Leveraging predictive analytics for operational efficiency in automotive after-sales services

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dc.authorid0000-0001-8698-8133
dc.authorid0000-0002-8646-0950
dc.authorid0000-0001-8214-825X
dc.contributor.authorİkizler, Tuğçe
dc.contributor.authorÖzçelik, Abdullah Engin
dc.contributor.authorUslu, Banu Çalış
dc.date.accessioned2025-02-13T07:11:37Z
dc.date.available2025-02-13T07:11:37Z
dc.date.issued2024
dc.departmentEnstitüler, Fen Bilimleri Enstitüsü, Makina Mühendisliği Ana Bilim Dalı
dc.departmentSelçuk Üniversitesi, Teknoloji Fakültesi, Makine Mühendisliği Bölümü
dc.description.abstractThis research explores the application of predictive analytics to optimize operational efficiency in the automotive after-sales sector, focusing on inventory management and workforce allocation. By employing ARIMA and SARIMA models, seasonal and trend-based forecasts were generated using data collected from multi-brand service centers between 2018 and 2021. The results demonstrated a strong seasonal influence on service demand, with peaks identified in the second and fourth quarters, aligning with routine maintenance patterns. Key findings revealed a 32% dependency between technician numbers and spare part usage, while daily replacement volumes ranged from 89 to 327 parts, requiring precise workforce planning during peak periods. The originality of this research lies in its integration of predictive analytics into after-sales service management, an area where empirical studies are scarce. Unlike traditional approaches, this study not only highlights the significance of after-sales services in customer satisfaction but also provides actionable insights for cost reduction and resource optimization. For instance, the forecasting models facilitated dynamic inventory management, reducing holding costs while maintaining service reliability. Additionally, seasonality analysis guided the efficient allocation of technicians, minimizing operational downtime and improving customer experiences. These findings underscore the transformative potential of predictive analytics in the automotive industry. By leveraging data-driven insights, businesses can enhance their operational resilience and competitiveness, laying the groundwork for more sustainable and efficient service systems. This research addresses a critical gap in the literature by demonstrating how predictive models can directly contribute to strategic decision making in after-sales services.
dc.identifier.citationİkizler, T., Özçelik, A. E., Uslu, B. Ç. (2024). Leveraging predictive analytics for operational efficiency in automotive after-sales services. Selcuk University Journal of Engineering Sciences, 23 (3), 92-98.
dc.identifier.endpage98
dc.identifier.issn2757-8828
dc.identifier.issue3
dc.identifier.startpage92
dc.identifier.urihttps://hdl.handle.net/20.500.12395/54401
dc.identifier.volume23
dc.institutionauthorİkizler, Tuğçe
dc.institutionauthorÖzçelik, Abdullah Engin
dc.institutionauthorid0000-0001-8698-8133
dc.institutionauthorid0000-0002-8646-0950
dc.language.isoen
dc.publisherSelçuk Üniversitesi
dc.relation.ispartofSelcuk University Journal of Engineering Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectPredictive Analytics
dc.subjectOperational Efficiency
dc.subjectAutomotive After-Sales
dc.subjectServices
dc.subjectInventory Management
dc.subjectWorkforce Optimization
dc.subjectTahmini Analitik
dc.subjectOperasyonel Verimlilik
dc.subjectOtomotiv Satış Sonrası
dc.subjectHizmetler
dc.subjectEnvanter Yönetimi
dc.subjectİş Gücü Optimizasyonu
dc.titleLeveraging predictive analytics for operational efficiency in automotive after-sales services
dc.typeArticle

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