Skip to main content
SpringerLink
Log in

Machine Learning Applications in the Supply Chain, a Literature Review

  • Conference paper
  • First Online:
Smart Applications with Advanced Machine Learning and Human-Centred Problem Design (ICAIAME 2021)

Part of the book series: Engineering Cyber-Physical Systems and Critical Infrastructures ((ECPSCI,volume 1))

Abstract

Machine Learning has been a top of mind topic during the last decade showing great benefits through experimental studies and real implementations in many areas. Supply chain, since it conception, has been one of the most improved and optimized processes in many industries. How is Machine learning doing in the supply chain? This review objective is to identify studies or researches focus on machine learning applied to any of the supply chain processes and know which industries have applied it, how positive or negative their results have been, what type of methods have been used and which method seems the better option. As a result is a quick reference of types of Machine learning versus machine learning techniques or algorithms versus supply chain processes versus industries.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abbasi B, Babaei T, Hosseinifard Z, Smith-Miles K, Dehghani M (2020) Predicting solutions of large-scale optimization problems via machine learning: a case study in blood supply chain management. Comput Operat Res 119:104941. https://doi.org/10.1016/j.cor.2020.104941

    Article  MathSciNet  MATH  Google Scholar 

  2. Aboutorab H, Hussain OK, Saberi M, Hussain FK (2022) A reinforcement learning-based framework for disruption risk identification in supply chains. Future Gener Comput Syst 126:110–122. https://doi.org/10.1016/j.future.2021.08.004

  3. Bertolini M, Mezzogori D, Neroni M, Zammori F (2021) Machine learning for industrial applications: a comprehensive literature review. Expert Syst Appl 175:114820. https://doi.org/10.1016/j.eswa.2021.114820

    Article  Google Scholar 

  4. Carbonneau R, Laframboise K, Vahidov R (2008) Application of machine learning techniques for supply chain demand forecasting. Eur J Operat Res 184(3):1140–1154. https://doi.org/10.1016/j.ejor.2006.12.004

  5. Han C, Zhang Q (2021) Optimization of supply chain efficiency management based on machine learning and neural network. Neural Comput Appl 33:1419–1433

    Article  Google Scholar 

  6. Hartley JL, Sawaya WJ (2019) Tortoise, not the hare: digital transformation of supply chain business processes. Bus Horiz 62(6):707–715. (Digital Transformation Disruption). https://doi.org/10.1016/j.bushor.2019.07.006

  7. Hathikal S, Chung SH, Karczewski M (2020) Prediction of ocean import shipment lead time using machine learning methods. SN Appl Sci 2(7):1–20

    Article  Google Scholar 

  8. Islam S, Amin SH (2020) Prediction of probable backorder scenarios in the supply chain using distributed random forest and gradient boosting machine learning techniques. Jo Big Data 7(1):1–22

    Google Scholar 

  9. Kauten C, Gupta A, Qin X, Richey G (2021) Predicting blood donors using machine learning techniques. Inf Syst Front 1–16

    Google Scholar 

  10. Kim CO, Kwon I-H, Baek J-G (2008) Asynchronous action-reward learning for nonstationary serial supply chain inventory control. Appl Intell 28(1):1–16

    Article  Google Scholar 

  11. Konovalenko I, Ludwig A (2021) Comparison of machine learning classifiers: a case study of temperature alarms in a pharmaceutical supply chain. Inf Syst 100:101759

    Article  Google Scholar 

  12. Lauer T, Legner S, Henke M (2019) Application of machine learning on plan instability in master production planning of a semiconductor supply chain. IFAC-PapersOnLine 52(13):1248–1253. (9th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2019). https://doi.org/10.1016/j.ifacol.2019.11.369

  13. Liu X-H, Shan M-Y, Zhang L-H (2016) Low-carbon supply chain resources allocation based on quantum chaos neural network algorithm and learning effect. Nat Hazards 83(1):389–409

    Article  Google Scholar 

  14. Loisel J, Duret S, CornuÃljols A, Cagnon D, Tardet M, Derens-Bertheau E, Laguerre O (2021) Cold chain break detection and analysis: can machine learning help? Trends in food science technology 112:391–399. https://doi.org/10.1016/j.tifs.2021.03.052

    Article  Google Scholar 

  15. Malviya L, Chittora P, Chakrabarti P, Vyas RS, Poddar S (2021) Backorder prediction in the supply chain using machine learning. Proc, Mater Today. https://doi.org/10.1016/j.matpr.2020.11.558

