Neural networks for forecasting irregular demand in an Automotive Diagnostic Centre

Subject Areas : Design of Experiment

Sonia Isabel Polo Triana ¹ , Juan Camilo Gutierrez ²

1 - Industrial Engineering Program, Faculty of Engineering, Universidad de Investigación y Desarrollo, Bucaramanga, Colombia
2 - Industrial Engineering Program, Faculty of Engineering, Universidad de Investigación y Desarrollo, Bucaramanga, Colombia

Received: 2023-12-01 Accepted : 2024-08-15 Published : 2024-08-19

Keywords: demand forecasting, neural networks, Automotive Diagnostic Centre, demand variability, model optimization.,

Abstract :

This study investigates the prediction of demand in an Automotive Diagnostic Centre (ADC) in Bucaramanga, Colombia, applying advanced neural network techniques to three services: technical inspections for motorcycles, private cars, and public service vehicles. Utilising daily data from December 2019 to December 2021 (628 observations), the squared coefficient of variation of demand (CV²) and the average demand interval (ADI) are employed to classify demand, following the methodology of Syntetos & Boylan (2005b). Five Deep Learning models were evaluated: RNN, Bidirectional LSTM, CNN, GRU, and MLP, adopting a recursive approach for prediction. The findings reveal that despite the promises of forecasting algorithms and their success in other sectors, their performance in the context of ADCs is limited. Evaluation metrics, including RMSE, MSE, MAE, r2_score, and MAPE, reveal that although the LSTM model exhibits the best overall performance, no model achieves precise and reliable prediction due to the complexity and irregularity of demand. This finding underscores the need for continued research in demand forecasting and suggests exploring hybrid approaches or more specialised models. The variability in performance across models and services reflects the importance of tailoring predictive approaches to the specific characteristics of each demand segment. The principal contribution of this study is the innovative approach in applying neural networks to irregular demand in ADCs, an area hitherto little explored. It highlights the need to integrate external variables and develop adaptive management practices. Future research should focus on the integration of external factors and the development of hybrid models and mitigation strategies for efficient management of demand uncertainty.

References:

