Machine Learning Algorithms in Supply Chain Forecasting: Accuracy, Efficiency, and Scalability Perspectives
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V4I1P104Keywords:
Supply Chain Forecasting, Machine Learning, LSTM, Random Forest, Scalability, Accuracy, EfficiencyAbstract
For today’s global trade market, supply chain forecasting assists companies with productivity, lessening expenses, and satisfying buyers. Traditional methods of forecasting may not be sufficient when facing the challenging data in today’s supply chains. ML is now an important tool because it discovers patterns in data and then makes predictions to address these issues. The study examines the use of a number of ML algorithms in supply chain forecasting by considering three main points. Accuracy, efficiency, and scalability. It measures the abilities of algorithms such as Linear Regression, SVM, Decision Trees, Random Forests, GBM, and LSTM networks in Deep Learning. The approach evaluates the effectiveness of forecasts by using records from sales and logistics in different industries. It turns out that while easy-to-understand models are quicker to train and easy to interpret, more advanced LSTM models perform better on data that changes often. We also point out that choosing an algorithm should depend on the situation in a supply chain and the necessary amount of computation. It explains a framework used for the project, which includes collecting, cleaning, selecting features, training models, validating them, and using them in practice. All in all, the study helps professionals and specialists in data science plan and apply machine learning forecasting to their supply chain
References
[1] Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
[2] Holt, C. C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International journal of forecasting, 20(1), 5-10.
[3] Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages. Management Science, 6(3), 324-342.
[4] Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. nature, 323(6088), 533-536.
[5] Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20, 273-297.
[6] Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations management. Production and operations management, 27(10), 1868-1883.
[7] Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and Machine Learning forecasting methods: Concerns and ways forward. PloS one, 13(3), e0194889.
[8] Ahmed, N. K., Atiya, A. F., Gayar, N. E., & El-Shishiny, H. (2010). An empirical comparison of machine learning models for time series forecasting. Econometric reviews, 29(5-6), 594-621.
[9] Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.
[10] Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175.
[11] Salinas, D., Flunkert, V., Gasthaus, J., & Januschowski, T. (2020). DeepAR: Probabilistic forecasting with autoregressive recurrent networks. International journal of forecasting, 36(3), 1181-1191.
[12] Sezer, O. B., Gudelek, M. U., & Ozbayoglu, A. M. (2020). Financial time series forecasting with deep learning: A systematic literature review: 2005–2019. Applied soft computing, 90, 106181.
[13] Lin, H., Lin, J., & Wang, F. (2022). An innovative machine learning model for supply chain management. Journal of Innovation & Knowledge, 7(4), 100276.
[14] Feizabadi, J. (2022). Machine learning demand forecasting and supply chain performance. International Journal of Logistics Research and Applications, 25(2), 119-142.
[15] Carbonneau, R., Vahidov, R., & Laframboise, K. (2009). Forecasting supply chain demand using machine learning algorithms. In Distributed Artificial Intelligence, Agent Technology, and Collaborative Applications (pp. 328-365). IGI Global Scientific Publishing.
[16] Wang, M., Fu, W., He, X., Hao, S., & Wu, X. (2020). A survey on large-scale machine learning. IEEE Transactions on Knowledge and Data Engineering, 34(6), 2574-2594.
[17] Al-Jarrah, O. Y., Yoo, P. D., Muhaidat, S., Karagiannidis, G. K., & Taha, K. (2015). Efficient machine learning for big data: A review. Big Data Research, 2(3), 87-93.
[18] Bhatnagar, R. (2018). Machine learning and big data processing: a technological perspective and review. In The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018) (pp. 468-478). Springer International Publishing.
[19] Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015). Machine learning applications in cancer prognosis and prediction. Computational and structural biotechnology journal, 13, 8-17.
[20] Matsunaga, A., & Fortes, J. A. (2010, May). On the use of machine learning to predict the time and resources consumed by applications. In 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (pp. 495-504). IEEE.
[21] Mair, C., Kadoda, G., Lefley, M., Phalp, K., Schofield, C., Shepperd, M., & Webster, S. (2000). An investigation of machine learning-based prediction systems. Journal of systems and software, 53(1), 23-29.
[22] Lakshmi Narasimha Raju Mudunuri, “AI Powered Supplier Selection: Finding the Perfect Fit in Supply Chain Management”, IJIASE, January-December 2021, Vol 7; 211-231.