  16. Meiners M, Mayr A, Thomsen M, Franke J (2020) Application of machine learning for product batch oriented control of production processes. Proc CIRP 93:431–436. (53rd CIRP Conference on Manufacturing Systems 2020). https://doi.org/10.1016/j.procir.2020.04.006

  17. Moroff NU, Kurt E, Kamphues J (2021) Machine learning and statistics: a study for assessing innovative demand forecasting models. Proc Comput Sci 180:40–49. (Proceedings of the 2nd International Conference on Industry 4.0 and Smart Manufacturing (ISM 2020)). https://doi.org/10.1016/j.procs.2021.01.127

  18. Nasurudeen Ahamed N, Karthikeyan P (2020) A reinforcement learning integrated in heuristic search method for self-driving vehicle using blockchain in supply chain management. Int J Intell Netw 1:92–101. https://doi.org/10.1016/j.ijin.2020.09.001

    Article  Google Scholar 

  19. Punia S, Singh SP, Madaan JK (2020) A cross-temporal hierarchical framework and deep learning for supply chain forecasting. Comput Ind Eng 149:106796. https://doi.org/10.1016/j.cie.2020.106796

    Article  Google Scholar 

  20. Rodríguez GG, Gonzalez-Cava JM, Pérez JAM (2020) An intelligent decision support system for production planning based on machine learning. J Intell Manuf 31(5):1257–1273

    Article  Google Scholar 

  21. Sharma R, Kamble SS, Gunasekaran A, Kumar V, Kumar A (2020) A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Comput Operat Res 119:104926

    Article  MathSciNet  MATH  Google Scholar 

  22. Tandon N, Tandon R (2019) Using machine learning to explain the heterogeneity of schizophrenia. Realizing the promise and avoiding the hype. Schizophrenia Res 214:70–75. (Machine Learning in Schizophrenia). https://doi.org/10.1016/j.schres.2019.08.032

  23. Vanvuchelen N, Gijsbrechts J, Boute R (2020) Use of proximal policy optimization for the joint replenishment problem. Comput Ind 119:103239. https://doi.org/10.1016/j.compind.2020.103239

    Article  Google Scholar 

  24. Wang D, Zhang Y (2020) Implications for sustainability in supply chain management and the circular economy using machine learning model. Inf Syst e-Business Manage 1–13

    Google Scholar 

  25. Yalan Y, Wei T (2021) Deep logistic learning framework for e-commerce and supply chain management platform. Arab J Sci Eng 1–15

    Google Scholar 

  26. Yang Y (2020) Research on the optimization of the supplier intelligent management system for cross-border e-commerce platforms based on machine learning. Inf Syst e-Business Manage 18(4):851–870

    Article  Google Scholar 

  27. Yang Y, Wu L (2021) Machine learning approaches to the unit commitment problem: current trends, emerging challenges, and new strategies. Electr J 34(1):106889

    Article  Google Scholar 

  28. Zarandi MHF, Moosavi SV, Zarinbal M (2013) A fuzzy reinforcement learning algorithm for inventory control in supply chains. Int J Adv Manuf Technol 65(1–4):557–569

    Article  Google Scholar 

  29. Zhu Y, Xie C, Wang G-J, Yan X-G (2017) Comparison of individual, ensemble and integrated ensemble machine learning methods to predict China’s SME credit risk in supply chain finance. Neural Comput Appl 28(1):41–50

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Facultad de Ingenieria, Universidad Panamericana, Augusto Rodin 498, Ciudad de Mexico, 03920, Mexico

    Walter Rosenberg-Vitorica

  2. Facultad de Ingenieria Mecanica y Electrica, Universidad Autonoma de Nuevo Leon. Pedro de Alba S/N, Ciudad Universitaria, San Nicolas de los Garza, Nuevo Leon, 66451, Mexico

    Tomas Eloy Salais-Fierro, Jose Antonio Marmolejo-Saucedo & Roman Rodriguez-Aguilar

Authors
  1. Walter Rosenberg-Vitorica
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tomas Eloy Salais-Fierro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jose Antonio Marmolejo-Saucedo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Roman Rodriguez-Aguilar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Walter Rosenberg-Vitorica .

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rosenberg-Vitorica, W., Salais-Fierro, T.E., Marmolejo-Saucedo, J.A., Rodriguez-Aguilar, R. (2023). Machine Learning Applications in the Supply Chain, a Literature Review. In: Smart Applications with Advanced Machine Learning and Human-Centred Problem Design. ICAIAME 2021. Engineering Cyber-Physical Systems and Critical Infrastructures, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-031-09753-9_58

Download citation

Publish with us

Policies and ethics

Search

Navigation