Fuzzy Decision Making Trial And Evaluation Laboratory Research Articles

Analyzing the operational barriers of government-supported healthcare supply chain

PurposeThe purpose of this study is to develop a methodology for the identification, categorization and prioritization of operational government-supported healthcare supply chain barriers (GHSCBs).Design/methodology/approachThis study develops a theoretical background for identifying and segregating relevant GHSCBs and proposes a 5W2H (a Toyota production system) with fuzzy DEcision MAking Trial and Evaluation Laboratory (DEMATEL) embedded approach to quantify the causal–effect relationships among the identified operational GHSCBs.FindingsSeven GHSCBs (i.e. uncertainty of demand management, lack of continuous improvement and learning, lack of deadline management, lack of social audit, warehousing equipment unavailability, human resource shortage and inadequate top level monitoring) were identified as significant cause group where the government, top management and decision-makers of government-supported healthcare supply chain (GHSC) have to put efforts.Research limitations/implicationsThe results obtained are specific to the GHSC of Indian perspective, which could be extended to global context. However, the proposed approach can be a base and provide a platform to understand and analyze the interactions among GHSCBs.Practical implicationsThe proposed methodology will show the appropriate areas for allocating efforts and resources to mitigate the impact of GHSCBs for successful implementation of healthcare supply chain.Originality/valueAccording to best of the authors' knowledge, this is the first study of operational barrier for GHSC in India in specific. The use of 5W2H embedded fuzzy DEMATEL approach for the development and analysis of the theoretical framework of Indian GHSCBs is unique in barrier literature.

Improving the urban heritage based on a tourism risk assessment using a hybrid fuzzy MADM method: The case study of Tehran's central district

AbstractA primary reason for the failure of renewal plans for the heritage of urban areas is ignorance of the risk factors of tourism. Urban heritage sites are valuable tourism resources and focus on tourism activities. These sites are affected by risks and negative events. Assessing tourism risks requires an in‐depth knowledge of the current situation. Thus, in order to reduce the risk of tourism on urban heritage, it is necessary to assess the risk of tourist destinations. This study assesses the urban heritage of central districts in Tehran with an emphasis on tourism risk as a real case study. This study provides an in‐depth review of the dimensions and criteria for evaluating tourism risks from the perspectives of social culture, environment, finance, safety and security. Fourteen criteria developed on the basis of the fuzzy decision‐making trial and evaluation laboratory (FDEMATEL) method are adopted for this assessment to construct the fuzzy influential network relation map and find the fuzzy influential weights; this is known as the DEMATEL‐based analysis network process (called DANP). The hybrid modified fuzzy VIKOR method is adopted to evaluate and reduce the tourism risk towards for closing the gap zero. Finally, an empirical case of Tehran city illustrates the availability and effectiveness of the proposed methods. The results revealed that safety and security risks have the greatest influence. The remaining dimensions are sociocultural, financial, and environmental risks. According to the model and the results of the risk assessment in tourism, this method is a reasonable solution for the assessment and risk analysis in real‐world problems. The proposed method can be a useful tool for managers in tourism and urban planning.

Enablers to implement sustainable practices in the service only supply chain: a case of an Indian hospital

PurposeThe inclusion of sustainable practices in the service only supply chain (SOSC) is less evident in the literature. The aim of this research is to analyse the enablers of sustainability to be implemented in (SOSC).Design/methodology/approachA rigorous literature review and experts’ outlook are used to identify fifteen key enablers of sustainability to be implemented in the SOSC. These key enablers are analysed by the interpretive structural modelling (ISM) and fuzzy decision making trial and evaluation laboratory (DEMATEL) technique. A case study of an Indian hospital is taken for testing the relevancy of the research.FindingsISM identified the six hierarchical levels of the enablers and MICMAC classified the enablers into four categories based on the driving and dependence power of the enablers. The fuzzy DEMATEL technique categorized the enablers into cause and effect groups. Four enablers were found in the effect group and eleven enablers were identified in the cause group.Practical implicationsThis research will help the managers in identifying the enablers of sustainability to implement in the SOSC and to handle the critical enablers with care.Originality/valueThis is the first of its kind of study which not only identifies the key enablers to achieve sustainability in the SOSC but also derives the hierarchy levels of enablers and categorizes these enablers into cause and effect groups.

