Key determinants to supply chain resilience to face pandemic disruption: An interpretive triple helix framework.

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.

Disruptive supply chain technology assessment for sustainability journey: A framework of probabilistic group decision making.

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.

Examining the role of soft dimensions on the implementation of ISO 14000 environmental management systems: a graph-theoretic approach.

Organizations worldwide are now embracing different standards while approaching environmental management systems (EMS) to be environmentally and economically sustainable. The ISO 14001 EMS has captured much attention by offering efficient environmental practices organization-wide and throughout the supply chain. Human factors (HFs) are crucial behind implementing ISO 14001 EMS since research articles about ISO 14001 strongly emphasize different HFs. This study proposes a framework that solely focuses on those HFs. Influential HFs are extracted through content analysis of related literature and categorized into 5 main HFs. Opinions from experts in the relevant field about interrelationships and inheritances of the HFs are collected and converted into quantitative data. Incorporating the graph theoretic and matrix approach (GTMA), the data delivered permanent values corresponding to the main HFs and their best and worst possible values. Among the main HFs, EMS Training has been found to have the most scopes for improvement, followed by Employee Empowerment and EMS Teamwork. This study demonstrates a framework about how to assess the role of HFs behind internalizing ISO 14001 EMS and enables organizations to go for competitive benchmarking and to define and adjust goals for environmental management.

Modelling the causes of boiler accidents: implications for economic and social sustainability at the workplace.

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.

Evaluating factors contributing to the failure of information system in the banking industry.

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.

Yard waste prediction from estimated municipal solid waste using the grey theory to achieve a zero-waste strategy.

Yard waste is one of the key components of municipal solid waste and can play a vital role in implementing zero-waste strategy to achieve sustainable municipal solid waste management. Therefore, the objective of this study is to predict yard waste generation using the grey theory from the predicted municipal solid waste generation. The proposed model is implemented using municipal solid waste generation data from the City of Winnipeg, Canada. To identify the generation factors that influence municipal solid waste generation and yard waste generation, a correlation analysis is performed among eight socio-economic factors and six climatic factors. The GM (1, 1) model is utilized to predict individual factors with overall MAPE values of 0.06%-10.39% for the in-sample data, while the multivariable GM (1, N) grey model is employed to forecast the quarterly level of municipal solid waste generation with overall MAPE values of 5.64%-7.54%. In this study, grey models predict quarterly yard waste generation from the predicted municipal solid waste generation values using only twelve historical data points. The results indicate that the grey model (based on the error matrices) performs better than the linear and nonlinear regression-based models. The outcome of this study will support the City of Winnipeg's sustainable planning for yard waste management in terms of budgeting, resource allocation, and estimating energy generation.

Assessing measures implemented by export-oriented RMG firms in an emerging economy during COVID-19.

Manufacturing firms that continued production activities during the COVID-19 have been taking necessary measures to cope with the risks imposed by the pandemic. This study assesses the measures implemented by the Ready-Made Garments (RMG) sector in Bangladesh. With the increase in COVID-19 cases in Bangladesh, following government order, along with firms in other manufacturing sectors, the RMG firms had to shut-down their production between March 26 and April 25, 2020. Soon after the factories reopened, they had to take necessary actions to ensure employee safety, supply of raw materials, and purchase orders from buyers. Using a semi-structured interview approach, we identify 16 measures that have been implemented in the RMG sector in Bangladesh for the employees, suppliers and buyers. Then, we assess the degree of implementation of these measures using the Bayesian Best-Worst method. We find that providing healthcare safety, bringing previously outsourced activities in-house, and ensuring smooth delivery of existing orders were the three most implemented measures for employees, suppliers and buyers, respectively. On a higher level, the RMG industry professionals prioritised buyer-related measures the most, followed by employee and supplier-related. The analysed measures provide a blueprint for supply chain risk management during future waves of COVID-19 transmission and for other potential large-scale natural disasters.

A fuzzy ANP-DEMATEL model on faulty behavior risks: implications for improving safety in the workplace.

The occurrence of occupational accidents and injuries has always been a major concern for industrial management. Such undesirable incidences are higher in developing countries, especially in India, than in developed countries. This research aims to identify, analyze and evaluate the faulty behavior risks (FBRs) that trigger occupational accidents and injuries. Using a data triangulation strategy, this study identified 19 FBR factors under five categories. An integrated approach comprising the fuzzy analytic network process (ANP) and the decision-making trial and evaluation laboratory (DEMATEL) method is proposed for assessing these FBRs. The five most prominent critical risk factors are the absence of continuous monitoring, defective equipment and maintenance, cognitive bias, proper signage and adverse ambient working conditions. The study postulates some implications for industrial management to mitigate occupational accidents and injuries based on the outcomes.

Addressing critical success factors for improving concurrent emergency management: lessons learned from the COVID-19 pandemic.

