TY  - JOUR
T1  - Quantitative Demand Forecasting of Spare Parts in The Aviation Industry: A Comparative Analysis
TT  - Havacilik Sektöründe Yedek Parçalarin Nicel Talep Tahmini: Karşilaştirmali Bir Analiz
AU  - Ekin, Emre
PY  - 2026
DA  - February
Y2  - 2025
DO  - 10.51785/jar.1677452
JF  - Journal of Aviation Research
JO  - JAR
PB  - İnan ERYILMAZ
WT  - DergiPark
SN  - 2687-3338
SP  - 1
EP  - 32
VL  - 8
IS  - 1
LA  - en
AB  - 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.
KW  - Demand Forecasting
KW  - Moving Averages Method
KW  - Simple Exponential Smoothing
KW  - Holt Winters Method
KW  - Holt's Linear Method.
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UR  - https://doi.org/10.51785/jar.1677452
L1  - https://dergipark.org.tr/tr/download/article-file/4779158
ER  -