Using Machine Learning Algorithms For Forecasting Rate of Return Product In Reverse Logistics Process

Abstract

Many textile products are in reverse logistics network due to mistakes made in activities such as sales forecasting, inventor y 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

Yapay Zeka ve Makine Öğrenmesi Tekniklerinin Lojistik Sektöründe Kullanımı

“…Tersine lojistik sürecine örnek olarak çalışmada nakliye, satış tahmini, stok planlama faaliyetlerinde meydana gelen hatalardan kaynaklanan iade ürünlerin oranları makine öğrenmesi yöntemleri ile tahmin edilmiştir. Tüketici davranış bilgilerinden hareketle veriler üzerinde sınıflandırma yöntemlerinden olan lineer regresyon, karar destek makinesi, yapay sinir ağı, karar ağacı algoritmaları kullanılarak iade oranları tahmin edildiğinde en iyi sonucu karar ağacı algoritmalarından olan MP5 algoritması vermiştir [59].…”