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Peter Madzik on Muck Rack

Peter Madzik

As seen in: Administrative Sciences, MDPI, Taylor & Francis Online, International Journal of Productivity and Performance Management, TQM Magazine, International Journal of Retail and Distribution Management, International Journal of Quality and Reliability Management, Journal of Enterprise Information Management, E+M Ekonomie a Management
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Articles

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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Exploring blockchain technologies in sustainable supply chains - unveiling the latent research topics using an AI approach

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By Peter Madzik, Lukáš Falát, Fatma Pakdil
| Taylor & Francis Online
Abstract Blockchain technology is reshaping sustainable Supply Chain Management (SSCM) by enhancing transparency, efficiency, and security. This study introduces an unsupervised machine learning approach, leveraging Latent Dirichlet Allocation (LDA) to systematically analyse over 4,000 scholarly articles and uncover latent research topics in blockchain applications for SSCM.
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State-of-the-art on analytic hierarchy process in the last 40 years: Literature review based on Latent Dirichlet Allocation topic modelling

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By Peter Madzik, Lukáš Falát
| PLOS
3.3 Topics related to AHP and their development We used LDA topic modeling to analyze topics. Topic modeling is the process of identifying topics in a set of documents. Our set of documents consisted of 35,430 articles, and the LDA was used for abstracts of these articles. Several experiments have been performed with LDA to achieve a reasonable number of clusters with good interpretability and distinguishability. Based on the settings listed in section 2.3, we identified nine topics.
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