202 Organizacija, V olume 57 Issue 2, May 2024 Research Papers 1 Received: 17th December 2023 ; Accepted: 23rd January 2024 Leveraging ChatGPT for Enhanced Logical Analysis in the Theory of Constraints Thinking Process Tomaž ALJAŽ 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 sug- gest 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 DOI: 10.2478/orga-2024-0014 1 Introduction Artificial intelligence (AI), notably represented by ChatGPT (Zamfiroiu et al., 2023), presents a transform- ative avenue for enhancing logical analysis in intricate decision-making processes. Integrating AI into established frameworks, such as the Theory of Constraints (TOC) Thinking Process (TP) (Gaspar et al., 2019; Goldratt, 2008), holds increasing importance as organizations seek advanced solutions. This study explores the integration of ChatGPT within the TOC TP to improve logical analysis, thereby enhancing decision-making outcomes. The TOC TP (Dettmer 2007) incorporates essential tools for systematic logical analysis and decision-making. These tools provide a structured approach to identifying and resolving root causes of problems and implementing effective solutions using logical trees: Current Reality Tree (CRT) identifies the root cause of a problem; Future Reality Tree (FRT) envisions the outcomes of proposed changes; Prerequisite Tree (PRT) establishes the necessary Faculty of Information Studies, Novo mesto, Slovenia, tomaz.aljaz@fis.unm.si 203 Organizacija, V olume 57 Issue 2, May 2024 Research Papers conditions for change; and Transition Tree (TT) develops a roadmap for executing the changes. Traditional methods for constructing logical trees often involve manual analysis of unstructured data. This process can be time-consuming, error-prone, and susceptible to bi- ases. ChatGPT’s ability to process and understand unstruc- tured data presents an opportunity to overcome these chal- lenges, potentially improving the accuracy and efficiency of logical analysis. The primary research question guiding this study is: How can ChatGPT be effectively integrated into the TOC TP to enhance logical analysis and decision-making out- comes? This study aims to investigate the potential of ChatGPT in integrating with TOC TP, focusing on bias reduction, decision-making acceleration, and clarity and accuracy of logical analysis. The research design adopts a comprehensive multi-fac- eted approach, integrating international literature analysis from reputable databases such as SpringerLink and Scop- us, alongside direct observation and insights derived from personal experience. Utilizing ChatGPT in this methodolo- gy, diverse prompts related to IT project management were posed, ranging from extracting common factors leading to project delays to investigating causes for resource unavail- ability. This approach ensures a thorough examination of ChatGPT’s integration within the TOC TP, particularly in real-world IT project management scenarios. Despite the transformative potential of AI, the existing body of research in this area remains remarkably scarce. A cursory review of academic databases reveals a limited number of publications, a lack of comprehensive reviews, and a dearth of empirical studies dedicated to this emerg- ing intersection. This scarcity of research highlights the need for further investigation to fully understand the op- portunities and challenges associated with this novel com- bination. By leveraging ChatGPT’s capabilities, this study contributes to existing knowledge by exploring the bene- fits, challenges, and implications of integrating ChatGPT into TOC TP, with a specific focus on constructing CRTs. The findings aim to empower organizations in making in- formed decisions about implementing ChatGPT into their decision-making processes. The study is organized as follows. Section 2 provides a brief review of the current research and problem definition on which our study is based. The research methodology in Section 3, while Section 4 presents the results. Section 5 summarizes and discusses the findings and implications for further practice. Section 6 draws conclusions. The significance of this study lies in its potential to of- fer organizations an innovative approach to decision-mak- ing, bridging the gap between traditional structured anal- ysis methods and the transformative capabilities of AI, as embodied by ChatGPT. 