to use blockchain in EHR systems followed by technological (28%), financial (17%), organizational (13%) and environmental (9%) criteria. A sensitivity analysis showed that the proposed approach offers good strength and robustness in evaluating the selected hospitals.

This study can be useful in developing knowledge in the field of technology management for evaluating blockchain implementation in the health-care industry using a novel, coherent and robust approach. In addition, the proposed approach provides comprehensive insight for decision-makers on assessing preparedness in deploying blockchain technology in EHR systems.

Selecting the optimal supplier is a challenging managerial decision that involves several dimensions that vary over time. Despite the considerable attention devoted to this issue, knowledge is required to be updated and analyzed in this field. This paper reveals new opportunities to advance supplier selection (SS) research from a multidimensional perspective. Moreover, this study aims to formalise SS knowledge to enable the appropriate selection of sustainable, resilient and circular criteria.

This study is developed in two stages: first, a systematic literature review is conducted to select relevant papers. Descriptive and thematic analyses are employed to analyze criteria, solving approaches and case studies. Second, a criterion knowledge-based framework is developed and validated by experts to be implemented as ontology using Protégé software.

Evaluating the viability of suppliers need further studies to integrate other criteria and to align SS objectives with research advancement. Artificial intelligence tools are needed to revolutionize and optimize the traditional techniques used to solve this problem. Literature lucks frameworks for specific sectors. The proposed ontology provides a consistent criteria knowledge base.

For academics, the results of this study highlight opportunities to improve the viable SS process. From a managerial perspective, the proposed ontology can assist managers in selecting the appropriate criteria. Future works can enrich the proposed ontology and integrate this knowledge base into an information system.

This study contributes to promoting knowledge about viable SS. Capitalizing the knowledge base of criteria in a computer-interpretable manner supports the digitalization of this critical decision.

Purpose of This Chapter: This research aims to study the need for more age of qualified talent. The evolving corporate needs, education, and curriculum require urgent reform. Current university methods do not align with corporate demands due to outdated content and ineffective pedagogy.

Design / Methodology / Approach: Drawing on established research, this study delves into 7 prominent training strategies across 14 sectors. A survey of 53 HR professionals and managers forms the basis for employing the non-linear best–worst method (BWM) and the Fuzzy BWM to discern the most effective training and development (T&D) modules. This comprehensive methodology ensures a nuanced analysis of T&D practices and insights for businesses seeking to align with Industry 5.0 demands.

Findings: On-the-job training emerges as the most impactful method, followed by case studies, interactive group learning, and more. These methods enhance employee skills in Industry 5.0.

Research Limitations: Limited by a small sample, future research should expand participant diversity for robustness.

Practical Implications: The study holds significance for bridging the skill gap between academic institutions and Industry 5.0. Aligning strategies with industry needs reduces skill disparities, fuels growth, and addresses employability. The study’s impact extends to society, lowering unemployment and shaping a resilient, adaptable workforce for Industry 5.0.

Originality: This innovative research examines decision-making for implementing T&D strategies in the emergence of Industry 5.0, aligning with a human-centric approach to business transformation.