Implementation of TISM and fuzzy MICMAC analysis in Jupyter notebook with Python
1
Issued Date
2024-01-01
Resource Type
Scopus ID
2-s2.0-85205347886
Journal Title
System Innovation for a Troubled World: Applied System Innovation IX
Start Page
53
End Page
57
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SCOPUS
Bibliographic Citation
System Innovation for a Troubled World: Applied System Innovation IX (2024) , 53-57
Suggested Citation
Pookkaman W., Samanchuen T., Sirisawat S., Boonkong K. Implementation of TISM and fuzzy MICMAC analysis in Jupyter notebook with Python. System Innovation for a Troubled World: Applied System Innovation IX (2024) , 53-57. 57. doi:10.1201/9781003460763-12 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/101539
Title
Implementation of TISM and fuzzy MICMAC analysis in Jupyter notebook with Python
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Abstract
Total Interpretive Structural Modeling (TISM) and fuzzy Matrixed Impacts Croises Multiplication Appliquee a a Classement (fuzzy MICMAC) are factor analysis techniques to evaluate the relationships among the factors on one interest topic. More works have started to use these techniques as the primary analysis tool. This paper introduces a new implementation of TISM and fuzzy MICMAC analysis using Jupyter Notebook and Python. The motivation behind this implementation is the limited availability of suitable software tools for conducting this type of analysis. The methodology used to implement the function is detailed in the paper. The TISM analysis function is implemented using an iterative algorithm approach, while the fuzzy MICMAC function is implemented using a matrix approach. The function allows for user input of criteria and factors, and outputs interpretive structural modeling and cross-impact matrix calculations, as well as a stability graph for fuzzy MICMAC analysis. To validate the implementation, the results generated by the function were compared to those obtained in previous research. The comparison showed that our implementation provides accurate results that align with those generated by the previous research. Furthermore, the function was found to be easy to use and allowed researchers to input their data and obtain results quickly.