2 Current Research on the Integration of ChatGPT in TOC TP This chapter provides an overview of the current re- search landscape related to the integration of ChatGPT and the tools of TOC TP. While this is a relatively new area of research, we aim to explore the existing knowledge and identify potential research gaps to establish a foundation for our study. The systematic literature review conducted from 18 June 2023 to 21 July 2023 covered the fields of en- gineering, computer science, decision science, and busi- ness management and accounting. Keyword searches in SpringerLink and Scopus databases, combining terms such as ChatGPT, Bias, Theory of Constraints, Theory of Constraints Thinking Process, Logical Analysis, Decision Making, and Structured Analysis Framework, yielded over 1.8 million papers. Refining the search with criteria such as “ChatGPT*” OR “Theory of Constraints Thinking Pro- cess” and English language narrowed down the results to 744 papers in SpringerLink and 369 papers in Scopus. A thorough review of abstracts revealed no direct research on the integration of ChatGPT and TOC TP, indicating a significant research gap in this specific area. While there is no direct research on the integration of ChatGPT and the TOC TP, related research examines their individual components. In the study (Hanmeng et al. 2023), ChatGPT significantly outperformed GPT-4 on log- ical reasoning benchmarks, indicating its stronger logical reasoning ability. The study (Hackaday, 2023) shows the effectiveness of ChatGPT in solving and scoring logic puz- zles. On the other hand, the study (Escape Velocity Labs, 2022) reveals ChatGPT’s surprising ability to imitate rea- soning, identify fallacies, and solve puzzles. These results suggest that ChatGPT accelerates the construction of logic trees and provides valuable insights for decision making. Another area of research focuses on using ChatGPT to reduce bias and speed up logical analysis. Using its ability to process large amounts of information and gen- erate unbiased responses could minimize human bias in the construction of logical trees, thereby improving the objectivity and accuracy of decision making. However, re- search (Fischer et al., 2023 and Frąckiewicz, 2023) reveals ChatGPT’s vulnerability to unconscious bias. In addition, a study (Chen et al., 2023) specifical- ly investigated behavioral biases relevant to operations management. ChatGPT exhibits human-like biases in complex, ambiguous, and implicit problems, such as con- junction bias, probability weighting, framing effects, sa- lience of anticipated regret, and reference dependence. It also struggles to process ambiguous information and as- sess risk differently from humans, exhibiting heuristic-like responses and confirmation biases, which are exacerbated by overconfidence. Moreover, it highlights the importance 204 Organizacija, V olume 57 Issue 2, May 2024 Research Papers of considering potential AI biases in the development and implementation of AI for business operations. It is noteworthy that the existing body of research in this specific area remains remarkably scarce. This apparent research gap presents significant opportunity for research- ers to delve into this unexplored area and conduct original research on this novel combination. By addressing this gap, our study aims to make an initial contribution to the emerging field of ChatGPT and TOC TP integration, there- by advancing the understanding and practical applications of this combination. The specific research questions that this study will address are: • What are the potential benefits of integrating ChatGPT and the TOC TP? • What are the challenges and implications of this approach? • How can ChatGPT be used to accelerate decision making and problem solving in real-world appli- cations? By addressing these research gaps, this study aims to provide a better understanding of the potential of this ap- proach and how it can be used to improve decision making and problem solving. 3 Methodology In this study, we adopted a comprehensive case study methodology, focusing on a real-world scenario in IT pro- ject management to assess the decision-making efficacy of ChatGPT. This involved examining typical challenges in IT project management, including task management diversity, cross-functional team coordination, and risk mitigation, particularly unforeseen delays, and resource constraints. The choice of such projects was driven by the need to understand the dynamic interplay of various fac- tors in complex IT environments and how ChatGPT could potentially navigate these complexities. To effectively evaluate ChatGPT’s role in this context, we concentrated on its application within the TOC TP. Our analysis primarily centered around the development of a CRT, utilizing the process outlined by Holt and Aljaž (2020) as shown in Figure 1. This approach was instru- mental in dissecting ChatGPT’s decision-making process, allowing us to explore its potential benefits and limitations in a structured and