Faculty Publications
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Item Harnessing Industry 4.0 in agri supply chains: a strategy for enhanced sustainability(Journal of Agribusiness in Developing and Emerging Economies, 2025-11-24) Sonar, Harshad; Joshi, Prasad; Ghag, Nikhil; Kumar, Shashank; Giri, NabanitPurpose To investigate the underlying factors contributing to this challenge, this research aims to identify key barriers to Industry 4.0 (I4.0) adoption in agriculture supply chains to enhance sustainability. Additionally, this work seeks to develop a structured framework to examine their interactions to investigate the fundamental causes of this problem. Design/methodology/approach Initial identification of I4.0 adoption barriers in agricultural supply chains is done using a comprehensive literature review. The Fuzzy-Delphi method is then used for validation and finalization. Then, the Decision-Making Trial and Evaluation Laboratory approach is used to determine causal links between these barriers. Findings The results show that the biggest obstacles to I4.0 implementation in agricultural supply chains are “lack of management commitment,” “lack of prioritization of circular economy (CE) practices,” “lack of a skilled workforce” and “lack of government support.” Research limitations/implications The study is directed from the viewpoint of a developing economy because the most pressing sustainability issues are found in these nations. I4.0 technologies will be essential in supplying developing nations with wholesome, secure, and environmentally sound food. Originality/value This paper offers a framework that tackles several problems in one model. As part of the technique, a thorough literature analysis is conducted to identify the main difficulties. These are then confirmed by speaking with industry experts and visiting different stakeholders in the sector. The developed framework provides an organized approach for implementing I4.0 and circular economy principles in the agricultural supply chain while methodically removing or minimizing the effects of barriers.Item Unveiling the nexus of Industry 4.0 and Lean Six Sigma for sustainable development: insights from bibliometric and structural topic modeling analysis Available to Purchase(Industry 4.0, Lean Six Sigma, Lean, Sustainability, Sustainable manufacturing, 2025-03-25) Wankhede, Vishal Ashok; Agrawal, RohitPurpose This study aims to investigate the alignment between Industry 4.0 and Lean Six Sigma (LSS) in the context of sustainable development, focusing on the reasons for the growing attention to this integration and its implications for sustainability. Design/methodology/approach This research is designed based on literature overview using a review of bibliometric analysis and structural topic modeling of existing literature to provide evidence on the relationship among Industry 4.0 technologies, LSS practices and sustainable organizational performance. Findings It is evident from the study that incorporating Industry 4.0 technology with LSS can lead to a great sustainable manufacturing. With the use of digital tools such as Internet of Things sensors, advanced analytics and artificial intelligence, organizations significantly improve real-time monitoring, improve resource allocation and apply predictive maintenance, thus minimizing waste and the environmental effect. This approach also propels innovation and agility and fosters a culture of continuous improvement while evolving around higher-level topics around quality control and strategic management. Findings reveal that these integrations ensure operational excellence and sustainable development. Research limitations/implications The study provides guidelines for researchers and practitioners working on integration of Industry 4.0 technologies and LSS for enhancing the environmental and resource performance in manufacturing. Originality/value The novelty of the research is the integration of the knowledge existing on Industry 4.0 and LSS and its contribution in sustainable development perspectives. Moreover, this study may also be useful for other researchers because it provides a general overview of the trends and theoretical contribution that are practically confirmed in the empirical level.Item Analysis of critical success factors of Quality 4.0 implementation in manufacturing SMEs using best–worst method(The TQM Journal, 2024-12-19) Vinodh,Sekar; Wankhede, Vishal Ashok; Muruganantham, GanesanPurpose To attain a competitive edge, it is essential to realize the significant critical success factors (CSFs) that contribute to the adoption of Quality 4.0 (Q4.0) in manufacturing organizations. Therefore, the study aimed to analyze CSFs for Q4.0 implementation in manufacturing small and medium-sized enterprises (SMEs) using multi-criteria decision-making (MCDM) tool. Design/methodology/approach The present study begins with a systematic literature review of past studies about Q4.0 implementation in manufacturing, followed by the identification of CSFs. Further, a case study was conducted wherein 42 CSFs identified were grouped into five dimensions. Best–worst method is a MCDM tool applied as a solution methodology for the analysis of CSFs based on expert opinion and priority order of CSFs attained. Findings The priority order of CSFs is obtained. Based on the findings, significant CSFs are “Data prediction and Analytics,” “Organizational culture towards Quality 4.0” and “Machine to Machine communication.” Practical implications The shifting market dynamics incorporate Q4.0 inclusion for realizing zero defects and high traceability in automotive SMEs. The present study offers implications for industry managers and practitioners by delivering insights on how Q4.0 could be serving automotive systems and CSFs that industry authorities need to pay attention to effectively adopt Q4.0 in the current quality systems. The study will facilitate industry practitioners to meticulously examine CSFs for Q4.0 toward the improvement of SME performance. Originality/value The identification of CSFs for Q4.0 adoption in manufacturing SMEs, along with the prioritization of CFSs using the MCDM tool, is the original contribution by the authors.