Leveraging ChatGPT for Enhanced Logical Analysis in the Theory of Constraints Thinking Process

Aljaž Tomaž ()
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Aljaž Tomaž: Faculty of Information Studies, Novo mesto, Slovenia

Organizacija, 2024, vol. 57, issue 2, 202-214

Abstract: Background/Purpose Artificial intelligence (AI) has traditionally been used for quantitative analysis using explicit data. However, much of the information critical to decision making remains undocumented and is not stored in a structured way. This study explores the integration of AI, specifically ChatGPT, into Theory of Constraints (TOC) Thinking Process (TP) tools. Method In this study, we applied ChatGPT to a real-world IT project management case using a variety of research methods, including international literature analysis, observation, and personal experience. The use of the TOC TP allowed us to understand the decision-making process of ChatGPT and to systematically explore its advantages and limitations in creating logical trees of TOC TP. Results ChatGPT significantly enhanced efficiency and depth in TOC TP data collection and analysis, effectively addressing logical leaps for more coherent structures. It also promoted deeper analytical thinking and aided root cause identification. The integration of ChatGPT into the TOC TP process led to faster decision-making, reduced bias, and clearer analysis. Challenges of ChatGPT including the need for human oversight, specific TOC TP training, and ethical considerations were noted. Conclusion This study provides an initial investigation into the use of ChatGPT in TOC TP tools. The results suggest that ChatGPT has the potential to be a valuable tool for organizations seeking to improve their decision making and performance. However, further research is needed to validate these findings and explore the full potential of AI in TOC TP.

Keywords: ChatGPT; Artificial Intelligence; Theory of Constraints; Theory of Constraints Thinking Process; Logical Analysis; Decision Making; Structured Analysis Framework (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:organi:v:57:y:2024:i:2:p:202-214:n:7

DOI: 10.2478/orga-2024-0014

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