TY - JOUR
T1 - Prioritization of Incident Management Process using ITIL-Fuzzy for Informatics Development Sector of Private Universities
AU - Vivian, Olano Garces Luisa
AU - Alessandra, Olano Garces Lidia
AU - Esther, Casildo Bedón Nancy
AU - Danny, Levano Rodriguez
AU - Jesús, Soria Quijaite Juan
N1 - Publisher Copyright:
© The Korean Institute of Intelligent Systems
PY - 2025
Y1 - 2025
N2 - The integration of fuzzy logic with the Information Technology Infrastructure Library (ITIL) practices for incident prioritization in technological development represents an innovative proposal, especially for systematizing processes in large organizations, including private universities. This study aimed to design and develop a fuzzy inference system (FIS) model for incident prioritization using the educational processes of a private university during periods 2023-II and 2024-I as a case study. This approach focused on evaluating how the ITILfuzzy model can improve response times in managing technological incidents, ensuring a high quality of service. The FIS model was implemented with 432 fuzzy rules using the Mamdani method to generate an inference engine. During the research, the current “AS-IS” incident management process (96 rules), the fuzzy logic-based prioritization model, and the optimized “TO-BE” process (178 rules) were analyzed. The results showed a significant reduction in incident resolution times: the average time for the “AS-IS” model was 119.64 hours. In comparison, the “TO-BE” model managed to reduce it to 43.91 hours, representing a decrease of 63.30%. The effectiveness of the model was validated by a Student t-test, obtaining tca = −4.88, tcritical = −1.65 and p-value = 0.000, confirming that the resolution time of the “TO-BE” model is significantly shorter. In addition, the process capability index Cp = 1.423 and the Taguchi index Cpm = 1.12 were determined, complying with the specifications of the optimized “TO-BE” process (7.99 ± 1.55 minutes, with a goal of 7.99 minutes). In conclusion, the integration of ITIL and fuzzy logic significantly improved incident resolution times, suggesting a high potential for its implementation in other private universities with similar operational structures.
AB - The integration of fuzzy logic with the Information Technology Infrastructure Library (ITIL) practices for incident prioritization in technological development represents an innovative proposal, especially for systematizing processes in large organizations, including private universities. This study aimed to design and develop a fuzzy inference system (FIS) model for incident prioritization using the educational processes of a private university during periods 2023-II and 2024-I as a case study. This approach focused on evaluating how the ITILfuzzy model can improve response times in managing technological incidents, ensuring a high quality of service. The FIS model was implemented with 432 fuzzy rules using the Mamdani method to generate an inference engine. During the research, the current “AS-IS” incident management process (96 rules), the fuzzy logic-based prioritization model, and the optimized “TO-BE” process (178 rules) were analyzed. The results showed a significant reduction in incident resolution times: the average time for the “AS-IS” model was 119.64 hours. In comparison, the “TO-BE” model managed to reduce it to 43.91 hours, representing a decrease of 63.30%. The effectiveness of the model was validated by a Student t-test, obtaining tca = −4.88, tcritical = −1.65 and p-value = 0.000, confirming that the resolution time of the “TO-BE” model is significantly shorter. In addition, the process capability index Cp = 1.423 and the Taguchi index Cpm = 1.12 were determined, complying with the specifications of the optimized “TO-BE” process (7.99 ± 1.55 minutes, with a goal of 7.99 minutes). In conclusion, the integration of ITIL and fuzzy logic significantly improved incident resolution times, suggesting a high potential for its implementation in other private universities with similar operational structures.
KW - Defuzzification
KW - Fuzzy logic
KW - Incident management
KW - ITIL
KW - Prioritization
UR - https://www.scopus.com/pages/publications/105001354415
U2 - 10.5391/IJFIS.2025.25.1.37
DO - 10.5391/IJFIS.2025.25.1.37
M3 - Article
AN - SCOPUS:105001354415
SN - 1598-2645
VL - 25
SP - 37
EP - 54
JO - International Journal of Fuzzy Logic and Intelligent Systems
JF - International Journal of Fuzzy Logic and Intelligent Systems
IS - 1
ER -