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Fusion: Practice and Applications
Volume 12 , Issue 1, PP: 24-37 , 2023 | Cite this article as | XML | Html |PDF

Title

Machine Learning Fusion and Data Analytics Models for Demand Forecasting in the Automotive Industry: A Comparative Study

  Esraa Kamal 1 * ,   Amal F. Abdel-Gawad 2 ,   Basem Ibraheem 3 ,   Shereen Zaki 4

1  Decision Support Department, Faculty of Computers and Informatics Zagazig University, Zagazig, 44519, Egypt
    (esraakamal183@gmail.com)

2  Decision Support Department, Faculty of Computers and Informatics Zagazig University, Zagazig, 44519, Egypt
    (amgawad2001@yahoo.com)

3  Computer and Systems Department, Electronics Research Institute ,Giza , Egypt
    (Basem@eri.sci.eg)

4  Decision Support Department, Faculty of Computers and Informatics Zagazig University, Zagazig, 44519, Egypt
    (szsoliman@zu.edu.eg)

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Doi   :   https://doi.org/10.54216/FPA.120102

Received: January 09, 2023 Revised: April 08, 2023 Accepted: June 03, 2023

Abstract :

Demand forecasting is a crucial aspect of managing the supply chain, as it helps companies optimize inventory levels and minimize expenses related to inventory shortages. In recent years, machine learning (ML) algorithms have gained popularity for demand forecasting, as they can handle large and complex datasets and provide accurate predictions. Precise demand prediction for car brands is vital for companies to minimize costs and prevent inventory shortages. The demand for distributing cars is a critical component of inventory management. However, estimating demand for new car sales is difficult due to its continuous nature. To address this challenge, a study was conducted to train, test, and compare the performance of five machine learning algorithms (Random Forest, Multiple Linear Regression, k-Nearest Neighbors, Extreme Gradient Boosting, and Support Vector Machine) using a benchmark dataset. Among all the experiments, the Support Vector Machine algorithm achieved the highest accuracy score of 71.42%. Moreover, Multiple Linear Regression performed well, with an accuracy score of 66.66%. On the other hand, the Extreme Gradient Boosting algorithm had the lowest accuracy score of 42.85%. All experiments used a train-test split of 75/25.

Keywords :

Demand Forecasting; Machine Learning; Multiple Linear Regression; Support Vector Machine; K-nearest Neighbors; Random Forest; Extreme Gradient Boosting. 

References :

[1] Fosso Wamba, Samuel, et al. "Big data analytics in logistics and supply chain management." The International Journal of Logistics Management 29.2 (2018): 478-484.

[2] Weng, Tingyu, Wenyang Liu, and Jun Xiao. "Supply chain sales forecasting based on lightGBM and LSTM combination model." Industrial Management & Data Systems 120.2 (2020): 265-279.

[3] Petropoulos, Fotios, Rob J. Hyndman, and Christoph Bergmeir. "Exploring the sources of uncertainty: Why does bagging for time series forecasting work?." European Journal of Operational Research 268.2 (2018): 545-554.

[4] KAYA, Sema KAYAPINAR, and Özal YILDIRIM. "A prediction model for automobile sales in Turkey using deep neural networks." Endüstri Mühendisliği 31.1 (2020): 57-74.

[5] Anggraeni, W., Vinarti, R. A., & Kurniawati, Y. D. (2015). Performance comparisons between Arima and Arimax Method in Moslem Kids Clothes Demand Forecasting: Case Study. Procedia Computer Science, 72, 630–637. Dio: https://doi.org/10.1016/j.procs.2015.12.172.

[6] Seyedan, Mahya, and Fereshteh Mafakheri. "Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities." Journal of Big Data 7.1 (2020): 1-22.

[7] Kilimci, Zeynep Hilal, et al. "An improved demand forecasting model using deep learning approach and proposed decision integration strategy for supply chain." Complexity 2019 (2019).

[8] Guo ZX, Wong WK, Li M. A multivariate intelligent decision-making model for retail sales forecasting. Decis Support Syst. 2013;55(1):247–55. https://doi.org/10.1016/J.DSS.2013.01.026

[9] Wei J-T, Lee M-C, Chen H-K, Wu H-H. Customer relationship management in the hairdressing industry: an application of data mining techniques. Expert Syst Appl. 2013;40(18):7513–8. https://doi.org/10.1016/J.ESWA.2013.07.053.

[10] Masoumi, S. Maryam, Nima Kazemi, and Salwa Hanim Abdul-Rashid. "Sustainable supply chain management in the automotive industry: A process-oriented review." Sustainability 11.14 (2019): 3945

[11] Irina V. Pustokhina,Denis A. Pustokhin. "A Comparative Analysis of Traditional Forecasting Methods and Machine Learning Techniques for Sales Prediction in E-commerce." Journal of American Journal of Business and Operations Research, 10 no. 2 (2023): 39-51.