methodical manner. The research design involved a multi-faceted approach, combining international literature analysis (SpringerLink and Scopus databases), observation, and personal experi- ence. Leveraging ChatGPT, we posed diverse prompts re- lated to the IT project management case, such as retrieving common factors contributing to project delays or explor- ing causes for resource unavailability. Human validation played a crucial role in ensuring the reliability and accuracy of ChatGPT-generated responses. One person accessed https://chat.openai.com/ for input prompts, and the generated ChatGPT responses were man- ually scored by at least two people, with disagreements resolved by majority vote. This approach emphasizes the importance of human oversight in validating ChatGPT generated content. Our methodology is grounded in the principles of logi- cal reasoning and problem-solving within the TOC, as de- tailed in (Scheinkopf, 1999). We adapted these principles to incorporate the capabilities of ChatGPT, thus enhancing the traditional framework with ChatGPT driven insights. The process is structured into three distinct steps, as de- picted in Figure 2. Figure 1: Workflow of creating CRT (Holt and Aljaž, 2020) 205 Organizacija, V olume 57 Issue 2, May 2024 Research Papers • Draft analysis: This step involves the collection and identification of relevant data (UnDesirable Effects - UDEs) related to the problem under in- vestigation, gathered from various sources such as observations, interviews, reports, measurements, and ChatGPT. A draft CRT is then constructed that outlines initial cause and effect relationships. • Leaps of logic: This step involves identifying and analyzing cause and effect relationships. In some cases, data may be insufficient to establish direct links between entities, leading to “leaps of logic” where long arrows connect entities based on the- oretical or assumed relationships. ChatGPT helps to validate the draft CRT by identifying inconsist- encies or leaps in logic, thereby improving the ac- curacy and credibility of the analysis. • CRT: This step involves identifying the root caus- es of the problem. ChatGPT helps to uncover hid- den assumptions and encourages critical thinking about the beliefs underlying the problems. Figure 2: Workflow with ChatGPT in the use case of creating CRT 206 Organizacija, V olume 57 Issue 2, May 2024 Research Papers Example prompts for ChatGPT: • Draft analysis: o “Retrieve common factors contributing to IT projects not being completed on time.” o “Explore possible causes for recurring issues with resource unavailability in IT projects.” o “Rank identified UDEs based on common pat- terns in project management.” o “Explain the concept of bottlenecks in IT pro- ject management.” • Leaps of logic: o “Validate/critically review the logical validity of cause-and-effect relationships in the Draft CRT for potential logical gaps.” • CRT (finalizing): o “Help us identify any hidden assumptions about our team’s productivity and how we can chal- lenge them.” The time it takes to create a CRT can vary depending on the complexity of the system being analyzed, the expe- rience of the person creating the CRT, and the availabil- ity of resources. To empirically assess these variables, a survey was conducted targeting students from FIŠ Novo mesto and Washington State University. The participants, who were enrolled in courses related to the TOC TP dur- ing the years 2016 and 2020, were queried regarding the time they allocated to the creation of logical trees within the TOC TP framework. The survey question posed was: ‘How long did you spend creating logical trees of TOC TP?’ The responses from this survey are intended to pro- vide quantitative insights into the time variability asso- ciated with CRT construction in diverse educational and experiential contexts. 4 Results The integration of ChatGPT into the IT project man- agement area, particularly within the construction of CRTs, has demonstrated notable enhancements in logical analysis. The application of ChatGPT has enabled project teams, the Project Management Office, and other stake- holders to gain deeper insights, develop more accurate assessments, and devise effective strategies for improving project performance. As outlined in previous section, the TOC TP method- ology starts with the identification of UDEs bothering the organization. In the CRT, these identified UDEs are logi- cally linked through intermediate entities, specifying rela- tionships down to a core problem. Analyzing the IT project management area has revealed multiple gaps (UDEs) and their interconnectedness across the organization. 