Profit margin prediction in sustainable road freight transportation using machine learning. (10th September 2021)

• Record Type: Journal Article
• Authors: Budak, Ayşenur; Sarvari, Peiman Alipour
• Is Part Of: Journal of cleaner production. Volume 314(2021)
• Publication Date: 2021-09-10
• Subjects: Sustainable road transportation -- Freight trucking in sustainable transportation -- Machine learning -- Feature engineering -- Profit margin prediction; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2021.127990
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: With the increasing transportation activities, road freight transportation has caused significant impacts on sustainability. The necessity of establishing sustainable road freight transportation plans have emerged for businesses. Therefore, it is important to develop decision support models that can be used by managers for sustainable road freight transportation. The objective of study is to predict the profit margin given to customers for freight trucking in sustainable road transportation. For this purpose, the variables affecting the profit margin to be given to the customers in sustainable road freight transportation are determined in the light of experts and managers. A data framework has been created using three-dimensional sustainability factors and customer-based variables. A machine learning-based methodology is developed. The scenario-based empirical investigation has been performed. Two different streams have been developed. The first stream is based on traditional importance analysis and second stream considers Recency, Frequency and Monetary based feature engineering and Discrete Wavelet Transform for noise reduction. Three different machine learning algorithms which are Random Forest, Robust Regression and XGBoost used for these two streams. Six different scenarios are considered. Finally, the proposed methodology is applied to two sustainable road freight transportation firms in Turkey. It is aimed to achieve the best performance for profit margin prediction analysis along with demonstrating that the proposed approach provides higher evaluation accuracy. The benchmarking results revealed the superiority of the proposed approach, which trained and tested the prediction model using the stream of data coming from a combination of Discrete Wavelet Transform and feature extraction. The benchmarking and comparisons enact that enhanced XGBoost algorithms provides the best prediction result. This paper presents a novel approach to predict customer-based profit margin in sustainable road freight transportation sector by combining different machine-learning methods for the first time. This study also provides useful insights about strategic and sustainable development perspectives to managers. Highlights: Predicting the profit margin in sustainable road freight transportation. Three-dimensional sustainability factors and customer-based variables are used. Developing a new methodology based on machine learning. Best performing machine learning algorithm is highlighted. Provides managerial and sustainable development implications.