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1 – 10 of 70Priya Ambilkar, Priyanka Verma and Debabrata Das
This research work has developed an integrated fuzzy Delphi and neutrosophic best–worst framework for selecting the sustailient (sustainable and resilient) supplier for an…
Abstract
Purpose
This research work has developed an integrated fuzzy Delphi and neutrosophic best–worst framework for selecting the sustailient (sustainable and resilient) supplier for an additive manufacturing (AM)-enabled industry.
Design/methodology/approach
An integrated fuzzy Delphi method (FDM) and neutrosophic best–worst method (N-BWM) approach is developed. 34 supplier evaluation criteria falling under 4 groups, that is, traditional, sustainable, resilient, and AM specific, are identified and validated using the FDM. Afterward, the weights of each criterion are measured by N-BWM. Later on, the performance evaluation is carried out to determine the best-suited supplier. Finally, sensitivity analysis is performed to know the stability and robustness of the proposed framework.
Findings
The outcome indicates the high performance of the suggested decision-making framework. The analysis reveals that supplier 4 (S4) is selected as the most appropriate for a given firm based on the FDM and N-BWM method.
Research limitations/implications
The applicability of this framework is demonstrated through an industrial case of a 3D-printed trinket manufacturer. The proposed research helps AM decision-makers better understand resiliency, sustainability, and AM-related attributes. With this, the practitioners working in AM business can prioritize the supplier selection criteria.
Originality/value
This is the primitive study to undertake the most critical aspect of supplier selection for AM-enabled firms. Apart from this, an integrated FDM-N-BWM framework is a novel contribution to the literature on supplier selection.
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Muhammad Shoaib, Shengzhong Zhang, Hassan Ali, Muhammad Azeem Akbar, Muhammad Hamza and Waheed Ur Rehman
This study aims to identify and prioritize the challenges to adopting blockchain in supply chain management and to make its taxonomic model. Moreover, validate whether these…
Abstract
Purpose
This study aims to identify and prioritize the challenges to adopting blockchain in supply chain management and to make its taxonomic model. Moreover, validate whether these challenging factors exist in the real world and, if they exist, then in what percentage.
Design/methodology/approach
This research adopted the fuzzy best-worst method (F-BWM), which integrates fuzzy set theory with the best-worst method to identify and prioritize the prominent challenges of the blockchain-based supply chain by developing a weighted multi-criteria model.
Findings
A total of 20 challenges (CH's) were identified. Lack of storage capacity/scalability and lack of data privacy challenges were found as key challenges. The findings of this study will provide a robust framework of the challenges that will assist academic researchers and industry practitioners in considering the most significant category concerning their working area.
Practical implications
Blockchain provides the best solution for tracing and tracking where RFID has not succeeded. It can improve quality management in a supply chain network by improving standards and speeding up operations. For inventory management, blockchain provides transparency of documentation for both parties within no time.
Originality/value
To the best of the authors' knowledge, no previous research has adopted the fuzzy best-worst method to prioritize the identified challenges of blockchain implementation in the supply chain. Moreover, no study provides a taxonomic model for the challenges of implementing a blockchain-based supply chain.
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Irfan Ali, Vincent Charles, Umar Muhammad Modibbo, Tatiana Gherman and Srikant Gupta
The COVID-19 pandemic has caused significant disruptions to global supply chains (SCs), affecting the production, distribution, and transportation of goods and services. To…
Abstract
Purpose
The COVID-19 pandemic has caused significant disruptions to global supply chains (SCs), affecting the production, distribution, and transportation of goods and services. To mitigate these disruptions, it is essential to identify the barriers that have impeded the seamless operation of SCs. This study identifies these barriers and assesses their impact on supply chain network (SCN).
Design/methodology/approach
To determine the relative importance of different barriers and rank the affected industries, a hybrid approach was employed, combining the best-worst method (BWM) and the technique for order preference by similarity to an ideal solution (TOPSIS). To accommodate the inherent uncertainties associated with the pandemic, a triangular fuzzy TOPSIS was used to represent the linguistic variable ratings provided by decision-makers.