Identification and analysis of circular supply chain management practices for sustainability: a fuzzy-DEMATEL approach

PurposeCircular supply chain management (CSCM) is proving to be a strong contributor towards sustainable development. The purpose of this study is to analyse the circular practices in CSCM implementation towards sustainability in the Indian auto sector; these practices are at the initial stage in a developing economy.Design/methodology/approachIn this research work, a framework has been developed to enrich understanding of various CSCM practices for sustainability. To develop a causal framework, a Fuzzy Decision-Making Trial and Evaluation Laboratory (F-DEMATEL) methodology has been adopted.FindingsThe results produce 32 practices with cause and effect groups and their importance/priorities. Based on F-DEMATEL, these 32 practices are grouped into cause and effect groups based on R + C and R − C values.Research limitations/implicationsThe findings will help managers and decision-makers to gain a deeper understanding of the practices and their nature; the governmental and financial practices are identified as the most critical factors that need more attention. The results will help strategy makers to plan accordingly.Originality/valueThis research has identified and analysed 32 basic practices and their interrelationships to achieve sustainability in CSCM.

An intelligent framework for analyzing supply chain resilience of firms in China: a hybrid multicriteria approach

PurposeThe paper aims to identify the most supply chain resilient company suitable for the customized preferences of partner firms in the context of the Chinese supply chain framework during the COVID-19 pandemic.Design/methodology/approachA hybrid multicriteria model, i.e. Fuzzy Analytical Hierarchy Process (AHP), was used to assign weights to each criterion, which was subsequently analyzed by three approaches, namely Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory), and Evaluation Based on Distance from Average Solution (EDA), to rank the top ten companies in descending order of supply chain resilience. Further, sensitivity analysis is performed to identify the consistency in ranking with variation in weights. The rankings are validated by a novel Ensemble Ranking algorithm and by supply chain domain experts.FindingsThe rankings suggest the company “China Energy Construction Group Tianjin Electric Power Construction Co., Ltd” is the most feasible and resilient company, presenting interesting findings to partner firms, and Bosch is the least reliable supply chain company of the ten firms considered, thus presenting interesting findings to partner companies.Practical implications“Crisis Management Beforehand” is most critical in the current pandemic scenario. This implies that companies need to first prioritize taking proactive steps in crisis management followed by the need to minimize the “Expected impact of pandemic.” Performance factors also need to be regulated (sales, supply chain rank and financial performance) to maintain the company's overall reputation. Considering the consistent performance of the China Energy Construction Group Tianjin Electric Power Construction Co., Ltd., it is recommended as the most reliable supply chain firm to forge strategic partnerships with other supply chain stakeholders like suppliers and customers. On the other hand, Bosch is not recommended as a supply chain reliable company and needs to improve its crisis management capabilities to minimize the pandemic impact.Originality/valueThe paper aims to identify the most supply chain resilient company suitable for the customized preferences of partner firms in the context of the Chinese supply chain framework during the COVID-19 pandemic. The rankings suggest the company “China Energy Construction Group Tianjin Electric Power Construction Co., Ltd” is the most feasible and resilient company, presenting interesting findings to partner firms, and Bosch is the least reliable supply chain company of the ten firms considered, thus presenting interesting findings to partner companies.

Assessing drivers of post-harvest losses: tangible and intangible resources’ perspective

Many stakeholders in agro-food industry are concerned about sustainability, especially in addressing post-harvest loss (PHL). However, resources available to various supply chain stakeholders such as in the raw cashew nuts (RCNs) supply network to address PHL remain a challenge. The extant literature on PHL has limited intricate insight into its drivers from the perspective of resources. This paper, focusing on RCNs supply network, systematically identifies and analyzes critical drivers that influence PHL guided by tangible and intangible resources’ perspective. Fuzzy-Decision Making Trial and Evaluation Laboratory (Fuzzy-DEMATEL) methodology was employed to analyze and convert an experts’ judgment into quantifiable data to establish the causal relationship among the drivers. The findings reveal that urgent and short-term attention to address PHL in the RCNs supply network should be given to the primary tangible driver of lack/insufficient proper packaging materials. Furthermore, in medium-term strategies, RCNs suppliers and government agencies in-charge of agriculture and industry bodies need to surmount three key cause drivers consisting of premature/green harvesting of cashew nuts, financial and economic constraints and lack of appropriate storage facility. In addition, drivers such as insufficient/lack of management support and commitment and lack of information dissemination on PHL within RCNs suppliers should be addressed in the long term. The study provides a framework for supply chain managers and policymakers to understand the interrelationship among PHL drivers from a resource perspective to enable the implementation of strategies that address PHL.

