{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:05:36Z","timestamp":1777705536165,"version":"3.51.4"},"reference-count":57,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T00:00:00Z","timestamp":1711670400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2025,2,28]]},"abstract":"<jats:p>The paper aims to present a hybrid model for measuring the performance of business processes in complex organizations based on the subjective decision-making of expert teams. The subject of the research is finding ways to measure, analyze and improve the key performance indicators (KPIs) process. Obtaining the values of KPIs, which reflect the real state of the process, creates a basis for their ranking, i.e. insight into KPIs that are extremely important for the process as well as KPIs that are of lesser importance, but as such are not excluded from consideration because they are necessary for the beginning, realization and completion of the process. The model was compiled through five phases and was tested through a case study in a real business organization, which deals with the maintenance of complex combat systems. The obtained results helped the management to take certain measures in order to improve the performance of the maintenance process. In the model, it is proposed to form two expert teams, which make assessments based on experience and express them in linguistic terms according to a predefined scale. Modeling of linguistic expressions is realized using intuitive fuzzy sets of a higher order, more precisely Fermatean fuzzy sets (FFS). Selecting KPIs, decomposing the process into sub-processes and assessing the relative importance of sub-processes is carried out by one team of experts, while another team carries out the assessment of KPIs at the level of each sub-process. Determining the relative importance of sub-processes is realized using the Delphi method extended to FFS while reaching a consensus. The measurement of process performance, i.e. the value of KPIs, is realized using Multi-Criteria Group Decision-Making (MCGDM), such as the ELECTRE method extended with FFS. The sensitivity analysis of the developed model is realized by uncertainty modeling with q-rung orthopair fuzzy sets.<\/jats:p>","DOI":"10.3233\/jifs-238907","type":"journal-article","created":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T12:40:34Z","timestamp":1711716034000},"page":"197-213","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Ranking of asset maintenance process KPIs using Fermatean fuzzy Delphi and Fermatean fuzzy ELECTRE method"],"prefix":"10.1177","volume":"48","author":[{"given":"Vladimir","family":"Milovanovi\u0107","sequence":"first","affiliation":[{"name":"Military Academy, University of Defense in Belgrade, Republic of Serbia"}]},{"given":"Aleksandar","family":"Aleksi\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Engineering Science, University of Kragujevac, Republic of Serbia"}]},{"given":"Marjan","family":"Milenkov","sequence":"additional","affiliation":[{"name":"Military Academy, University of Defense in Belgrade, Republic of Serbia"}]},{"given":"Vlada","family":"Sokolovi\u0107","sequence":"additional","affiliation":[{"name":"Military Academy, University of Defense in Belgrade, Republic of Serbia"}]}],"member":"179","published-online":{"date-parts":[[2024,3,29]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110480"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.105992"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.116945"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119237"},{"key":"e_1_3_1_6_2","first-page":"2023","article-title":"Multi-attribute decision-making based on novel Fermatean fuzzy similarity measure and entropy measure","author":"Alahmadi R.A.","unstructured":"AlahmadiR.A., GanieA.H., Al-QudahY., KhalafM.M. and GanieA.H., Multi-attribute decision-making based on novel Fermatean fuzzy similarity measure and entropy measure, Granular Computing, 2023. https:\/\/doi.org\/10.1007\/s41066-023-00378-x.","journal-title":"Granular Computing"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.3390\/su14052686"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0165-0114(86)80034-3"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trpro.2023.02.194"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-201557"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.5937\/vojtehg67-18446"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-010-2765-5"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-219196"},{"key":"e_1_3_1_14_2","unstructured":"EnosD.D. Performance improvement: Makingithappen CRC Press 2007."},{"key":"e_1_3_1_15_2","unstructured":"European Founation for Quality Management: https:\/\/efqm.org\/the-efqm-model\/#."