Findings
The study found that the airlines and hospitality industry was the most affected by the barriers, accounting for 46% of the total, followed by the healthcare industry (23%), the manufacturing industry (19%), and finally the consumer and retail industry (17%).
Research limitations/implications
This study is limited to the four critical industries and nine identified barriers. Other industries and barriers may have different weights and rankings. Nevertheless, the findings offer valuable insights for decision-makers in SC management, aiding them in mitigating the impact of COVID-19 on their operations and enhancing their resilience against future disruptions.
Originality/value
This study enhances understanding of COVID-19’s impact on SCN and provides a framework for assessing disruptions using multi-criteria decision-making processes. The hybrid approach of BWM and TOPSIS in a fuzzy environment is unique and offers potential applicability in various evaluation contexts.
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Sandeep Kumar, Vikas Swarnakar, Rakesh Kumar Phanden, Jiju Antony, Raja Jayaraman and Dinesh Khanduja
This study aims to identify, analyze and rank the critical success factors (CSFs) of Lean Six Sigma (LSS) implementation in Indian manufacturing sector based micro, small and…
Abstract
Purpose
This study aims to identify, analyze and rank the critical success factors (CSFs) of Lean Six Sigma (LSS) implementation in Indian manufacturing sector based micro, small and medium enterprises (MSMEs). This study provides critical insight for managers and researchers aspiring for successful implementation of LSS in Indian manufacturing MSMEs.
Design/methodology/approach
The CSFs were extracted from literature followed by a questionnaire-based survey from 120 industry professionals with extensive knowledge and experience about LSS working in Indian manufacturing MSMEs. Further, the CSFs were grouped based on their fundamental relevance and ranked using best worst method (BWM) approach using inputs from LSS experts.
Findings
This study provides insights on success factors that have helped Indian manufacturing MSMEs to implement LSS. The findings signify that “Strategy based CSFs” were ranked as the top most important factors, followed by two other category factors namely “Bottom-Line CSFs” and “Supplier based and other category-based CSFs”.
Research limitations/implications
The proposed research is specifically relevant to the context of MSMEs in the Indian manufacturing sector. In the future, the same approach can be extended to a global context, encompassing service sector-based MSMEs in healthcare and finance.
Practical implications
This study provides valuable inputs for managers, decision-makers, industrial practitioners and researchers about Indian manufacturing MSMEs. The identified CSFs and their prioritization offer a roadmap for successful adoption of LSS. Managers can allocate resources, and make strategic decisions based on the prioritized CSFs. Decision-makers can align their initiatives with the identified CSFs. Industrial practitioners gain insights to enhance their LSS initiatives, and researchers can focus their efforts on areas critical to LSS implementation in Indian MSMEs. Furthermore, the structured approach employed in this study can be adopted by various MSME sectors globally, thereby broadening the comprehension of LSS implementation.
Originality/value
This study contributes to the existing body of knowledge by addressing the gaps in literature on CSFs related to LSS adoption within Indian manufacturing MSMEs. While LSS has been widely studied, there is limited focus on its adoption in the context of Indian MSMEs. The combination of extensive literature review, questionnaire-based survey and the application of the BWM approach for prioritizing CSFs adds originality to the research.
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The study aims to identify the possible risk factors for electricity grids operational disruptions and to determine the most critical and influential risk indicators.
Abstract
Purpose
The study aims to identify the possible risk factors for electricity grids operational disruptions and to determine the most critical and influential risk indicators.
Design/methodology/approach
A multi-criteria decision-making best-worst method (BWM) is employed to quantitatively identify the most critical risk factors. The grey causal modeling (GCM) technique is employed to identify the causal and consequence factors and to effectively quantify them. The data used in this study consisted of two types – quantitative periodical data of critical factors taken from their respective government departments (e.g. Indian Meteorological Department, The Central Water Commission etc.) and the expert responses collected from professionals working in the Indian electric power sector.
Findings
The results of analysis for a case application in the Indian context shows that temperature dominates as the critical risk factor for electrical power grids, followed by humidity and crop production.