The world is witnessing a difficult time trying to circumscribe the spread of COVID-19 pandemic. The situation has become even more challenging as several natural disasters and emergencies coincided with this pandemic and created a situation of concurrent emergency. Effectively managing concurrent emergencies are extremely challenging. Only by utilizing and applying the knowledge gathered while combating a real-life concurrent emergency, one can further be prepared to face such emergencies. Hence, this study intends to identify the critical success factors (CSFs) having a major role in effective management of concurrent emergencies and evaluate the indispensable links among the factors. Twelve CSFs are identified from Pareto analysis, for which feedback from 46 experts involved in the management of emergencies is utilized. In addition, the revised rough-decision making trial and evaluation laboratory (rough-DEMATEL) approach is suggested to analyze the indispensable links among the CSFs and further, these factors are ranked based on the average vector length. The recent case of concurrent emergency in Bangladesh- floods amid a pandemic is considered to establish the applicability of this method. The findings of this research reveal that to handle concurrent emergencies 'Incremental improvement of proactive measures', 'Resilient supply chain and logistics network', and 'Government leadership and military cooperation' are the most critical factors to concurrent emergency management (CEM). These CSFs play a major role in ensuring the effectiveness of CEM. Thus, this study can be a building block in developing an effective CEM plan for the policymakers, managers, and practitioners.

Challenges to COVID-19 vaccine supply chain: Implications for sustainable development goals.

The COVID-19 outbreak has demonstrated the diverse challenges that supply chains face to significant disruptions. Vaccine supply chains are no exception. Therefore, it is elemental that challenges to the COVID-19 vaccine supply chain (VSC) are identified and prioritized to pave the way out of this pandemic. This study combines the decision-making trial and evaluation laboratory (DEMATEL) method with intuitionistic fuzzy sets (IFS) to explore the key challenges of the COVID-19 VSC. The IFS theory tackles the uncertainty of key challenges while DEMATEL addresses the interlaced causal relationships among crucial challenges to the COVID-19 VSC. This work identifies 15 challenges and reveals that 'Limited number of vaccine manufacturing companies', 'Inappropriate coordination with local organizations', 'Lack of vaccine monitoring bodies', 'Difficulties in monitoring and controlling vaccine temperature', and 'Vaccination cost and lack of financial support for vaccine purchase' are the most critical challenges. The causal interactions along with mutual relationships among these challenges are also scrutinized, and implications for sustainable development goals (SDGs) are drawn. The results offer practical guidelines for stakeholders and government policy makers around the world to develop an improved VSC for the COVID-19 virus.

A supply chain disruption risk mitigation model to manage COVID-19 pandemic risk.

This study develops a mathematical model to mitigate disruptions in a three-stage (i.e., supplier, manufacturer, retailer) supply chain network subject to a natural disaster like COVID-19 pandemic. This optimization model aims to manage supply chain disruptions for a pandemic situation where disruptions can occur to both the supplier and the retailer. This study proposes an inventory policy using the renewal reward theory for maximizing profit for the manufacturer under study. Tested using two heuristics algorithms, namely the genetic algorithm (GA) and pattern search (PS), the proposed inventory-based disruption risk mitigation model provides the manufacturer with an optimum decision to maximize profits in a production cycle. A sensitivity analysis was offered to ensure the applicability of the model in practical settings. Results reveal that the PS algorithm performed better for such model than a heuristic method like GA. The ordering quantity and reordering point were also lower in PS than GA. Overall, it was evident that PS is more suited for this problem. Supply chain managers need to employ appropriate inventory policies to deal with several uncertain conditions, for example, uncertainties arising due to the COVID-19 pandemic. This model can help managers establish and redesign an inventory policy to maximize the profit by considering probable disruptions in the supply chain network.

Improving supply chain sustainability in the context of COVID-19 pandemic in an emerging economy: Exploring drivers using an integrated model.

Motivated by the COVID-19 pandemic and the challenges it poses to supply chain sustainability (SCS), this research aims to investigate the drivers of sustainable supply chain (SSC) to tackle supply chain disruptions in such a pandemic in the context of a particular emerging economy: Bangladesh. To achieve this aim, a methodology is proposed based on the Pareto analysis, fuzzy theory, total interpretive structural modelling (TISM), and Matriced Impacts Cruoses Multiplication Applique a un Classement techniques (MICMAC). The proposed methodology is tested using experienced supply chain practitioners as well as academic experts' inputs from the emerging economy. This study reveals the influential relationships and indispensable links between the drivers using fuzzy TISM to improve the SCS in the context of COVID-19. Findings also reveal that financial support from the government as well as from the supply chain partners is required to tackle the immediate shock on SCS due to COVID-19. Also, policy development considering health protocols and automation is essential for long-term sustainability in supply chains (SCs). Additionally, MICMAC analysis has clustered the associated drivers to capture the insights on the SCS. These findings are expected to aid industrial managers, supply chain partners, and government policymakers to take initiatives on SSC issues in the context of the COVID-19 pandemic.