Evaluation of strategies to manage risks in smart, sustainable agri‐logistics sector: A Bayesian‐based group decision‐making approach

Gupta, H.C.; Kharub, Manjeet; Shreshth, Kumar; Kumar, Ashwani; Huisingh, Donald

doi:10.1002/bse.3368

Cited by 9 publications

(4 citation statements)

References 87 publications

(183 reference statements)

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“…The "Best-to-Others" and "Others-to-Worst" matrixes were generated from this survey data. Sixteen experts from the CSF validation phase were invited again to participate in the Bayesian BWM analysis survey [ 26 , 77 ], among which 12 responded (75% response rate). Both surveys were conducted online using Google Forms for the convenience of the experts.…”

Section: Methodsmentioning

confidence: 99%

Assessing the critical success factors for implementing industry 4.0 in the pharmaceutical industry: Implications for supply chain sustainability in emerging economies

Debnath,

Shakur,

Bari

et al. 2023

PLoS ONE

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The emerging technologies of Industry 4.0 (I4.0) are crucial to incorporating agility, sustainability, smartness, and competitiveness in the business model, enabling long-term sustainability practices in the pharmaceutical supply chain (PSC). By leveraging the latest technologies of I4.0, pharmaceutical companies can gain real-time visibility into their supply chain (SC) operations, allowing them to make data-driven decisions that improve SC performance, efficiency, resilience, and sustainability. However, to date, no research has examined the critical success factors (CSFs) that enable the pharmaceutical industry to adopt I4.0 successfully to enhance overall SC sustainability. This study, therefore, analyzed the potential CSFs for adopting I4.0 to increase all facets of sustainability in the PSC, especially from the perspective of an emerging economy like Bangladesh. Initially, sixteen CSFs were identified through a comprehensive literature review and expert validation. Later, the finalized CSFs were clustered into three relevant groups and analyzed using a Bayesian best-worst method (BWM)-based multi-criteria decision-making (MCDM) framework. The study findings revealed that "sufficient investment for technological advancement", "digitalized product monitoring and traceability", and "dedicated and robust research and development (R&D) team" are the top three CSFs to adopt I4.0 in the PSC. The study’s findings can aid industrial practitioners, managers, and policymakers in creating effective action plans for efficiently adopting I4.0 in PSC to avail of its competitive benefits and ensure a sustainable future for the pharmaceutical industry.

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Section: Methodsmentioning

confidence: 99%

Assessing the critical success factors for implementing industry 4.0 in the pharmaceutical industry: Implications for supply chain sustainability in emerging economies

Debnath,

Shakur,

Bari

et al. 2023

PLoS ONE

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show abstract

“…Low productivity and efficiency, a sluggish reaction to market changes, poor decision-making skills, a lack of creativity, an inability to adapt to new technology, and altering client expectations are just a few ways this might show themselves. (Gupta et al 2023), (Chakravorty 2009) Replicating another organization's LSS strategy If a corporation adopts a strategy without first ensuring that it is compatible with its culture and aims, it may suffer the repercussions of doing so. (Bhasin 2012a), (Bhasin 2012b) Weak infrastructure Weak infrastructure can hinder economic growth and development, leading to negative impacts on productivity, competitiveness, and overall well-being of a society.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The capacity of management and leadership to successfully lead the adoption of LSS can also be impacted by a lack of knowledge. (Chakravorty 2009), (Gupta et al 2023)…”

Section: Lack Of Training and Educationmentioning

confidence: 99%

Ranking the Critical Failure Factors to Lean Six Sigma Implementation in Dairy Processing Industry: A Bayesian BWM Approach

Sarwar,

Pias,

Tahsin

2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Lean Six Sigma (LSS) is a data-driven process improvement approach for waste minimization, cost-effective processing, and increasing overall performance. This study proposes a Bayesian Best Worst Method (BBWM) approach to rank critical failure factors inhibiting the implementation of Lean Six Sigma in the dairy processing industry. BBWM is an advanced Multi-Criteria Decision-Making Method (MCDM) that incorporates the subjective judgment of decision-makers to reduce the uncertainty and complexity of the decision-making process. The study is based on the Bangladeshi dairy processing industry. Four main criteria are found through an extensive literature review and experts' opinions. These four main criteria have been ranked using BBWM. Each major criteria are divided into sub-criteria which are also ranked using the same approach. This study will be helpful for further analysis of LSS applications in the dairy processing industry.

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“…They identified criteria for stock portfolio selection and developed a new multicriteria sorting method. Recently, Gupta et al [40] identified risks to smart agriculture supply chains and prioritized them using Bayesian BWM for group MCDM.…”

Section: Introductionmentioning

confidence: 99%

A Group Decision-Making Approach in MCDM: An Application of the Multichoice Best–Worst Method

2023

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Multicriteria decision-making (MCDM) techniques have successfully been used to address a wide range of real-world decision-making issues. The best–worst method (BWM) is one of the several deterministic MCDM approaches. A recently proposed method called the multichoice best–worst method (MCBWM) takes into account several linguistic terms for pairwise comparisons of relative preferences among the criteria. It has been shown that the MCBWM approach has advantages over BWM: it reduces the calculation and determines optimal weight values by providing the choices for the optimal solution. This paper proposes a unique method for group decision-making based on MCBWM. We extended the MCBWM to solve group decision-making problems. A novel solution approach was developed and validated for multiple problems. Two examples and one case study were solved using the proposed approach to demonstrate the validity and application of the proposed method. The results were further compared with existing models to validate the proposed approach. We found that the obtained ranking order for all problems is the same and that the proposed model has a higher consistency ratio than the existing approaches. This method can be extended to other mathematical programming models for collective decision making in uncertain situations.

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Evaluation of strategies to manage risks in smart, sustainable agri‐logistics sector: A Bayesian‐based group decision‐making approach

Cited by 9 publications

References 87 publications

Assessing the critical success factors for implementing industry 4.0 in the pharmaceutical industry: Implications for supply chain sustainability in emerging economies

Assessing the critical success factors for implementing industry 4.0 in the pharmaceutical industry: Implications for supply chain sustainability in emerging economies

Ranking the Critical Failure Factors to Lean Six Sigma Implementation in Dairy Processing Industry: A Bayesian BWM Approach

A Group Decision-Making Approach in MCDM: An Application of the Multichoice Best–Worst Method

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