A new integrated MCDM approach for improving QFD based on DEMATEL and extended MULTIMOORA under uncertainty environment

Quality characteristics (QCs) prioritization is a criterial issue in QFD. However, due to its own inherent flaws, QFD is in a difficult situation in practical applications. As an effective method of multi-objective decision-making, the multi-criteria decision-making (MCDM) method can describe, evaluate, and rank the evaluation objects according to different criteria. Therefore, this paper proposes a new integrated MCDM method for improving QFD, which integrates the hesitant fuzzy linguistic term set (HFLTS), decision-making trial and evaluation laboratory (DEMATEL), and multi-objective optimization by ratio analysis plus full multiplicative form (MULTIMOORA). Firstly, the HFLTS is used to deal with the ambiguity in the evaluation process. Secondly, with regard to the interaction relationships among QCs, fuzzy DEMATEL technology is employed to capture their influence weights. Furthermore, an extended MULTIMOORA method is developed, which combines the entropy weight method to obtain the objective weights of customer requirements (CRs), and introduces the influence weights to prioritize QCs. Finally, the feasibility and practicability of the proposed method are verified by an example regarding the product design of CNC machine tool.

Comparing world regional sustainable supply chain finance using big data analytics: a bibliometric analysis

PurposeSustainable supply chain finance (SSCF) is a fascinated consideration for both academics and practitioners because the indicators are still underdeveloped in achieving SSCF. This study proposes a bibliometric data-driven analysis from the literature to illustrate a clear overall concept of SSCF that reveals hidden indicators for further improvement.Design/methodology/approachA hybrid quantitative and qualitative approach combining data-driven analysis, fuzzy Delphi method (FDM), entropy weight method (EWM) and fuzzy decision-making trial and evaluation laboratory (FDEMATEL) is employed to address the uncertainty in the context.FindingsThe results show that blockchain, cash flow shortage, reverse factoring, risk assessment and triple bottom line (TBL) play significant roles in SSCF. A comparison of the challenges and gaps among different geographic regions is provided in both advanced local perspective and a global state-of-the-art assessment. There are 35 countries/territories being categorized into five geographic regions. Of the five regions, two, Latin America and the Caribbean and Africa, show the needs for more improvement, exclusively in collaboration strategies and financial crisis. Exogenous impacts of wars, natural disasters and disease epidemics are implied as inevitable attributes for enhancing the sustainability.Originality/valueThis study contributes to (1) boundary SSCF foundations by data driven, (2) identifying the critical SSCF indicators and providing the knowledge gaps and directions as references for further examination and (3) addressing the gaps and challenges in different geographic regions to provide advanced assessment from local viewpoint and to diagnose the comprehensive global state of the art of SSCF.

A hyper-hybrid fuzzy decision-making framework for the sustainable-resilient supplier selection problem: a case study of Malaysian Palm oil industry.

One of the major challenges of the supply chain managers is to select the best suppliers among all possible ones for their business. Although the research on the supplier selection with regards to green, sustainability or resiliency criteria has been contributed by many papers, simultaneous consideration of these criteria in a fuzzy environment is rarely studied. Hence, this study proposes a fuzzy decision framework to investigate the sustainable-resilient supplier selection problem for a real case study of palm oil industry in Malaysia. Firstly, the resilient-based sustainable criteria are localized for the suppliers’ performance evaluation in palm oil industry of Malaysia. Accordingly, 30 criteria in three different aspects (i.e. general, sustainable and resilient) are determined by statistical tests. Moreover, a hyper-hybrid model with the use of FDEMATEL (fuzzy decision-making trial and evaluation laboratory), FBWM (fuzzy best worst method), FANP (fuzzy analytical network process) and FIS (fuzzy inference system), simultaneously is developed to employ their merits in an efficient way. In this framework, regarding the outset, the relationships among the criteria/sub-criteria are obtained by FDEMATEL method. Then, initial weights of the criteria/sub-criteria are measured by FBWM method. Next, the final weights of criteria/sub-criteria considering the interrelationships are calculated by FANP. Finally, the performance of the suppliers is evaluated by FIS method. To show the applicability of this hybrid decision-making framework, an industrial case of palm oil in Malaysia is presented. The findings indicate the high performance of the proposed framework in this concept and identify the most important criteria including the cost in general aspects, resource consumption as the most crucial sustainable criterion and agility as the most important resilient criterion.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11356-021-12491-y.

