Akaryakıt İstasyonu Yakıt Satış Otomasyonu Veriler Üzerinden Bir Veri Madenciliği Çalışması

Year 2020, , 282 - 296, 28.06.2020

İlhan Tarımer , Buse Cennet Karadağ

https://doi.org/10.35193/bseufbd.611749

Abstract

Bu makale benzin istasyonlarında satılan benzin, otogaz gibi petrol ürünlerinin satış tahminlerine ilişkin bir çalışmadır. Akaryakıt tiplerinin gelecekteki satışlarını tahmin etmek için bazı veri madenciliği teknikleri kullanıldı ve sonuçlar karşılaştırmalı olarak sunuldu. Elde edilen sonuçların akaryakıt istasyonlarında tank yönetimi stratejisini etkilediği görülmektedir. Bu amaçla belirlenen bir akaryakıt istasyonu deposuna giren ürün miktarı ve istasyondan satılan akaryakıt miktarı verilerinden, istasyonun periyodik satış tahminleri çıkartılmıştır. Önce C# ve .NET dillerinde web tabanlı bir yazılım geliştirilmiş ve bununla ilişkili bir veritabanı kurulmuştur. Geliştirilen yazılımın admin ve kullanıcı adlı 2 ayrı girişi üzerinden anlık satış verilerinin kaydı tutulmuştur. Ardından bu veritabanından çekilen depo ve satış verileri üzerinde veri madenciliği yapılmıştır. Bu çalışma sonuçlarının turizm güzergâhında bulunan akaryakıt istasyonlarına depo yönetimi ve satış tahminleri konusunda destek vermesi ve istasyon için bir satış denetim düzeni kurulmasına yardımcı olması beklenmektedir.

Keywords

Yazılım, Akaryakıt satışı, Veri tahmini

Thanks

Muğla Sıtkı Koçman Üniversitesi

A Data Mining Case Study Over Fuel Sales Automation Data

Abstract

This paper is devoted to the sales estimations of petroleum products that are are sold at fuel stations such as auto gas and diesel. Some techniques of data mining were used to predict the future sales of fuel oils types and the results were presented comparatively. It is also seen that the results obtained effect the strategy of tank management at fuel stations. For this purpose, the periodic sales estimations of the station are deducted from the data of the quantity of products entering a fuel station depot and the amount of fuel sold from the station. First, a web-based software was developed in C # and .NET languages and a related database was established. Instant sales data was recorded through 2 separate entries called as admin and user of the developed software. Then, data mining was performed on the warehouse and sales data drawn from an associated database. The results of this study are expected to support fuel stations on the tourism route in terms of warehouse management and sales estimates and help to establish a sales control scheme for the station.

Keywords

Software, Fuel sales, Data estimation

References

• Eroglu, H. “Stock Control of Fuel Products and Evaluation of Wastes in Fuel Stations”, İstanbul, Turkey: Beykent University Graduate School of Social Sciences, 2019.
• Skeet, J., “C# in Depth, Manning Publications Company”, 4th ed., ISBN-13: 978-1617294532, 497 pages, 2019.
• Esposito, D., “Programming ASP.NET Core (Developer Reference)”, ISBN: 978-509304417, 416 pages, Publisher: Microsoft Press; 1st Edition, USA, May 19, 2018.
• Pala, Z. “Step by Step Web Applications by ASP.NET”, Turkmen Book-House, ISBN-13: 978-9756392485, 359 pages, 2006.
• Sonmez, E., Kacar, S., Web Laboratory Design Based Matlab Builder Ne and Asp.Net for Control Systems Course, Sakarya University Journal of Science, vol. 20, iss. 2, p.p. 155-165, 2016.
• [Online]. Available: https://jamshidhashimi.com/net-ve-c/ 26.08.2019.
• Yildiz, M., Seker, S. E., Data Mining Tools, YBS Encyclopedia, vol. 3, no. 4, p.p. 10–19, 2016.
• Silahtaroglu, G., “Data Mining Concepts and Algorithms”, ISBN: 9756797815, 3rd Publishment, 304 pages, Turkey, 2016.
• Kocaman, A.E., “Sentiment Analysis on Twitter by Data Mining Technics”, B.Sc. Thesis, Technology Faculty of Mugla Sitki Kocman University, Mugla, 2018.
• Atan, S., Data, Big Data and Business Administration, Balikesir University The Journal of Social Sciences Institute, vol. 19, no. 35, p.p. 137–153, 2016.
• Zhong, N., Zhou, L., “Methodologies for Knowledge Discovery and Data Mining”, in Proc. Third Pacific-Asia Conference, Pakdd-99, p. 26-28, China, 1999.
• Akpinar, H., Knowledge Discovery and Data Mining in Databases, İstanbul Univ. Journal of Business Administration, vol. 29, no. 1, p.p. 2000.
• [Online]. Available: https://www.ibm.com/tr-tr/products/spss-modeler 27.02.2020
• Silahtaroğlu, G., Data Mining, İstanbul:Papatya Publishing, 2016
• Bulut, F . (2016). The Right Professional Preference with Multilayer Perceptron. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering , 17 (1) , 97-109 . DOI: 10.18038/btda.45787
• Irmak, S., Köksal, C. D., Asilkan, Ö., (2012) Predicting Future Patient Volumes of the Hospitals by Using Data Mining Methods. International Journal of Alanya Faculty of Business, 4 (1),101-114.
• Alpar, R. (2003) Introduction to Applied Multivariate Statistical Methods 1. 2nd Edition, Nobel Book-House, Ankara.
• Efe, E., Bek, Y., Şahin, M. (2000) Statistical Methods with Solutions in SPSS II, Kahramanmaraş Sütçü İmam University Rectorate Publication No: 10, Kahramanmaraş.
• Çakır Zeytinoğlu, F. (2007) Effects of Current Assets of Businesses on Sales: Selection of the Best Regression Equation and Sectoral Comparison. Marmara University İ.İ.B.F. Journal, 23 (2): 331-349.
• Montgomery, D.C., Peck, E.A., Vining, G.G. (2001) Introduction to Linear Regression Analysis, 3rd Edition, John Wiley & Sons, New York
• Işık, A.(2006) Applied Statistics-II. Beta Edition, İstanbul.