Abstract
Machine learning (ML) has emerged as a powerful tool in supply chain management (SCM), enabling organizations to accomplish valuable insights from numerous data and attain informed decisions. This paper presents an inclusive review of the recent advancements and applications of ML in SCM. The objective is to provide a holistic understanding of how ML techniques are being utilized to enhance various aspects of supply chain operations. The review begins by outlining the fundamental concepts of ML and its relevance to SCM. It then discusses the key challenges faced by supply chain professionals and how ML can address these challenges. The paper presents an overview of different ML techniques, including regression analysis, clustering, classification, time series analysis, neural networks, genetic algorithms, reinforcement learning, and ensemble methods, highlighting their specific applications in SCM. Furthermore, the review discusses the recent research trends and developments in the field, focusing on demand forecasting, inventory optimization, supplier selection and risk assessment, logistics optimization, supply chain risk management, and sustainability initiatives. The paper also explores the integration of ML with emergent technologies such as blockchain, IoT, and edge computing in the context of SCM. The findings of the review indicate that ML has demonstrated significant potential in improving decision-making, optimizing operations, enhancing supply chain resilience, and addressing sustainability challenges.
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References
Ni D, Xiao Z, Lim MK (2020) A systematic review of the research trends of machine learning in supply chain management. Int J Mach Learn Cybern 11:1463–1482
Bousqaoui H, Achchab S, Tikito K (2017, October) Machine learning applications in supply chains: an emphasis on neural network applications. In: 2017 3rd international conference of cloud computing technologies and applications (CloudTech). IEEE, pp 1–7
Seif G (2018) The 5 clustering algorithms data scientists need to know. Towards Data Science
Du CJ, Sun DW (2006) Learning techniques used in computer vision for food quality evaluation: a review. J Food Eng 72(1):39–55
Wenzel H, Smit D, Sardesai S (2019) A literature review on machine learning in supply chain management. In: Artificial intelligence and digital transformation in supply chain management: innovative approaches for supply chains. Proceedings of the Hamburg international conference of logistics (HICL), vol 27. Epubli GmbH, Berlin, pp 413–441
Akbari M, Do TNA (2021) A systematic review of machine learning in logistics and supply chain management: current trends and future directions. Benchmarking: Int J 28(10):2977–3005
Hu H, Xu J, Liu M, Lim MK (2023) Vaccine supply chain management: an intelligent system utilizing blockchain, IoT and machine learning. J Bus Res 156:113480
Lin H, Lin J, Wang F (2022) An innovative machine learning model for supply chain management. J Innov Knowl 7(4):100276
Tirkolaee EB, Sadeghi S, Mooseloo FM, Vandchali HR, Aeini S (2021) Application of machine learning in supply chain management: a comprehensive overview of the main areas. Math Probl Eng 2021:1–14
Ghazal TM, Alzoubi HM (2022) Fusion-based supply chain collaboration using machine learning techniques. Intell Autom Soft Comput 31(3):1671–1687
Kohli S, Godwin GT, Urolagin S (2021) Sales prediction using linear and KNN regression. In: Advances in machine learning and computational intelligence: proceedings of ICMLCI 2019. Springer Singapore, pp 321–329
Shilong Z (2021, January) Machine learning model for sales forecasting by using XGBoost. In: 2021 IEEE international conference on consumer electronics and computer engineering (ICCECE). IEEE, pp 480–483
Park KJ (2021) Determining the tiers of a supply chain using machine learning algorithms. Symmetry 13(10):1934
Islam S, Amin SH (2020) Prediction of probable backorder scenarios in the supply chain using distributed random forest and gradient boosting machine learning techniques. J Big Data 7:1–22
Vairagade N, Logofatu D, Leon F, Muharemi F (2019) Demand forecasting using random forest and artificial neural network for supply chain management. In: Computational collective intelligence: 11th international conference, ICCCI 2019, Hendaye, France, September 4–6, 2019, Proceedings, Part I, vol 11. Springer International Publishing, pp 328–339
Ali MR, Nipu SMA, Khan SA (2023) A decision support system for classifying supplier selection criteria using machine learning and random forest approach. Decis. Anal. J 100238
Raza SA, Govindaluri SM, Bhutta MK (2023) Research themes in machine learning applications in supply chain management using bibliometric analysis tools. Benchmarking: Int J 30(3):834–867
Ding S, Cui T, Wu X, Du M (2022) Supply chain management based on volatility clustering: the effect of CBDC volatility. Res Int Bus Financ 62:101690
De Lucia C, Pazienza P, Bartlett M (2020) Does good ESG lead to better financial performances by firms? Machine learning and logistic regression models of public enterprises in Europe. Sustainability 12(13):5317
Li L (2022) Predicting the investment risk in supply chain management using BPNN and machine learning. Wirel Commun Mob Comput 2022
Sinha GK (2022) Relationship between sustainable logistics practices and the organization’s performance in automobile industry—an empirical study with logistic regression machine learning. Int J Mech Eng 7(1)
Nguyen HD, Tran KP, Thomassey S, Hamad M (2021) Forecasting and anomaly detection approaches using LSTM and LSTM Autoencoder techniques with the applications in supply chain management. Int J Inf Manag 57:102282
Weng T, Liu W, Xiao J (2020) Supply chain sales forecasting based on lightGBM and LSTM combination model. Ind Manag Data Syst 120(2):265–279
Bousqaoui H, Achchab S, Tikito K (2019) Machine learning applications in supply chains: Long short-term memory for demand forecasting. In: Cloud computing and big data: technologies, applications and security, vol 3. Springer International Publishing, pp 301–317
Yani LPE, Priyatna IMA, Aamer AM (2019) Exploring machine learning applications in supply chain management. In: 9th international conference on operations and supply chain management, pp 161–169
Carbonneau R, Vahidov R, Laframboise K (2007) Machine learning-Based demand forecasting in supply chains. Int J Intell Inf Technol (IJIIT) 3(4):40–57
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Thejasree, P., Manikandan, N., Vimal, K.E.K., Sivakumar, K., Krishnamachary, P.C. (2024). Applications of Machine Learning in Supply Chain Management—A Review. In: Vimal, K.E.K., Rajak, S., Kumar, V., Mor, R.S., Assayed, A. (eds) Industry 4.0 Technologies: Sustainable Manufacturing Supply Chains. Environmental Footprints and Eco-design of Products and Processes. Springer, Singapore. https://doi.org/10.1007/978-981-99-4819-2_6
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