Amirkolaii, K. N., Baboli, A., Shahzad, M. K., & Tonadre, R. (2017). Demand Forecasting for Irregular Demands in Business Aircraft Spare Parts Supply Chains by using Artificial Intelligence (AI). IFAC-PapersOnLine, 50(1), 15221-15226. https://doi.org/10.1016/j.ifacol.2017.08.2371
Bremen, B.-B. L. e V. (s. f.). Forecasting Irregular Demand Using Single Hidden Layer Neural Networks - Die BVL: Das Logistik-Netzwerk für Fach- und Führungskräfte. Recuperado 19 de noviembre de 2023, de https://www.bvl.de/lore/all-volumes--issues/volume-15/issue-1/forecasting-irregular-demand-using-single-hidden-layer-neural-networks
Cea Morán, J. J. (2020). Redes neuronales recurrentes para la generación automática de música. Universidad Politécnica de Madrid.
Croston, J. D. (1972). Forecasting and Stock Control for Intermittent Demands. Journal of the Operational Research Society, 23(3), 289-303. https://doi.org/10.1057/jors.1972.50
da Silva, D. G., & Meneses, A. A. de M. (2023). Comparing Long Short-Term Memory (LSTM) and bidirectional LSTM deep neural networks for power consumption prediction. Energy Reports, 10, 3315-3334. https://doi.org/10.1016/j.egyr.2023.09.175
Eseye, A. T., Lehtonen, M., Tukia, T., Uimonen, S., & John Millar, R. (2019). Machine learning based integrated feature selection approach for improved electricity demand forecasting in decentralized energy systems. IEEE Access, 7, 91463-91475. Scopus. https://doi.org/10.1109/ACCESS.2019.2924685
Fierro, A. A. (2020). Predicción de Series Temporales con Redes Neuronales. En Facultad de Informática Universidad Nacional de La Plata Argentina. Universidad Nacional de La Plata.
Guerrero, J. (2020). Redes recurrentes. Universidad de Sevillla.
Gutierrez, R. S., Solis, A. O., & Mukhopadhyay, S. (2008). Lumpy demand forecasting using neural networks. International Journal of Production Economics, 111(2), 409-420. https://doi.org/10.1016/j.ijpe.2007.01.007
Hoffmann, M. A., Lasch, R., & Meinig, J. (2022). Forecasting Irregular Demand Using Single Hidden Layer Neural Networks (6.a ed.). Bundesvereinigung Logistik (BVL) e.V. https://doi.org/10.23773/2022_6
Jiang, P., Huang, Y., & Liu, X. (2021). Intermittent demand forecasting for spare parts in the heavy-duty vehicle industry: A support vector machine model. International Journal of Production Research, 59(24), 7423-7440. https://doi.org/10.1080/00207543.2020.1842936
Kourentzes, N. (2013). Intermittent demand forecasts with neural networks. International Journal of Production Economics, 143(1), 198-206. https://doi.org/10.1016/j.ijpe.2013.01.009
Lolli, F., Gamberini, R., Regattieri, A., Balugani, E., Gatos, T., & Gucci, S. (2017). Single-hidden layer neural networks for forecasting intermittent demand. International Journal of Production Economics, 183, 116-128. https://doi.org/10.1016/j.ijpe.2016.10.021
Muhaimin, A., Prastyo, D. D., & Horng-Shing Lu, H. (2021). Forecasting with Recurrent Neural Network in Intermittent Demand Data. 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 802-809. https://doi.org/10.1109/Confluence51648.2021.9376880
Mukhopadhyay, S., Solis, A. O., & Gutierrez, R. S. (2012). The Accuracy of Non-traditional versus Traditional Methods of Forecasting Lumpy Demand. Journal of Forecasting, 31(8), 721-735. https://doi.org/10.1002/for.1242
Nasiri Pour, A., Rostami-Tabar, B., & Rahimzadeh, A. (2008). A hybrid neural network and traditional approach for forecasting lumpy demand. Proceedings of the World Academy of Science, Engineering and Technology, 2(4), Article 4. http://waset.org/publications/10793/a-hybrid-neural-network-and-traditional-approach-for-forecasting-lumpy-demand
Nikolopoulos, K. (2021). We need to talk about intermittent demand forecasting. European Journal of Operational Research, 291(2), 549-559. https://doi.org/10.1016/j.ejor.2019.12.046
Nikolopoulos, K., Syntetos, A. A., Boylan, J. E., Petropoulos, F., & Assimakopoulos, V. (2011). An aggregate–disaggregate intermittent demand approach (ADIDA) to forecasting: An empirical proposition and analysis. Journal of the Operational Research Society, 62(3), 544-554. https://doi.org/10.1057/jors.2010.32
Sarlo, R., Fernandes, C., & Borenstein, D. (2023). Lumpy and intermittent retail demand forecasts with score-driven models. European Journal of Operational Research, 307(3), 1146-1160. https://doi.org/10.1016/j.ejor.2022.10.006
Shenstone, L., & Hyndman, R. J. (2005). Stochastic models underlying Croston’s method for intermittent demand forecasting. Journal of Forecasting, 24(6), 389-402. https://doi.org/10.1002/for.963
Steuer, D., Korevaar, P., Hutterer, V., & Fromm, H. (2018). A Similarity-Based Approach for the All-Time Demand Prediction of New Automotive Spare Parts. Proceedings of the 51st Hawaii International Conference on System Sciences. http://hdl.handle.net/10125/50078
Svetunkov, I., & Boylan, J. E. (2023). iETS: State space model for intermittent demand forecasting. International Journal of Production Economics, 265, 109013. https://doi.org/10.1016/j.ijpe.2023.109013
Syntetos, A. A., & Boylan, J. E. (2001). On the bias of intermittent demand estimates. International Journal of Production Economics, 71(1), 457-466. https://doi.org/10.1016/S0925-5273(00)00143-2
Syntetos, A. A., & Boylan, J. E. (2005a). The accuracy of intermittent demand estimates. International Journal of Forecasting, 21(2), 303-314. https://doi.org/10.1016/j.ijforecast.2004.10.001
Syntetos, A. A., & Boylan, J. E. (2005b). The accuracy of intermittent demand estimates. International Journal of Forecasting, 21(2), 303-314. https://doi.org/10.1016/j.ijforecast.2004.10.001
Tian, X., Wang, H., & E, E. (2021). Forecasting intermittent demand for inventory management by retailers: A new approach. Journal of Retailing and Consumer Services, 62, 102662. https://doi.org/10.1016/j.jretconser.2021.102662
Willemain, T. R., Smart, C. N., & Schwarz, H. F. (2004). A new approach to forecasting intermittent demand for service parts inventories. International Journal of Forecasting, 20(3), 375-387. https://doi.org/10.1016/S0169-2070(03)00013-X
Zenchenko, V., & Grigoriev, M. (2020). Analysis of Trends and Processes of Auto Service Promotion. En Z. Popovic, A. Manakov, & V. Breskich (Eds.), VIII International Scientific Siberian Transport Forum (pp. 578-590). Springer International Publishing. https://doi.org/10.1007/978-3-030-37916-2_56
Zhang, G. P., Xia, Y., & Xie, M. (2023). Intermittent demand forecasting with transformer neural networks. Annals of Operations Research. https://doi.org/10.1007/s10479-023-05447-7
Zohdi, M., Rafiee, M., Kayvanfar, V., & Salamiraad, A. (2022). Demand forecasting based machine learning algorithms on customer information: An applied approach. International Journal of Information Technology, 14(4), 1937-1947. https://doi.org/10.1007/s41870-022-00875-3