[12] Pirttilä, Miia, et al. "Working capital management in the Russian automotive industry supply chain." International Journal of Production Economics 221 (2020): 107474.

[13] Feizabadi, Javad. "Machine learning demand forecasting and supply chain performance." International Journal of Logistics Research and Applications 25.2 (2022): 119-142.

[14] Kourentzes, Nikolaos, Devon Barrow, and Fotios Petropoulos. "Another look at forecast selection and combination: Evidence from forecast pooling." International Journal of Production Economics 209 (2019): 226-235.

[15] Huber, Jakob, and Heiner Stuckenschmidt. "Daily retail demand forecasting using machine learning with emphasis on calendric special days." International Journal of Forecasting 36.4 (2020): 1420-1438.

[16] Abolghasemi, Mahdi, et al. "Demand forecasting in supply chain: The impact of demand volatility in the presence of promotion." Computers & Industrial Engineering 142 (2020): 106380.

[17] Makridakis, Spyros, Evangelos Spiliotis, and Vassilios Assimakopoulos. "The M4 Competition: 100,000 time series and 61 forecasting methods." International Journal of Forecasting 36.1 (2020): 54-74.

[18] Xiong, Zhiying, et al. "Ciprofloxacin-resistant Salmonella enterica serovar Kentucky ST198 in broiler chicken supply chain and patients, China, 2010–2016." Microorganisms 8.1 (2020): 140.

[19] Gahirwal, Manisha. "Inter time series sales forecasting." arXiv preprint arXiv:1303.0117 (2013).

[20] Matsumoto, M. & Ikeda, A. (2015). Examination of demand forecasting by time series analysis for auto parts remanufacturing. Journal of Remanufacturing, 5(1), 1. Doi: https://doi.org/10.1186/s13243-015-0010-y.

[21] Chawla, A., Singh, A., Lamba, A., Gangwani, N. & Soni, U. (2019). Demand Forecasting Using Artificial Neural Networks - A Case Study of American Retail Corporation. In H. Malik, S. Srivastava, Y. R. Sood, & A. Ahmad (Eds.), Applications of Artificial Intelligence Techniques in Engineering (pp. 79– 89). Singapore: Springer.

[22] Ferreira, R. P., Martiniano, A., Ferreira, A., Ferreira, A. & Sassi, R. J. (2016). Study on daily demand forecasting orders using Artificial Neural Network. IEEE Latin America Transactions, 14(3), 1519–1525. Doi: https://doi.org/10.1109/ TLA.2016.7459644.

[23] Slimani, I., Farissi, I. E. & Achchab, S. (2015). Artificial neural networks for demand forecasting: Application using Moroccan supermarket data. 15th International Conference on Intelligent Systems Design and Applications (ISDA), 266–271. Doi: https://doi.org/10.1109/ISDA.2015.7489236.

[24] Jebaraj, S. & Iniyan, S. (2015). Oil demand forecasting for India using artificial neural network. International Journal of Global Energy Issues, 38(4–6), 322–341. Doi: https://doi.org/10.1504/ IJGEI.2015.070280.

[25] Jaipuria, S. & Mahapatra, S. S. (2014). An improved demand forecasting method to reduce bullwhip effect in supply chains. Expert Systems with Applications, 41(5), 2395–2408. Doi: https://doi.org/10.1016/j.eswa.2013.09.038.

[26] Merkuryeva, Galina, Aija Valberga, and Alexander Smirnov. "Demand forecasting in pharmaceutical supply chains: A case study." Procedia Computer Science 149 (2019): 3-10.

[27] Fantazzini, D. & Toktamysova, Z. (2015). Forecasting German car sales using Google data and multivariate models. International Journal of Production Economics, 170, 97–135. Doi: https://doi.org/10.1016/j.ijpe.2015.09.010.

[28] Pai, P. & Liu, C. (2018). Predicting Vehicle sales by sentiment analysis of twitter data and stock market values. IEEE Access, 6, 57655–57662. Doi: https://doi.org/10.1109/ACCESS.2018.2873730.

[29] Hamzah A. Alsayadi, El-Sayed M. El-Kenawy, Abdelhameed Ibrahim,Marwa M. Eid, Abdelaziz A. Abdelhamid. (2022). Blog Feedback Prediction based on Ensemble Machine Learning Regression Model: Towards Data Fusion Analysis. Journal of Fusion: Practice and Applications, 9 ( 1 ), 38-46..

[30] Ullah, Ehsan Inam, et al. "Hydrological and ecological impacts of run off river scheme; a case study of Ghazi Barotha hydropower project on Indus River, Pakistan." Heliyon (2023): e12659.

