A Sugeno ANFIS Model Based on Fuzzy Factor Analysis for IS/IT Project Portfolio Risk Prediction
Risk inherence jeopardises Information System (IS) and Information Technology (IT) Project Portfolio Management (PPM) to realise the strategic objectives. Previous studies have mainly provided Artificial Intelligence (AI) and statistical models to predict the overall risk of IS/IT project portfolio,...
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| Format: | Article |
| Language: | English |
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Universiti Utara Malaysia Press
2024
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| Online Access: | https://repo.uum.edu.my/id/eprint/31267/1/JICT%2023%2002%202024%20139-176.pdf https://doi.org/10.32890/jict2024.23.2.1 |
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| author | Zaidouni, Anass Idrissi, Mohammed Abdou Janati Bellabdaoui, Adil |
| author_facet | Zaidouni, Anass Idrissi, Mohammed Abdou Janati Bellabdaoui, Adil |
| author_sort | Zaidouni, Anass |
| collection | UUM |
| description | Risk inherence jeopardises Information System (IS) and Information Technology (IT) Project Portfolio Management (PPM) to realise the strategic objectives. Previous studies have mainly provided Artificial Intelligence (AI) and statistical models to predict the overall risk of IS/IT project portfolio, whereas neuro-fuzzy models were rarely used. This paper proposes a Sugeno Adaptive Neuro-Fuzzy Inference System (ANFIS) model based on Fuzzy Factor Analysis (FFA) named ANFIS-OPR to predict the overall risk of IS/IT project portfolio from historical IS/IT project risk data. The ANFIS-OPR inputs are the relevant factor loadings resulting from the FFA application on the IS/IT projects risks set to cope with the curse of dimensionality. Then, the Sugeno ANFIS model is adopted to give strategic interpretability to the predicted IS/IT project portfolio overall risk by implementing the IS/IT Project Management Office (PMO) expert knowledge, represented by fuzzy rules, on the relationship between IS/IT project portfolio strategic alignment and the IS/IT projects risks. The ANFISOPR outputs are the predicted Overall Portfolio Risk (OPR) and Root Mean Square Error (RMSE). The paper also presents an IS/ IT PMO case study that shows the proposed ANFIS-OPR efficacy, which predicted the OPR values closely to the OPR estimates with an accepted RMSE of 0.108. The proposed ANFIS-OPR is a novel intelligent decision-making tool that enables the IS/IT PMO to monitor the OPR, considering its linkage with strategic alignment; thus, contingency plans can be carried out appropriately while ensuring that the IS/IT project portfolio is strategically aligned |
| first_indexed | 2024-10-01T01:44:13Z |
| format | Article |
| id | uum-31267 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-10-01T01:44:13Z |
| publishDate | 2024 |
| publisher | Universiti Utara Malaysia Press |
| record_format | eprints |
| spelling | uum-312672024-08-14T06:35:00Z https://repo.uum.edu.my/id/eprint/31267/ A Sugeno ANFIS Model Based on Fuzzy Factor Analysis for IS/IT Project Portfolio Risk Prediction Zaidouni, Anass Idrissi, Mohammed Abdou Janati Bellabdaoui, Adil T Technology (General) Risk inherence jeopardises Information System (IS) and Information Technology (IT) Project Portfolio Management (PPM) to realise the strategic objectives. Previous studies have mainly provided Artificial Intelligence (AI) and statistical models to predict the overall risk of IS/IT project portfolio, whereas neuro-fuzzy models were rarely used. This paper proposes a Sugeno Adaptive Neuro-Fuzzy Inference System (ANFIS) model based on Fuzzy Factor Analysis (FFA) named ANFIS-OPR to predict the overall risk of IS/IT project portfolio from historical IS/IT project risk data. The ANFIS-OPR inputs are the relevant factor loadings resulting from the FFA application on the IS/IT projects risks set to cope with the curse of dimensionality. Then, the Sugeno ANFIS model is adopted to give strategic interpretability to the predicted IS/IT project portfolio overall risk by implementing the IS/IT Project Management Office (PMO) expert knowledge, represented by fuzzy rules, on the relationship between IS/IT project portfolio strategic alignment and the IS/IT projects risks. The ANFISOPR outputs are the predicted Overall Portfolio Risk (OPR) and Root Mean Square Error (RMSE). The paper also presents an IS/ IT PMO case study that shows the proposed ANFIS-OPR efficacy, which predicted the OPR values closely to the OPR estimates with an accepted RMSE of 0.108. The proposed ANFIS-OPR is a novel intelligent decision-making tool that enables the IS/IT PMO to monitor the OPR, considering its linkage with strategic alignment; thus, contingency plans can be carried out appropriately while ensuring that the IS/IT project portfolio is strategically aligned Universiti Utara Malaysia Press 2024 Article PeerReviewed application/pdf en cc4_by https://repo.uum.edu.my/id/eprint/31267/1/JICT%2023%2002%202024%20139-176.pdf Zaidouni, Anass and Idrissi, Mohammed Abdou Janati and Bellabdaoui, Adil (2024) A Sugeno ANFIS Model Based on Fuzzy Factor Analysis for IS/IT Project Portfolio Risk Prediction. Journal of ICT, 23 (2). pp. 139-176. ISSN 1675-414X https://e-journal.uum.edu.my/index.php/jict/article/view/19784 https://doi.org/10.32890/jict2024.23.2.1 https://doi.org/10.32890/jict2024.23.2.1 |
| spellingShingle | T Technology (General) Zaidouni, Anass Idrissi, Mohammed Abdou Janati Bellabdaoui, Adil A Sugeno ANFIS Model Based on Fuzzy Factor Analysis for IS/IT Project Portfolio Risk Prediction |
| title | A Sugeno ANFIS Model Based on Fuzzy Factor Analysis for IS/IT Project Portfolio Risk Prediction |
| title_full | A Sugeno ANFIS Model Based on Fuzzy Factor Analysis for IS/IT Project Portfolio Risk Prediction |
| title_fullStr | A Sugeno ANFIS Model Based on Fuzzy Factor Analysis for IS/IT Project Portfolio Risk Prediction |
| title_full_unstemmed | A Sugeno ANFIS Model Based on Fuzzy Factor Analysis for IS/IT Project Portfolio Risk Prediction |
| title_short | A Sugeno ANFIS Model Based on Fuzzy Factor Analysis for IS/IT Project Portfolio Risk Prediction |
| title_sort | sugeno anfis model based on fuzzy factor analysis for is it project portfolio risk prediction |
| topic | T Technology (General) |
| url | https://repo.uum.edu.my/id/eprint/31267/1/JICT%2023%2002%202024%20139-176.pdf https://doi.org/10.32890/jict2024.23.2.1 |
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