Research limitations/implications
The study helps to understand the contribution of factors in electricity grids operational disruptions. Considering the cause consequences from the GCM causal analysis, rainfall, temperature and dam water levels are identified as the causal factors, while the crop production, stock prices, commodity prices are classified as the consequence factors. In practice, these causal factors can be controlled to reduce the overall effects.
Practical implications
From the results of the analysis, managers can use these outputs and compare the risk factors in electrical power grids for prioritization and subsequent considerations. It can assist the managers in efficient allocation of funds and manpower for building safeguards and creating risk management protocols based on the severity of the critical factor.
Originality/value
The research comprehensively analyses the risk factors of electrical power grids in India. Moreover, the study apprehends the cause-consequence pair of factors, which are having the maximum effect. Previous studies have been focused on identification of risk factors and preliminary analysis of their criticality using autoregression. This research paper takes it forward by using decision-making methods and causal analysis of the risk factors with blend of quantitative and expert response based data analysis to focus on the determination of the criticality of the risk factors for the Indian electric power grid.
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Sheak Salman, Shah Murtoza Morshed, Md. Rezaul Karim, Rafat Rahman, Sadia Hasanat and Afia Ahsan
The imperative to conserve resources and minimize operational expenses has spurred a notable increase in the adoption of lean manufacturing within the context of the circular…
Abstract
Purpose
The imperative to conserve resources and minimize operational expenses has spurred a notable increase in the adoption of lean manufacturing within the context of the circular economy across diverse industries in recent years. However, a notable gap exists in the research landscape, particularly concerning the implementation of lean practices within the pharmaceutical industry to enhance circular economy performance. Addressing this void, this study endeavors to identify and prioritize the pivotal drivers influencing lean manufacturing within the pharmaceutical sector.
Findings
The outcome of this rigorous examination highlights that “Continuous Monitoring Process for Sustainable Lean Implementation,” “Management Involvement for Sustainable Implementation” and “Training and Education” emerge as the most consequential drivers. These factors are deemed crucial for augmenting circular economy performance, underscoring the significance of management engagement, training initiatives and a continuous monitoring process in fostering a closed-loop practice within the pharmaceutical industry.
Research limitations/implications
The findings contribute valuable insights for decision-makers aiming to adopt lean practices within a circular economy framework. Specifically, by streamlining the process of developing a robust action plan tailored to the unique needs of the pharmaceutical sector, our study provides actionable guidance for enhancing overall sustainability in the manufacturing processes.
Originality/value
This study represents one of the initial efforts to systematically identify and assess the drivers to LM implementation within the pharmaceutical industry, contributing to the emerging body of knowledge in this area.
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Aswin Alora and Himanshu Gupta
The purpose of this paper is to identify and prioritise supply chain finance (SCF) adoption enablers and develop a novel comprehensive framework to select supplier firms based on…
Abstract
Purpose
The purpose of this paper is to identify and prioritise supply chain finance (SCF) adoption enablers and develop a novel comprehensive framework to select supplier firms based on their SCF adoption capability.
Design/methodology/approach
The study deploys a three-phase method to identify and prioritise SCF adoption enablers, followed by developing a model to select suppliers according to their SCF adoption capability. An extensive literature review, followed by a Delphi approach-based expert interview, has been used to finalise the enablers. Using the Best Worst Method and the VIsekriterijumsko KOmpromisno Rangiranje technique, a supplier selection model has been developed in the context of a case company.
Findings
The financial health and technological advancement variables received the top priority, followed by collaborative efficiency, whereas the human resources and organisational variables received the slightest significance. A supplier selection framework has also been developed by using the adoption capability of these factors by the supplier partners. In this study’s model, Supplier 4 exhibited better SCF adoption capability and received the top priority.
Research limitations/implications
Manufacturing supply chains in a developing country are the scope of the current study. Extensive future studies are required to derive a global consensus.
Practical implications
The proposed framework of this study can be used to select supplier firms based on their SCF adoption capability. Policymakers can emphasise the most critical enablers of SCF adoption to assist small supplier firms to be a part of the advanced global supply chains.
