Havacilik Sektöründe Yedek Parçalarin Nicel Talep Tahmini: Karşilaştirmali Bir Analiz

Abstract

İşletmeler, veriye dayalı planlar oluşturmalarını ve iyi bilgilendirilmiş iş kararları almalarını sağladığı için tahminlerden yararlanır. Arızalı ürün tahmini ise bir firma için gelecek arızalı parçaları tahmin ederek firmanın bu arızalara karşı hazırlıklı olmasını sağlar. Bu çalışmanın amacı; büyük bir havayolu bakım onarım firmasının en sık karşılaştığı 10 farklı ürün için ay bazında atölyelere gelen arızalı parçaların kantitatif tahmin teknikleri kullanılarak analiz edilmesi, en uygun tahmin modelinin belirlenmesi ve daha sonraki dönemlere ait arızalı ürün sayısını tahmin etmektir. Çalışmada, Ocak 2021 – Aralık 2022 dönemi verileri kullanılmıştır. Bu veriler ile 2023 yılı için arızalı ürün tahmini yapılmıştır. Dört farklı sayısal tahmin yöntemi kullanılmış ve bu dört farklı modelin tahmin etkinliği Ortalama Mutlak Hata (MAE), Ortalama Kare Hata (MSE) ve Ortalama Mutlak Yüzde Hata (MAPE) ölçüleri ile belirlenmiştir. Çalışma sonucunda; Basit Üstel Düzgünleştirme Yöntemi (α =0,90) en başarılı tahmin sonuçlarını verirken, Holt-Winters Yöntemi (α =0,50, β=0,30, γ=0,80) en başarısız tahmin sonuçlarını vermiştir. Sonuç olarak; bu çalışma arızalı parçaların tahmin edilmesinde kantitatif tahmin tekniklerinin kullanılmasının etkinliğini ortaya koymaktadır. Gelecekteki araştırmalarda tahmin doğruluğunu ve uyarlanabilirliğini daha da geliştirmek için farklı yöntemler veya makine öğrenimi algoritmaları çalışmaya dahil edilebilir.

Keywords

• Talep Tahmini
• Hareketli Ortalamalar Yöntemi
• Basit Üstel Düzgünleştirme
• Holt Winters Yöntemi
• Holt’un Doğrusal Yöntemi.

Quantitative Demand Forecasting of Spare Parts in The Aviation Industry: A Comparative Analysis

Abstract

Businesses benefit from forecasting, because it enables them to create data-driven plans and make well-informed business decisions. Defective product forecasting, on the other hand, predicts future defective parts for a company and ensures that the company is prepared for these shortcomings. This study's objective is to examine the defective parts arriving at the workshops monthly for 10 different products that are most frequently encountered by a large airline maintenance and repair company by using quantitative forecasting techniques, to determine the most appropriate forecasting model and to predict the number of defective products for the following periods. The study's data for the time frame January 2021 - December 2022 were used. With this data, defective product forecast for 2023 was made. Four different numerical forecasting methods were used and the forecasting efficiency of these four different models was determined by Mean Absolute Error (MAE), Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) measures. Because of the research, Simple Exponential Smoothing Method (α = 0.90) gave the most successful prediction results, while Holt-Winters Method (α = 0.50, β = 0.30, γ = 0.80) gave the least successful prediction results. Therefore, this study shows how effective it is to use quantitative prediction techniques in the prediction of defective parts. In future research, different methods perhaps the study can incorporate machine learning algorithms to advance improve prediction accuracy and adaptability.

Keywords

• Demand Forecasting
• Moving Averages Method
• Simple Exponential Smoothing
• Holt Winters Method
• Holt's Linear Method.

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