Analysing sustainable concerns in diamond supply chain: a fuzzy ISM-MICMAC and DEMATEL approach

ABSTRACT Sustainable supply chain management has become one of the significant areas of concern for modern industries. Enterprises are now adopting management that implements viable practices involving environmental protection and financial savings in a combined form. In this aspect, this study focuses on detecting various concerns associated with sustainable supply chain management in the diamond mining industry globally. These parameters are classified based on their dependency and driving power (DP) with the help of fuzzy MICMAC analysis. In addition to this, a structural model of the recognised concerns has been established using the interpretive structural modelling technique. Furthermore, the interdependence among the respective concerns have been identified by utilising the decision-making trial and evaluation laboratory (DEMATEL) approach. Also, an integrated ISM-DEMATEL model has been employed to form an evident understanding of these concerns. The findings of this study illustrate that ‘Awareness Programmes’ and ‘Proper Infrastructure Investment’ should be given due consideration to ensure a sustainable competitive advantage.

Modelling Internet of things (IoT)-driven global sustainability in multi-tier agri-food supply chain under natural epidemic outbreaks.

Epidemic outbreak (COVID-19, SARS-CoV-2) is an exceptional scenario of agri-food supply chain (AFSC) risk at the globalised level which is characterised by logistics’ network breakdown (ripple effects), demand mismatch (uncertainty), and sustainable issues. Thus, the aim of this research is the modelling of the sustainable based multi-tier system for AFSC, which is managed through the different emerging application of Internet of things (IoT) technology. Different IoT technologies, viz., Blockchain, robotics, Big data analysis, and cloud computing, have developed a competitive AFSC at the global level. Competitive AFSC needs cautious incorporation of multi-tiers suppliers, specifically during dealing with globalised sustainability issues. Firms have been advancing towards their multi suppliers for driving social, environments and economical practices. This paper also studies the interrelationship of 14 enablers and their cause and effect magnitude as contributing to IoT-based food secure model. The methodology used in the paper is interpretative structural modelling (ISM) for establishing interrelationship among the enablers and Fuzzy-Decision-Making Trial and Evaluation Laboratory (F-DEMATEL) to provide the magnitude of the cause-effect strength of the hierarchical framework. This paper also provides some theoretical contribution supported by information processing theory (IPT) and dynamic capability theory (DCT). This paper may guide the organisation’s managers in their strategic planning based on enabler’s classification into cause and effect groups. This paper may also encourage the mangers for implementing IoT technologies in AFSC.

Identifying and prioritizing the effective factors in the implementation of green supply chain management in the construction industry

In recent years, environmental protection and sustainability have become significant issues and have attracted everyone's attention. And many organizations are now interested in using it as their strategy to gain customer satisfaction and market share and outperform competitors. This article aims to identify and prioritize the main factors that implement green supply chain management (GSCM) in the construction industry. To achieve the goal, the integrated approach combining is fuzzy decision-making trial and evaluation laboratory (FDEMATEL) and fuzzy analysis network Process (FANP) developed. The parameters employed are in this approach identified through an extensive literature review, and validation is criteria introduced through the experts’ opinions to discuss data uncertainty. First, the FDEMATEL method sets up the interrelationships between the criteria, which used for determining are the most important factors in the GSCM approach. Then, the local weight of the criteria calculated using the FANP approach based on cause and effect relationships, and through the FDEMATEL method. The results of this study show that external factors are the most important and influential factors in the GSCM approach, Therefore, the findings of this study can guide managers to make better use of the GSCM approach in the Iranian construction industry.

A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping

Landslides are dangerous events which threaten both human life and property. The study aims to analyze the landslide susceptibility (LS) in the Kysuca river basin, Slovakia. For this reason, previous landslide events were analyzed with 16 landslide conditioning factors. Landslide inventory was divided into training (70% of landslide locations) and validating dataset (30% of landslide locations). The heuristic approach of Fuzzy Decision Making Trial and Evaluation Laboratory (FDEMATEL)-Analytic Network Process (ANP) was applied first, followed by bivariate Frequency Ratio (FR), multivariate Logistic Regression (LR), Random Forest Classifier (RFC), Naïve Bayes Classifier (NBC) and Extreme Gradient Boosting (XGBoost), respectively. The results showed that 52.2%, 36.5%, 40.7%, 50.6%, 43.6% and 40.3% of the total basin area had very high to high LS corresponding to FDEMATEL-ANP, FR, LR, RFC, NBC and XGBoost model, respectively. The analysis revealed that RFC was the most accurate model (overall accuracy of 98.3% and AUC of 97.0%). Besides, the heuristic approach of FDEMATEL-ANP model (overall accuracy of 93.8% and AUC of 92.4%) had better prediction capability than bivariate FR (overall accuracy of 86.9% and AUC of 86.1%), multivariate LR (overall accuracy of 90.5% and AUC of 91.2%), machine learning NBC (overall accuracy of 76.3% and AUC of 90.9%) and even deep learning XGBoost (overall accuracy of 92.3% and AUC of 87.1%) models. The study revealed that the FDEMATEL-ANP outweighed the NBC and XGBoost machine learning models, which suggests that heuristic methods should be tested out before directly applying machine learning models.

