SmartISM 2.0: A Roadmap and System to Implement Fuzzy ISM and Fuzzy MICMAC
Interpretive structural modeling (ISM) is a widely used technique to establish hierarchical relationships among a set of variables in diverse domains, including sustainability. This technique is generally coupled with MICMAC (Matrice d’Impacts Croisés Multiplication Appliquée á un Classement (cross-...
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| Vydáno v: | Sustainability Ročník 16; číslo 20; s. 8873 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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MDPI AG
01.10.2024
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| ISSN: | 2071-1050, 2071-1050 |
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| Abstract | Interpretive structural modeling (ISM) is a widely used technique to establish hierarchical relationships among a set of variables in diverse domains, including sustainability. This technique is generally coupled with MICMAC (Matrice d’Impacts Croisés Multiplication Appliquée á un Classement (cross-impact matrix multiplication applied to classification)) to classify variables in four clusters, although the manual application of the technique is complex and prone to error. In one of the previous works, a novel concept of reduced conical matrix was introduced, and the SmartISM software was developed for the user-friendly implementation of ISM and MICMAC. The web-based SmartISM software has been used more than 48,123 times in 87 countries to generate ISM models and MICMAC diagrams. This work attempts to identify existing approaches to fuzzy ISM and fuzzy MICMAC and upscale the SmartISM to incorporate fuzzy approaches. The fuzzy set theory proposed by Zadeh 1965 and Goguen 1969 helps the decision makers to provide their input with the consideration of vagueness in the real environment. The systematic review of 32 studies identified five significant approaches that have used different linguistic scales, fuzzy numbers, and defuzzification methods. Further, the approaches have differences in either using single or double defuzzification, and the aggregation of inputs of decision makers either before or after defuzzification, as well as the incorporation of transitivity either before or after defuzzification. A roadmap was devised to aggregate and generalize different approaches. Further, two of the identified approaches have been implemented in SmartISM 2.0 and the results have been reported. Finally, the comparative analysis of different approaches using SmartISM 2.0 in the area of digital transformation shows that, with a wide flexibility of fuzzy scales, the results converge and improve the confidence in the final model. The roadmap and SmartISM 2.0 will help in the implementation of fuzzy ISM and fuzzy MICMAC in a more robust and informed way. |
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| AbstractList | Interpretive structural modeling (ISM) is a widely used technique to establish hierarchical relationships among a set of variables in diverse domains, including sustainability. This technique is generally coupled with MICMAC (Matrice d’Impacts Croisés Multiplication Appliquée á un Classement (cross-impact matrix multiplication applied to classification)) to classify variables in four clusters, although the manual application of the technique is complex and prone to error. In one of the previous works, a novel concept of reduced conical matrix was introduced, and the SmartISM software was developed for the user-friendly implementation of ISM and MICMAC. The web-based SmartISM software has been used more than 48,123 times in 87 countries to generate ISM models and MICMAC diagrams. This work attempts to identify existing approaches to fuzzy ISM and fuzzy MICMAC and upscale the SmartISM to incorporate fuzzy approaches. The fuzzy set theory proposed by Zadeh 1965 and Goguen 1969 helps the decision makers to provide their input with the consideration of vagueness in the real environment. The systematic review of 32 studies identified five significant approaches that have used different linguistic scales, fuzzy numbers, and defuzzification methods. Further, the approaches have differences in either using single or double defuzzification, and the aggregation of inputs of decision makers either before or after defuzzification, as well as the incorporation of transitivity either before or after defuzzification. A roadmap was devised to aggregate and generalize different approaches. Further, two of the identified approaches have been implemented in SmartISM 2.0 and the results have been reported. Finally, the comparative analysis of different approaches using SmartISM 2.0 in the area of digital transformation shows that, with a wide flexibility of fuzzy scales, the results converge and improve the confidence in the final model. The roadmap and SmartISM 2.0 will help in the implementation of fuzzy ISM and fuzzy MICMAC in a more robust and informed way. |
| Audience | Academic |
| Author | Ahmad, Naim |
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| Cites_doi | 10.1108/IMDS-03-2017-0109 10.1108/JEIM-09-2019-0301 10.1108/SRJ-12-2020-0485 10.1142/9789812709677_0193 10.1016/j.ergon.2018.08.011 10.4018/jisscm.2013010105 10.1109/IIHMSP.2007.4457683 10.1109/TSMC.1974.5408523 10.1504/IJPQM.2018.094768 10.1007/978-3-642-14880-4_37 10.1007/s40171-014-0087-4 10.1371/journal.pone.0239140 10.1007/s10479-021-04072-6 10.1002/wics.82 10.1007/978-981-15-2647-3_15 10.1177/2278682120908557 10.1016/0016-3287(86)90094-7 10.1016/j.eswa.2006.01.035 10.1016/j.rico.2023.100300 10.1142/S0218488503002387 10.1016/j.asoc.2014.11.013 10.3390/math9040447 10.3390/su13168801 10.1109/TSMC.1974.5408524 10.1016/S0898-1221(99)00172-8 10.1007/BF00485654 10.1155/2016/6945184 10.1371/journal.pone.0249046 10.1007/s40171-012-0008-3 10.1080/01969727608927531 10.1016/S0019-9958(65)90241-X 10.1007/978-94-010-0646-0 10.1108/JM2-08-2014-0065 10.1007/s10696-011-9076-5 10.1016/j.fss.2020.05.008 10.3934/jimo.2024055 10.46300/9106.2021.15.137 |
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| Title | SmartISM 2.0: A Roadmap and System to Implement Fuzzy ISM and Fuzzy MICMAC |
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