Leveraging predictive analytics for operational efficiency in automotive after-sales services
Yükleniyor...
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Selçuk Üniversitesi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
This 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.
Açıklama
Anahtar Kelimeler
Predictive Analytics, Operational Efficiency, Automotive After-Sales, Services, Inventory Management, Workforce Optimization, Tahmini Analitik, Operasyonel Verimlilik, Otomotiv Satış Sonrası, Hizmetler, Envanter Yönetimi, İş Gücü Optimizasyonu
Kaynak
Selcuk University Journal of Engineering Sciences
WoS Q Değeri
Scopus Q Değeri
Cilt
23
Sayı
3
Künye
İkizler, 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.