[31] Cervantes, Jair, et al. "A comprehensive survey on support vector machine classification: Applications, challenges and trends." Neurocomputing 408 (2020): 189-215.

[32] Pandey, Amit, and Achin Jain. "Comparative analysis of KNN algorithm using various normalization techniques." International Journal of Computer Network and Information Security 11.11 (2017): 36.

[33] Shah, Kanish, et al. "A comparative analysis of logistic regression, random forest and KNN models for the text classification." Augmented Human Research 5 (2020): 1-16.

[34] Ma, Wenyuan, et al. "Identifying forest fire driving factors and related impacts in china using random forest algorithm." Forests 11.5 (2020): 507.

[35] Schonlau, Matthias, and Rosie Yuyan Zou. "The random forest algorithm for statistical learning." The Stata Journal 20.1 (2020): 3-29.

[36] Parashar, Jyoti, and Munishwar Rai. "Breast cancer images classification by clustering of ROI and mapping of features by CNN with XGBOOST learning." Materials Today: Proceedings (2020).

[37] Liew, Xin Yu, Nazia Hameed, and Jeremie Clos. "An investigation of XGBoost-based algorithm for breast cancer classification." Machine Learning with Applications 6 (2021): 100154.

[38] Alajmi, Mahdi S., and Abdullah M. Almeshal. "Predicting the tool wear of a drilling process using novel machine learning XGBoost-SDA." Materials 13.21 (2020): 4952.

[39] Afonso, Ricardo, et al. "Assessment of interventions in fuel management zones using remote sensing." ISPRS International Journal of Geo-Information 9.9 (2020): 533.Noh, Jiseong, et al. "Gated recurrent unit with genetic algorithm for product demand forecasting in supply chain management." Mathematics 8.4 (2020): 565.

[40] Cherif, Iyad Lahsen, and Abdesselem Kortebi. "On using extreme gradient boosting (XGBoost) machine learning algorithm for home network traffic classification." 2019 Wireless Days (WD). IEEE, 2019.

[41] Huiting Zheng, Jiabin Yuan, and Long Chen. Short-term load forecasting using emd-lstm neural networks with a xgboost algorithm for feature importance evaluation. Energies, 10(8):1168, 2017

Cite this Article as :
Style #
MLA Esraa Kamal, Amal F. Abdel-Gawad, Basem Ibraheem, Shereen Zaki. "Machine Learning Fusion and Data Analytics Models for Demand Forecasting in the Automotive Industry: A Comparative Study." Fusion: Practice and Applications, Vol. 12, No. 1, 2023 ,PP. 24-37 (Doi   :  https://doi.org/10.54216/FPA.120102)
APA Esraa Kamal, Amal F. Abdel-Gawad, Basem Ibraheem, Shereen Zaki. (2023). Machine Learning Fusion and Data Analytics Models for Demand Forecasting in the Automotive Industry: A Comparative Study. Journal of Fusion: Practice and Applications, 12 ( 1 ), 24-37 (Doi   :  https://doi.org/10.54216/FPA.120102)
Chicago Esraa Kamal, Amal F. Abdel-Gawad, Basem Ibraheem, Shereen Zaki. "Machine Learning Fusion and Data Analytics Models for Demand Forecasting in the Automotive Industry: A Comparative Study." Journal of Fusion: Practice and Applications, 12 no. 1 (2023): 24-37 (Doi   :  https://doi.org/10.54216/FPA.120102)
Harvard Esraa Kamal, Amal F. Abdel-Gawad, Basem Ibraheem, Shereen Zaki. (2023). Machine Learning Fusion and Data Analytics Models for Demand Forecasting in the Automotive Industry: A Comparative Study. Journal of Fusion: Practice and Applications, 12 ( 1 ), 24-37 (Doi   :  https://doi.org/10.54216/FPA.120102)
Vancouver Esraa Kamal, Amal F. Abdel-Gawad, Basem Ibraheem, Shereen Zaki. Machine Learning Fusion and Data Analytics Models for Demand Forecasting in the Automotive Industry: A Comparative Study. Journal of Fusion: Practice and Applications, (2023); 12 ( 1 ): 24-37 (Doi   :  https://doi.org/10.54216/FPA.120102)
IEEE Esraa Kamal, Amal F. Abdel-Gawad, Basem Ibraheem, Shereen Zaki, Machine Learning Fusion and Data Analytics Models for Demand Forecasting in the Automotive Industry: A Comparative Study, Journal of Fusion: Practice and Applications, Vol. 12 , No. 1 , (2023) : 24-37 (Doi   :  https://doi.org/10.54216/FPA.120102)
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