Application of fuzzy DEMATEL and fuzzy CODAS for analysis of workforce attributes pertaining to Industry 4.0: a case study

PurposeThe aim of this study is to analyze workforce attributes related to Industry 4.0 using fuzzy decision-making trial and evaluation laboratory (DEMATEL) and fuzzy combinative distance-based assessment (CODAS).Design/methodology/approachTechnological trends stipulate various revolution in industries. Industry 4.0 is a vital challenge for modern manufacturing industries. Workforce adoption to such challenge is gaining vital importance. Therefore, such workforce-related attributes need to be identified for enhancing their performance in Industry 4.0 environment. In this context, this article highlights the analysis of 20 workforce attributes for Industry 4.0. Relevant criteria are prioritized using fuzzy DEMATEL. Workforce attributes are prioritized using fuzzy CODAS.FindingsThe key attributes are “Skills/training in decision-making (WA2)”, “Competences in complex system modelling and simulation (WA1)” and “Coding skills (WA20)”.Research limitations/implicationsIn the present study, 20 workforce attributes are being considered. In future, additional workforce attributes could be considered.Practical implicationsThe study has been conducted based on inputs from industry experts. Hence, the inferences have practical relevance.Originality/valueThe analysis of workforce attributes for Industry 4.0 using MCDM methods is the original contribution of the authors.

An Integrated Fuzzy DEMATEL and Intuitionistic Fuzzy TOPSIS Method to Evaluate Sustainable Supplier Performance

Sustainability has gained more attention in an attempt to avoid unwanted results in the business environment since its main objective is to minimize the negative impact on the economy, society, and the environment. A supplier relationship is one of the critical concepts in sustainable supply chain management. Companies ought to measure their supplier’s performance in terms of sustainability. This study aims to suggest an efficient alternative integrated Multi-Criteria-Decision-Making (MCDM) method in order to evaluate sustainable supplier performance. In this method, an integrated Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) method and Intuitionistic Fuzzy Technique for Order Preference by Similarity to Ideal Solution (IF-TOPSIS) method is proposed to evaluate sustainable supplier performance. A case study is conducted in a Turkish chemical company operating in the FMCG industry.

Application of fuzzy DEMATEL method for analysing of accidents in enclosed spaces onboard ships

The present study provides a robust and systematic approach for the analysis of accidents in enclosed spaces onboard ships. An enclosed space is considered a hazardous working environment since its atmosphere contains substances harmful to health. Despite improvements in safety standards, accidents in enclosed spaces remain a major concern for the shipping industry today. Therefore, this paper analyses accidents to identify the factors causing them. The study makes use of a fuzzy DEMATEL (Decision Making Trial and Evaluation Laboratory) method to evaluate causal factors which can be mitigated through the adoption of certain measures. The study focuses on 101 enclosed space accidents/incidents, which have led to 93 deaths and 96 injuries, and determines their causal factors. The study findings emphasize the fact that human error and weaknesses in organizational factors are primarily responsible for accidents in enclosed spaces. Accordingly, effective control measures have been identified which can prevent accidents and enhance the safety and sustainability of the shipping industry. This study overall makes a contribution to safety management in the shipping industry.