4.1 Draft analysis ChatGPT can significantly speed up the data collec- tion process during the Draft analysis phase of the TOC TP. By retrieving specific data related to the project(s), ChatGPT can help gain a comprehensive understanding of the current state and identify potential UDEs. Additional- ly, ChatGPT can communicate with stakeholders in a way that is easy for them to understand, which can help to en- sure that stakeholders are on board with the CRT and the solutions that are proposed. An example of UDEs related to our study is shown in Figure 3. In addition to data retrieval, ChatGPT serves as an ef- fective brainstorming partner during the construction of the Draft CRT. By posing questions and scenarios related to project challenges, ChatGPT prompts the generation of alternative cause-and-effect relationships, uncovering hid- den UDEs and potential root causes. Through the analysis of large datasets and pattern identification, ChatGPT contributes to data-driven deci- sion-making during the Draft Analysis phase. The model can assist in prioritizing UDEs, as shown in Figure 4, based on their impact and probability, allow- ing us to focus on high-impact areas for improvement. ChatGPT’s instant responses enable quick answers to spe- cific questions, supporting stakeholders in resolving un- certainties and maintaining productive momentum in the analysis process. 4.2 Leaps of logic (long arrows) Long arrows, representing leaps of logic, are effective- ly addressed with ChatGPT during the TOC TP analysis phase. The large language model identifies and mitigates logical leaps that might not be obvious to humans, generat- ing text that comprehensively describes the current reality. Moreover, ChatGPT proposes multiple potential solutions for bridging these leaps, ensuring the identification of op- timal solutions. In the IT project management area, ChatGPT can be utilized to enhance the cause-and-effect relationship in a CRT. Figure 5 illustrates the preliminary cause-and-effect relationships within the IT project management area. By validating and critically reviewing the logical va- lidity of cause-and-effect relationships, as shown in Figure 6, ChatGPT contributes to improved input for constructing the CRT. This iterative process helps in refining and clari- fying the cause-and-effect relationships, thereby reducing the likelihood of errors or oversights in the final CRT. 207 Organizacija, V olume 57 Issue 2, May 2024 Research Papers Figure 3: UDEs in the IT Project management area Figure 4: Prioritized UDEs by ChatGPT 208 Organizacija, V olume 57 Issue 2, May 2024 Research Papers Figure 5: Initial cause-and-effect relationship 4.3 Current Reality Tree As mentioned in Chapter 1, the CRT is a visual rep- resentation of the cause-and-effect relationships within a system, highlighting the root causes of problems and con- straints. ChatGPT can be used to generate text describing the various elements of the CRT and the relationships between them. This text can be used to identify gaps in the analy- sis and potential areas for improvement. For example, as shown in Figure 8, ChatGPT could be used to ask ques- tions and encourage users to think critically about their be- liefs. This approach can engage users in a deeper explora- tion of their perspectives and viewpoints, leading to more insightful analysis and accelerate creation of CRT. Additionally, this process could help users uncover the assumptions underlying their problems, which is a crucial step in the CRT process. As illustrated in Figure 9, by us- ing ChatGPT to probe these underlying assumptions, us- ers can gain a clearer understanding of the foundational beliefs impacting their problem-solving approach. This accelerates the identification and examination of assump- tions, facilitating a more efficient and effective creation of CRT. The ability to swiftly pinpoint and challenge these assumptions is particularly beneficial in complex scenar- ios where they might be less obvious or more deeply in- grained. The TOC TP is not a one-time activity; it is an ongo- ing process that can be revisited and refined as new infor- mation becomes available or as the system evolves. For example, if a new constraint is identified, the CRT can be updated to reflect this change. An example of a simplified CRT from the area of IT project management is shown in Figure 10. 