An intelligent decision support system for warranty claims forecasting: Merits of social media and quality function deployment

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dc.authoridTirkolaee, Erfan Babaee/0000-0003-1664-9210
dc.authoridNikseresht, Ali/0000-0002-6107-7699
dc.authoridNikookar, Ethan/0000-0002-4626-367X
dc.authorwosidTirkolaee, Erfan Babaee/U-3676-2017
dc.authorwosidNikseresht, Ali/AAW-8030-2020
dc.contributor.authorNikseresht, Ali
dc.contributor.authorShokouhyar, Sajjad
dc.contributor.authorTirkolaee, Erfan Babaee
dc.contributor.authorNikookar, Ethan
dc.contributor.authorShokoohyar, Sina
dc.date.accessioned2024-05-19T14:50:21Z
dc.date.available2024-05-19T14:50:21Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractThis work develops a novel approach based on Machine Learning (ML)-assisted Quality Function Deployment (QFD) to sift the gold from the stone. It includes Time-Varying Filter-based Empirical Mode Decomposition (TVFEMD), Deep Ensemble Random Vector Functional Link (DE-RVFL), and a Bayesian optimization algorithm for optimizing the shaped DE-RVFLTVF-EMD hyperparameters. This approach makes it possible for the proposed methods to be dynamic enough to deal with the data's volatility, complexity, uncertainty, and ambiguity. It is demonstrated that incorporating TVF-EMD to provide time-frequency analysis along DE-RVFL, and goal-oriented social media analytics boosts the performance of out-of-sample predictions statistically and compensates for the warranty data maturation effect. The proposed algorithm's Root Mean Square Error (RMSE) decreases by 23.37%-88.76% relative to other benchmark cutting-edge models. This study contributes significantly to the services management community. Using the proposed methodology, managers could create plans for warranty claims strategies that reduce inventory levels and waste while optimizing customer satisfaction, advocacy, and revenues. These merits provide incentives and support for policymakers to adopt advanced technologies, such as the ones developed and implemented in the current study, in warranty claims forecasting to improve accuracy and efficiency.en_US
dc.identifier.doi10.1016/j.techfore.2024.123268
dc.identifier.issn0040-1625
dc.identifier.issn1873-5509
dc.identifier.scopus2-s2.0-85185197179en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.techfore.2024.123268
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5681
dc.identifier.volume201en_US
dc.identifier.wosWOS:001188539900001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Science Incen_US
dc.relation.ispartofTechnological Forecasting and Social Changeen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectWarranty Claims Predictionen_US
dc.subjectSocial Media Analyticsen_US
dc.subjectQuality Function Deploymenten_US
dc.subjectTime-Frequency Analysisen_US
dc.subjectDeep Learningen_US
dc.subjectDeep Ensemble Random Vector Functional Linken_US
dc.titleAn intelligent decision support system for warranty claims forecasting: Merits of social media and quality function deploymenten_US
dc.typeArticleen_US

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