},{"issue":"1","key":"e_1_3_1_16_2","article-title":"From Fuzzy Sets to Deep Learning: Exploring the Evolution of Pattern Recognition Techniques","volume":"31","author":"Ganai S.A.","year":"2023","unstructured":"GanaiS.A., BhardwajN. and PadderR.A., From Fuzzy Sets to Deep Learning: Exploring the Evolution of Pattern Recognition Techniques, International Journal of Science, Mathematics and Technology Learning 31(1) (2023),ISSN: 2327-7971 (Print) ISSN: 2327-915X (Online)","journal-title":"International Journal of Science, Mathematics and Technology Learning"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.3390\/su14159373"},{"key":"e_1_3_1_18_2","unstructured":"HarringtonH.J. Business process improvement: The break through strategy for total quality productivity and competitiveness McGraw Hill Professional 1991."},{"key":"e_1_3_1_19_2","unstructured":"Vital Signs: Using Quality. Time andcost performance measurements to chart your company\u2019s future Amacom New York NY. 1993."},{"key":"e_1_3_1_20_2","unstructured":"http:\/\/www.cimlss.rs\/5s\/."},{"key":"e_1_3_1_21_2","unstructured":"JudsonA.S. Making Strategy Happen Transforming Plans into Reality. London: Basil Blackwell. 1990"},{"issue":"5","key":"e_1_3_1_22_2","first-page":"134","article-title":"Putting the Balanced Scorecard to Work","volume":"71","author":"Kaplan S.R.","year":"1993","unstructured":"KaplanS.R. and NortonD.P., Putting the Balanced Scorecard to Work, Harvard Business Review 71(5) (1993), 134\u2013147.","journal-title":"Harvard Business Review"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2012.10.031"},{"key":"e_1_3_1_24_2","first-page":"115","article-title":"A hybrid AHP-ELECTRE I multicriteria model for performance assessment and team selection. Organizational productivity and performance measurements using predictive modeling and analytics","author":"Khatrouch I.","year":"2017","unstructured":"KhatrouchI., KermadL., el MhamediA. and Boujelbene,Y., A hybrid AHP-ELECTRE I multicriteria model for performance assessment and team selection. Organizational productivity and performance measurements using predictive modeling and analytics, IGI Global (2017), 115\u2013127.","journal-title":"IGI Global"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-023-05395-w"},{"issue":"1","key":"e_1_3_1_26_2","first-page":"1","article-title":"Fermatean Fuzzy Type Statistical Concepts with Medical","volume":"4","author":"Kirisci M.","year":"2023","unstructured":"KirisciM., Fermatean Fuzzy Type Statistical Concepts with Medical, Fuzzy Optimization and Modelling 4(1) (2023), 1\u201314. E-ISNN: 2676-7007,https:\/\/doi.org\/10.30495\/fomj.2023.1982256.1082.","journal-title":"Fuzzy Optimization and Modelling"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.omega.2023.102849"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.24818\/RMCI.2023.2.228"},{"issue":"3","key":"e_1_3_1_29_2","first-page":"84","article-title":"Application of ELECTRE method in performance analysis of food retailers in Serbia","volume":"1","author":"Lukic R.","year":"2021","unstructured":"LukicR., Application of ELECTRE method in performance analysis of food retailers in Serbia, Business Excellence and Management 1(3) (2021a)84\u2013102. DOI:https:\/\/doi.org\/10.24818\/beman\/2021.11.3-05.","journal-title":"Business Excellence and Management"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.23055\/ijietap.2023.30.1.8479"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","unstructured":"Milovanovi\u0107V. Aleksi\u0107A. Sokolovi\u0107S. and MilenkovM. Uncertainty modeling using intuitionistic fuzzy number. Vojnotehni\u010dki glasnik\/Military technical courier 69(4) (2021) 905\u2013929. DOI: 10.5937\/vojtehg69-33301; https:\/\/doi.org\/10.5937\/vojtehg69-33301.","DOI":"10.5937\/vojtehg69-33301"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.5539\/ies.v8n9p171"},{"key":"e_1_3_1_33_2","doi-asserted-by":"crossref","unstructured":"NeelyA. Business Performance Measurement \u2013Theory and Practice Cambride University Press (2002) pp. 63.","DOI":"10.1017\/CBO9780511753695"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.12568"},{"key":"e_1_3_1_35_2","doi-asserted-by":"crossref","unstructured":"ParmenterD. Key performance indicators: Developing implementing and using winning KPIs. JohnWiley&Sons. 2015.","DOI":"10.1002\/9781119019855"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-020-01070-1"},{"issue":"1","key":"e_1_3_1_37_2","first-page":"57","article-title":"Implementing feedback systems to enhance productivity: A practical guide","volume":"10","author":"Pritchard R.D.","year":"1990","unstructured":"PritchardR.D., RothP-L., JonesS.D. and RothP.G., Implementing