Originality/value
The current study established a novel comprehensive framework for supplier selection based on the Supply Chain Finance adoption capability of MSME supplier firms.
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Ehsan Aghakarimi, Hamed Karimi, Amir Aghsami and Fariborz Jolai
Considering the direct impact of retailers' performance on the economy, this paper aimed to propose a comprehensive framework to evaluate the performance of different branches of…
Abstract
Purpose
Considering the direct impact of retailers' performance on the economy, this paper aimed to propose a comprehensive framework to evaluate the performance of different branches of a retailer.
Design/methodology/approach
Through a case study, the weights of indicators were calculated by the best-worst method (BWM) and the branches' performance was appraised using data envelopment analysis (DEA).
Findings
The branches were ranked in terms of performance, and sensitivity analysis and statistical tests were conducted to realize the weaknesses and strengths of the branches. Then, some strategies were proposed using strengths, weaknesses, opportunities and threats (SWOT) analysis to improve the performance of the weak branches.
Originality/value
This paper contributes to previous studies on the evaluation of retailers' performance by proposing a triple framework based on resilience, sustainability and sales-marketing indicators. This paper focused on branches' operations and branches' optimization by improving performance in terms of these three indicators. This paper also offers a qualitative and quantitative analysis of retailers' performance, which has received less attention in previous studies.
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Matthew Quayson, Eric Kofi Avornu and Albert Kweku Bediako
Blockchain technology enhances information management in healthcare supply chains by securing healthcare information and providing medical resource traceability. However, there is…
Abstract
Purpose
Blockchain technology enhances information management in healthcare supply chains by securing healthcare information and providing medical resource traceability. However, there is no decision framework to support blockchain implementation for managing information, especially in emerging economies’ healthcare supply chains. This paper develops a hierarchical decision model for implementing blockchain technology for information management in emerging economies’ healthcare supply chains.
Design/methodology/approach
This study uses 20 health supply chain experts in Ghana to rank 17 decision criteria for implementing blockchain for healthcare information management using the best-worst method (BWM) multi-criteria decision technique.
Findings
The results show that “security” and “privacy,” “infrastructural facility” and “presence of training facilities” are the top three critical factors impacting blockchain adoption in the health supply chain for healthcare information management. Other sub-factors are prioritized.
Practical implications
To implement blockchain effectively to enhance information management in the healthcare supply chain, health institutions, blockchain technology providers and state authorities should concentrate on the highly critical factors extracted from the study.
Originality/value
This is the first study that develops a hierarchical decision model for implementing blockchain technology in emerging economies' health supply chains.
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This study attempts to analyse and rank the impact of work-related issues arising within the Indian construction industry in the context of the existing pandemic. Furthermore…
Abstract
Purpose
This study attempts to analyse and rank the impact of work-related issues arising within the Indian construction industry in the context of the existing pandemic. Furthermore, this is the first attempt to provide strategies to overcome issues among a workforce that is highly contractual in nature and is currently witnessing the uncertainties of the pandemic's aftermath. To the best of the author's knowledge, few studies have highlighted the combined analysis of job insecurity, psychological stress and emotional exhaustion in the Indian construction industry.
Design/methodology/approach
The “Best Worst Methodology” (BWM) has been used in this study to analyse and rank the key factors that eliminate negative characteristics among contractual construction employees. The BWM, outlined by Rezaei (2015, 2016), is a popular “multi-criteria decision analysis technique” due to its advantage of consistent results and lesser pairwise data requirements.
Findings
The study identified and ranked the socioeconomic impact of the three waves of the COVID-19 pandemic on construction sector employees in the Indian subcontinent. The results indicate that job insecurity has the most prominent impact, which ultimately produces psychological stress and emotional exhaustion among employees.
Originality/value
To achieve the objective of identifying and prioritising the criteria of adverse socioeconomic impacts during the pandemic and outlining plans of action for the construction industry, ten experts (civil engineers/managers) from ten different construction projects were involved in a mixed-method case study, which has never been explored in the Indian construction sector.
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