Enhancement of value stream mapping application process through using fuzzy DEMATEL and fuzzy QFD approaches: a case study considering economic and environmental perspectives

PurposeThis paper aims to investigate an integrated approach that aims at enhancing the application process of value stream mapping (VSM) method. It also proposes an extended VSM called Economic and Environmental VSM(E-EVSM). The proposed approach highlights the improvement of economic and environmental performances.Design/methodology/approachThe proposed approach has studied the integration of VSM, fuzzy decision-making trial and evaluation laboratory (DEMATEL) and fuzzy quality function deployment (QFD) to improve the economic and environmental performances of manufacturing processes. The VSM method is used for data collection and manufacturing process assessment, whereas fuzzy DEMATEL is used to analyse the current state map. Finally, fuzzy QFD is used to organize the improvement phase of VSM method.FindingsThe clear findings of this research prove the effectiveness of VSM method on the environmental and economic performances of manufacturing processes. In addition, the proposed approach will show the advantages of fuzzy DEMATEL and fuzzy QFD approaches in improving the application of the VSM method.Research limitations/implicationsThe limitation of this study includes the lack of consideration of other dimensions such as social, technological and managerial. In addition, the proposed approach studied an average set of environmental and economic indicators.Originality/valueThe novelty of the proposed approach is proved by the development of an extended VSM method (E-EVSM). Also, the proposed approach contributes by a new methodology for analysing and improving the current state map of manufacturing processes.

WITHDRAWN: Heuristic fuzzy logic approach for analyzing the effects of emotional intelligence in teachers’ teaching quality and teamwork in dance training institutions

Abstract Emotional intelligence is characterized as the capacity to recognize, evaluate, and control one's feelings, others' feelings, and group activities. Emotional intelligence in teachers' teaching is fundamental. It teaches students skills such as active listening, self-awareness, emotional control, empathy, relationship skills, and self-motivation, which can equip them to succeed academically and socially. In this paper, the Heuristic Fuzzy Logic Approach (HFLA) has been proposed to Analyse the Effects of Emotional Intelligence in teachers' teaching quality and teamwork in dance training institutions. Here the Heuristic Fuzzy Logic Approach has been used to map the events and observation of emotional behaviors and the state of emotions easily. The teamwork in dance training with high emotional intelligence may provide a productive dynamic team with learning to manage their reactions and interaction among group members. The experimental results show that the proposed heuristic fuzzy logic approach enhances the student's positive emotions than traditional approaches such as fuzzy decision-making trial and evaluation laboratory (F-DEMATEL), Observe, Amend and Analysis (OAA), Analysis of variance(ANOVA), Partial Least Square - Structural Equation Modelling (PLS-SEM).

Interactions among Cognitive Factors Affecting Unsafe Behavior: Integrative Fuzzy DEMATEL ISM Approach

The prevention of industrial accidents is not completely practical by implementing safety programs unless focusing on protecting vulnerable workers. The unsafe behavior cognitive factors (UBCFs) are essential determinants contributing to human failure. This study aimed at eliciting the most important UBCFs, along with investigating hierarchical cause-effect interactions among them. A qualitative approach using metasynthesis was utilized to extract all essential UBCFs among industrial workplaces. Afterward, the relationships between UBCFs were recognized using the fuzzy decision-making trial and evaluation laboratory (DEMATEL) method and rated by an expert panel. Also, a hierarchical model was developed based on the final matrix of DEMATEL by employing the interpretive structural modeling (ISM) method. Ten criteria were extracted as UBCFs through the metasynthesis method. The threshold value was set as 0.175 in DEMATEL following experts’ ideas. Inadequacy of persons’ resilience and habitual rule ignorance were recognized as the most important predictive UBCFs. The developed model was tested through a case study in a petrochemical company. The results of the study can be used to help industrial managers and HSE practitioners to consider workers’ capabilities either cognitively or physically when designing the required tasks to reduce unsafe behaviors. Also, the findings of the study are applicable for other researchers to prioritize the most important factors affecting unsafe behavior in different workplaces.

A hybrid model for analyzing the risks of green supply chain in a fuzzy environment

ABSTRACT Effective adoption and implementation of green initiatives in greening a supply chain are threatened by several risks. Hence, investigating appropriate responses to mitigate risks is needed by most companies. The aim of this study is to propose a new approach for prioritizing the risks of Green Supply Chain (GSC) in a fuzzy environment. For this purpose, a fuzzy Multi-criteria Decision-Making (MCDM) approach is proposed for which, a systematic methodology is conducted by integrating a fuzzy decision-making trial and evaluation laboratory (DEMATEL) and fuzzy technique for order of preference by similarity to ideal solution (TOPSIS). The proposed approach makes the use of fuzzy DEMATEL in order to evaluate the weights of risk criteria. Then, the responses of GSC’ risks are assessed and ranked based on six main risk categories and 12 criteria. To highlight the real-life applicability of the proposed approach, an empirical case study is conducted.