feedback systems to enhance productivity: A practical guide, Global Business and Organizational Excellence 10(1) (1990), 57\u201367.","journal-title":"Global Business and Organizational Excellence"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2023.01.260"},{"key":"e_1_3_1_39_2","first-page":"1","article-title":"Environmental Adaptation of Construction Barriers under Intuitionistic Fuzzy Theory,","volume":"15","author":"Rogulj K.","year":"2021","unstructured":"RoguljK. and Kili\u0107-Pamukovi\u0107J., Environmental Adaptation of Construction Barriers under Intuitionistic Fuzzy Theory, , Technical Journal 15 (2021), 1,1\u201310. https:\/\/doi.org\/10.31803\/tg-20210215210742.","journal-title":"Technical Journal"},{"key":"e_1_3_1_40_2","doi-asserted-by":"crossref","unstructured":"RosemannM. BrockeV.J. The six core elements of business process management U J. vom Brocke & M. Rosemann (Editori) Handbook on Business Process Management. Introduction methods and informations system. (2010) 107\u2013122. Berlin: Springer.","DOI":"10.1007\/978-3-642-00416-2_5"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2023.035480"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118497"},{"key":"e_1_3_1_43_2","doi-asserted-by":"crossref","unstructured":"SarajiM.K. StreimikieneD.D. and KyriakopoulosG.L. Fermatean Fuzzy CRITIC-COPRAS Method for Evaluating the Challenges to Industry 4.0 Adoption for a Sustainable Digital Transformation Sustainability 13 (2021) 9577. https:\/\/doi.org\/10.3390\/su13179577.","DOI":"10.3390\/su13179577"},{"issue":"2","key":"e_1_3_1_44_2","doi-asserted-by":"crossref","first-page":"391","DOI":"10.15388\/Informatica.2019.211","article-title":"Some new operations over Fermatean fuzzy numbers and application of Fermatean fuzzy WPM in multiple criteria decision making,b)","volume":"30","author":"Senapati T.","year":"2019","unstructured":"SenapatiT. and YagerR.R., Some new operations over Fermatean fuzzy numbers and application of Fermatean fuzzy WPM in multiple criteria decision making,b), Informatica 30(2) (2019), 391\u2013412.","journal-title":"Informatica"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-019-01377-0"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2019.05.012"},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-201760"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2021.103578"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.5937\/vojtehg64-10304"},{"issue":"2","key":"e_1_3_1_50_2","first-page":"163","article-title":"Selection of Alternative Filling Material in The Bed Production with AHP and ELECTRE Methods","volume":"7","author":"Vural D.","year":"2020","unstructured":"VuralD. and K\u00f6seE., Selection of Alternative Filling Material in The Bed Production with AHP and ELECTRE Methods, Journal of Applied Research on Industrial Engineering 7(2) (2020), 163\u2013176.","journal-title":"Journal of Applied Research on Industrial Engineering"},{"key":"e_1_3_1_51_2","doi-asserted-by":"crossref","unstructured":"WangJ. WeiG. WeiC. WeiY. MABAC method for multiple attribute group decision making under q-rung orthopair fuzzy environment Defence Technology 2019;https:\/\/doi.org\/10.1016\/j.dt.2019.06.019.","DOI":"10.1016\/j.dt.2019.06.019"},{"key":"e_1_3_1_52_2","doi-asserted-by":"crossref","unstructured":"WangY. YeoG.T. Intermodal route selection for cargo transportation from Korea to Central Asia by adopting Fuzzy Delphi and Fuzzy ELECTRE I methods Maritime Policy & Management 2017; ISSN: 0308-8839 Journal homepage:http:\/\/www.tandfonline.com\/loi\/tmpm20.","DOI":"10.1080\/03088839.2017.1319581"},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2011.04.010"},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.24018\/ejers.2019.4.10.1571"},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.1109\/IFSA-NAFIPS.2013.6608375"},{"key":"e_1_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jairtraman.2013.06.004"},{"key":"e_1_3_1_57_2","doi-asserted-by":"publisher","DOI":"10.3390\/systems11030162"},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.15388\/21-INFOR463"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-238907","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-238907","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-238907","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:44:09Z","timestamp":1777455849000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-238907"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,29]]},"references-count":57,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,2,28]]}},"alternative-id":["10.3233\/JIFS-238907"],"URL":"https:\/\/doi.org\/10.3233\/jifs-238907","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,29]]}}}