209 Organizacija, V olume 57 Issue 2, May 2024 Research Papers Figure 6: Improving cause-and-effect relationship for constructing CRT using ChatGPT 4.4 Time efficiency in CRT Construction The time it takes to create a CRT can vary depending on the complexity of the system being analyzed, the expe- rience of the person creating the CRT, and the availability of resources. To investigate the time it takes to create TOC TP logi- cal trees (without ChatGPT), we surveyed 40 students re- garding the time taken to create logic trees. The responses were categorized into five distinct time ranges: 0-3 hours, 4-6 hours, 7-9 hours, 10-15 hours, and more than 15 hours. The data revealed, as shown in Figure 10, a diverse range of time commitments among the participants. A significant 37% of students completed their logic trees within the shortest time frame of 0-3 hours, suggest- ing notable efficiency or familiarity with the task among a significant portion of them. Another 22,2% of students reported needing 4-6 hours, indicating a moderate level of complexity or effort. A considerable segment, 18,5% of the students fell into the 10-15 hours bracket, suggesting a high level of engagement or complexity in the task for these individuals. Similarly, 14,8%, indicated a substan- tially longer time investment of more than 15 hours, which might reflect the complexity of the task or varying levels of prior experience. Lastly, 7,4% reported spending 7-9 hours on their logic trees, positioning this group between the moderately complex and the more time-consuming categories. These findings highlight the broad range of time investments required to complete logic trees among students, with the variance possibly attributed to factors like individual student’s prior experience, understanding of the task, or the complexity of their specific logic trees. 210 Organizacija, V olume 57 Issue 2, May 2024 Research Papers Figure 7: Encourage critical thinking, challenging beliefs, when constructing CRT using ChatGPT However, for more complex systems, it typically takes several days or even weeks to complete, especially if the analysis requires a deep understanding of the underlying processes and data collection. The time is also affected by how thoroughly the team wants to investigate and validate each causal relationship. It is important to spend enough time to ensure accuracy, but it is also important to avoid overanalyzing and overcomplicating the CRT. Considering the diverse time investments required to create TOC TP logical trees, as shown in our survey, the integration of ChatGPT technology presents a transforma- tive advantage. ChatGPT’s usage in CRT construction sig- nificantly reduces the time and resources needed, especial- ly for complex systems that typically demand extensive analysis. Its ability to quickly process and analyze large datasets enables quicker identification of UDEs and more efficient construction of logical trees. GPT also aids in en- hancing the accuracy of the CRT by providing a more thor- ough examination of causal relationships, minimizing the risk of oversights or errors. This results in a more robust and reliable CRT, ensuring that critical issues are not just identified but are understood in their entirety. 5 Discussion The integration of ChatGPT into the construction of CRT within IT project management area, as explored in Chapter 4, represents a significant shift in approach and methodology. This chapter reflects on the implications and 211 Organizacija, V olume 57 Issue 2, May 2024 Research Papers Figure 8: Encourage critical thinking, challenging assumptions, when constructing CRT using ChatGPT challenges of this integration, spanning from draft analysis and data collection to the final construction of CRT. ChatGPT’s role in the Draft Analysis phase (Section 4.1) has shown a substantial improvement in the efficien- cy and comprehensiveness of data collection and analysis. This advancement is particularly impactful in gaining com- prehensive understanding of the current state and identify potential UDEs, enhancing both the speed and accuracy of the CRT construction process. Furthermore, as not- ed in Section 4.2, ChatGPT effectively addresses logical leaps within the CRT, aiding in the development of more coherent and logically sound structures. This contribution is crucial in ensuring that the CRTs accurately reflect the complexities and nuances of IT project management area. Moreover, the findings from Section 4.3 highlight ChatGPT’s capacity to encourage deeper analytical think- ing, guiding users to critically evaluate their beliefs and as- sumptions. This aspect of ChatGPT’s application enhances the depth of the CRT analysis, leading to a more insightful understanding of the cause-and-effect relationships. Such depth is indispensable for identifying the root causes of problems and devising effective strategies in project man- agement. 