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Öğe Leveraging predictive analytics for operational efficiency in automotive after-sales services(Selçuk Üniversitesi, 2024) İkizler, Tuğçe; Özçelik, Abdullah Engin; Uslu, Banu ÇalışThis 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.Öğe Konya Mavi Tünel İçme Suyu Uygulama Projesinin Çevreye Olan Etkilerin Değerlendirilmesi(Selçuk Üniversitesi, 2017) Büyükkaracığan, Naci; Demiröz, Atila; Mobarez, Abdul HakimKonya Mavi Tünel İçme Suyu Projesi, Konya il merkezi, Çumra ilçe merkezi, İçeri Çumra ve civar yerleşim yerlerinde yaşayan yaklaşık 2 milyon 200 bin kişinin 2045 yılına kadar olan içme ve kullanma suyu ihtiyacını karşılayacaktır. Proje ile sağlıklı, temiz, güvenli ve kesintisiz içme ve kullanma suyu ihtiyacı karşılanmış olacaktır. Bunun yanı sıra, Konya ‘ nın ihtiyaç duyduğu kaliteli içme suyunu temin edecek tesisler ile, yeraltı suları üzerindeki baskı azaltılacak, sulardan kaynaklanan hastalıklar önlenecek ve buna bağlı sağlık giderlerinde de azalma sağlanacaktır. Bu çalışmada, Konya Mavi Tünel İçme Suyu Projesi’ nin başta su ekosistemi olmak üzere toprak kaynakları, taşkın hidroloji, fiziksel ve biyolojik, sosyo- ekonomik gibi çevreye olan etkileri araştırılmıştır. Bunun yanında, proje ile kazanılacak olan çevresel faydalar da incelenmiştir. Sonuç olarak söz konusu çevresel fayda ve zararlar değerlendirilmiştir.Öğe Prediction of Diesel Engine Performance Using Biofuels with Artificial Neural Network(Pergamon-Elsevier Science Ltd, 2010) Oğuz, Hidayet; Sarıtaş, İsmail; Baydan, Hakan EmreBiodiesel, bioethanol and biogas are the most important alternative fuels produced by using biologic origin sources. Effect of biofuel on engine performance is one of the research subjects of today. The engine experiments to test the engines are many times are hard, time consuming and high cost. Additionally, it is impossible to perform the test outside of limiting values. In this study, an artificial neural network, an artificial intelligence technique, is developed to successfully apply on automotive sector as well as many different areas of technology aiming to overcome difficulties of the experiments, minimize the cost, time and workforce waste. Diesel fuel, biodiesel, B20 and bioethanol-diesel fuel having different percentages (5%, 10%, and 15%) and biodiesel were mixed together, to use in developed artificial neural network Mixtures were also controlled for their fuel properties and motor experiments were performed to collect the reference values. Power, moment, hourly fuel consumption and specific fuel consumption were estimated by using the artificial neural network developed by using the reference values. Estimated values and experiment results are compared. As a result, from the performed statistical analyses, it is seen that realized artificial intelligence model is an appropriate model to estimate the performance of the engine used in the experiments. Reliability value is calculated as 99.94% (p = 0.9994 and p > 0.05) by using statistical analyses.Öğe On-line Prediction of Tool Wears by Using Methods of Artificial Neural Networks and Fuzzy Logic(ACADEMIC JOURNALS, 2010) Başçiftçi, Fatih; Şeker, HüseyinThe goal of this study is prediction of tool wear with integrated system made by on-line monitoring of the changes on tool during cutting operations with using artificial neural networks and fuzzy logic methods. For best monitoring, the tool condition, multiple sensor data are collected to represent the tool condition. Artificial neural networks with different parameters was first trained with sample experimental data and then tested with test data. Fuzzy logic is used for the classification of tool wear which is estimated with neural network according to the predefined levels. Results with 100% accuracy are gained by fuzzy process in predefined classes. The software written for this study can be used to monitor tool condition on-line, saving sensor data, viewing the process on graphic and producing alarm-control signals when it is necessary.
