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Michael Sony on Muck Rack

Michael Sony

As seen in: Wiley Online Library, MDPI, Taylor & Francis Online, Technovation, TQM Magazine, emerald insight, Journal of Manufacturing Technology Management, Business Process Management Journal, European Journal of Training and Development, International Journal of Quality and Reliability Management
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Articles

Quality Improvement and the UNSDGs: Mapping Industry Contributions at the Goal, Target and Indicator Levels

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By Shreeranga Bhat, Jiju Antony, Michael Sony
| Wiley Online Library Verified
1 Introduction One globally accepted framework for addressing the most pressing environmental, social and governance (ESG) challenges facing humanity is the United Nations' Sustainable Development Goals (UNSDGs). This framework comprises 17 goals, 169 targets and 231 distinct indicators, serving as aspirational commitments and metrics for measuring progress across nations, sectors and organisations (Sachs et al. 2021; United Nations 2015a).
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Lean, Green, and Sustainability 4.0-A Systematic Literature Review

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By Jiju Antony, Michael Sony, Olivia Mcdermott, Diogo Marques Vilaça
| Wiley Online Library Verified
1 Introduction The circular economy and sustainability are deeply interconnected, as both aim to reduce waste, conserve resources, and promote long-term environmental and economic resilience (Awan et al. 2021). A circular economy fosters sustainability by emphasizing reuse, recycling, and remanufacturing, ensuring that materials and products remain in use for as long as possible (Kirchherr et al. 2017).
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Exploring research trends in Lean, Six Sigma and Lean Six Sigma methodologies through a hybrid artificial intelligence approach

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By Peter Madzik, Lukáš Falát, Raja Jayaraman, Michael Sony
| Taylor & Francis Online
Abstract Over recent decades, the adoption of operational excellence (OPEX) methods has grown rapidly, boosting productivity and profitability across various industries. This expansion has led to a fragmented body of research, making it difficult to gain a comprehensive understanding through traditional manual or bibliometric methods. To address this challenge, this study integrates machine learning with bibliometric analysis to map the landscape of Lean Six Sigma (LSS) research.
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