212 Organizacija, V olume 57 Issue 2, May 2024 Research Papers Figure 9: Resulting CRT (simplified) in the IT Project management area 213 Organizacija, V olume 57 Issue 2, May 2024 Research Papers Figure 10: Time needed to create logical trees of TOC TP (without ChatGPT) The results presented in Section 4.4 also sheds light on the time efficiency gained through ChatGPT integration in CRT construction, particularly in complex systems. This efficiency not only translates into time savings but also en- ables a more agile and responsive IT project management approach. However, despite these benefits, several challenges need to be addressed. First, ChatGPT is not perfect and can sometimes generate inaccurate or misleading text. This could lead to incorrect conclusions being drawn during the TOC TP process. Second, ChatGPT is not a replacement for human judgment. Humans still need to be involved in the TOC TP process to ensure that the results are accurate and that the solutions are feasible. Third, ChatGPT is not yet trained on the specific concepts and terminology used in the TOC TP. This means that it would need to be trained on this terminology before it could be used effectively in the TOC TP. Finally, it is important to ensure that ChatGPT is used in a safe and ethical manner. ChatGPT is a powerful tool and could be used to generate text that is misleading or harmful. Thus, maintaining a balance between ChatGPT generated insights and human expertise is essential. 6 Conclusion The integration of ChatGPT with TOC TP holds im- mense promise for enhancing logical analysis and de- cision-making. By leveraging ChatGPT’s language ca- pabilities and data insights, organizations can overcome constraints, make more informed choices, and achieve greater success. Our study demonstrated ChatGPT’s substantial impact on accelerating data collection, brainstorming, and validat- ing cause-and-effect relationships within CRTs. However, successful integration necessitates addressing challenges related to ChatGPT’s understanding, accuracy, and ethical use. Overcoming these hurdles is crucial for organizations to unlock the full potential of this integration and revolu- tionize decision-making. While our research focused on a specific IT project management case study, the findings provide valuable insights that can be extended to diverse organizational contexts. Further research and development are needed to refine ChatGPT’s capabilities and expand its applicability across various industries and decision-making domains. The synergistic combination of AI, exemplified by ChatGPT, and human expertise holds transformative po- tential for problem-solving and decision-making. Deci- sion-makers can leverage this partnership to drive progress and innovation in a wide range of fields. However, it is paramount to approach this integration with care, ensur- ing continuous improvements in both technological capa- bilities and ethical considerations. Responsible, fair, and transparent use of AI is essential for its successful adoption in managerial and societal contexts. In conclusion, our study contributes valuable insights into the integration of ChatGPT with TOC TP. By embrac- ing ethical considerations and addressing research gaps, we can pave the way for a future where AI augments hu- man decision-making, leading to more efficient, effective, and beneficial decision-making processes. Literature Arqum M., and More D. (2023). Aplying TOC Thinking Process Tools in Managing Challenges of Supply Chain Finance: a Case Study.” International Journal of Services and Operations Management 15.4 (2013): 389. Web. Chen Y ., Andiappan M., Jenkin T., and Ovchinnikov A. 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ChatGPT Powers A Different Kind Of Logic Analyzer. https://hackaday.com/2023/04/06/ chatgpt-powers-a-different-kind-of-logic-analyzer/, downloaded: July 18th 2023 Hanmeng L., Ruoxi N., Zhiyang T., Jian L., Qiji Z., and Yue Z. (2023). Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4. https://www.researchgate.net/publication/369911689_ Evaluating_the_Logical_Reasoning_Ability_of_ ChatGPT_and_GPT-4] Holt J., and Aljaž, T. (2020), EM 526 Lecture materials, Washington State University Scheinkopf L. J. (1999). Thinking for a Change: Putting the TOC Thinking Processes to Use, CRC Press; 1st edition Zamfiroiu A., Vasile D., and Savu D. (2023). ChatGPT–A Systematic Review of Published Research Papers. Informatica Economica. 2023;27(1):5-16. PMID: 36981544 Tomaž Aljaž, Ph.D., with over 26 years in Information & Telecommunication area, manages IT projects at Spar Slovenija and also working as adjunct professor at Faculty of Information Science, Novo mesto, Slovenia. His fields of interests in ICT area are related to resource management, project management and process improvements using the Theory of the Constraints methodology.