alphanumeric journal
The Journal of Operations Research, Statistics, Econometrics and Management Information Systems
Using Machine Learning Algorithms For Forecasting Rate of Return Product In Reverse Logistics Process
Ayşe Nur Adıgüzel Tüylü, Ph.D.
Abstract
Many textile products are in reverse logistics network due to mistakes made in activities such as sales forecasting, inventory planning and distribution. In order to reduce resource usage and cost at first step, in addition to producing the correct quantity, these products must be sent to branches, in correct properties (amount, color, size, model…) and transportation planning and stock planning should be done correctly. Statistical methods, artificial intelligence and machine learning methods are used because of the difficulty of establishing mathematical models in multi-parameter and multi-variable problems. In general, all these activities are based on demand forecasts by time series, but there are important differences between these demand predictions and the actual demands because of fashion and consumers’ requests change very quickly. Artificial intelligence and machine learning methods provide faster and more accurate results in complex data sets. The difference of this study from other studies is to estimate the product return rates in Reverse Logistics with Machine Learning. In this direction, it is aimed to predict the claims accurately by concentrating on the customers' preferences, their reasons and the replies of the products which are sold to the customers. Thus, the consumer information obtained as a result of these analyzes can provide us with more accurate planning in terms of avoiding unnecessary production, transportation and storage activities, and sending the products with the correct properties; amount, color, size and model, to the branches. Best results (the correlation coefficient value is 82.35% and lowest error metrics) of this study are obtained with M5P algorithms of machine learning techniques
Keywords: Forecasting Rate of Return Product, Machine Learning, Reverse Logistics, Textile
Jel Classification: C01
Tersine Lojistik Sürecinde İade Oranlarının Tahmini İçin Makine Öğrenme Algoritmalarının Kullanılması
Öz
Satış tahmini, stok planlama ve dağıtım gibi faaliyetlerde yapılan hatalar nedeni ile birçok tekstil ürünü tersine lojistik ağına girmektedir. Kaynak kullanımını ve maliyeti en başta azaltmak için doğru sayıda üretimin yanı sıra bu ürünlerin doğru şubelere doğru sayıda, renkte, bedende ve modelde gönderilmesi, nakliyesinin ve stok planlamasının doğru bir şekilde yapılması gerekmektedir. Çok parametreli ve çok değişkenli problemlerde matematiksel model kurmanın zorluğu nedeniyle istatistiksel yöntemler, yapay zeka yöntemleri ve makine öğrenme yöntemleri kullanılmaktadır. Genel olarak tüm bu faaliyetler zaman serisine dayalı talep tahminleri baz alınarak yapılır, fakat moda ve tüketicilerin çok çabuk değişen istekleri nedeniyle talep tahminleri ile gerçekleşen talepler arasında önemli farklılıklar doğmaktadır. Son dönemde yapılan çalışmalar gösteriyor ki bu şekilde karmaşık yapılı büyük veri setlerinde yapay zeka ve makine öğrenme yöntemleri diğer tahmin yöntemlerine göre doğruluğu daha yüksek sonuçlar vermektedir. Bu çalışmada diğer çalışmalardan faklı olarak Tersine Lojistikte ürün iade oranlarının ilk defa Makine Öğrenme yöntemleri ile tahmin edilmesi yapılmıştır. Bu kapsamda müşterilerin tercihleri ile birlikte satışa çıkan ürünlerin iadeleri ve nedenleri üzerinde yoğunlaşılıp iadelerin daha doğru bir şekilde tahmin edilmesi amaçlanmıştır. Elde edilen analizler sonucunda şubelere doğru beden, renk ve modelde ürünlerin gitmesi; gereksiz üretim, nakliye ve depolama faaliyetlerinden kaçınılması; maliyetin, kaynak kullanımının ve çevre kirliliğinin azaltılması; kaçınılamayan nakliye ve depolama maliyetlerinin tahmin edilmesi konularında daha doğru bir planlama yapılması sağlanmıştır. Makine Öğrenme tekniklerinden M5P algoritması ile en iyi tahmin performansına (% 82,35 korelasyon katsayısı ve en düşük hata ölçütleri) ulaşmıştır.
Anahtar Kelimeler: Makine Öğrenme, Tekstil, Tersine Lojistik, Ürün İade Oran Tahmini
Suggested citation
(). Using Machine Learning Algorithms For Forecasting Rate of Return Product In Reverse Logistics Process. Alphanumeric Journal, 7(1), 143-156. http://dx.doi.org/10.17093/alphanumeric.541307
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2019.07.01.OR.05
alphanumeric journal
Pages 143-156
Received: March 18, 2019
Accepted: June 20, 2019
Published: June 30, 2019
2019 Adıgüzel Tüylü, AN., Eroğlu, E.
This is an Open Access article, licensed under Creative Commons Attribution-NonCommercial 4.0 International License.
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