Profit margin prediction in sustainable road freight transportation using machine learning
| Autor: | Aysenur Budak, Peiman Alipour Sarvari |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Feature engineering
Decision support system Computer science 020209 energy Strategy and Management Feature extraction 02 engineering and technology Machine learning computer.software_genre Industrial and Manufacturing Engineering 12. Responsible consumption Margin (machine learning) 11. Sustainability 0202 electrical engineering electronic engineering information engineering Profit margin 0505 law General Environmental Science Sustainable development Renewable Energy Sustainability and the Environment business.industry 05 social sciences Building and Construction Benchmarking Sustainability 050501 criminology Artificial intelligence business computer |
| Zdroj: | Journal of Cleaner Production |
| ISSN: | 0959-6526 |
| DOI: | 10.1016/j.jclepro.2021.127990 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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