Article section

Article title
Contributor
Abstract
About article
DOI
Citation style
Copyright
Open access
References
Review Article

Application of Artificial Intelligence in Supply Chain Management: A Review on Strengths and Weaknesses of Predictive Modeling Techniques

Authors

  • Aminu Adamu Ahmed Department of Information Communication Technology, Federal Polytechnic Kaltungo, Gombe State, Nigeria

    aaahmed.pg@atbu.edu.ng

    https://orcid.org/0000-0002-9602-0852
  • Ali Usman Abdullahi Department of Computer Science Education, Federal College of Education, Gombe State, Nigeria
  • Abdulsalam Ya’u Gital Department of Computer and Mathematical Sciences, Abubakar Tafawa Balewa University Bauchi, Nigeria
  • Abubakar Yusuf Dutse Department of Management and Information Technology, Abubakar Tafawa Balewa University Bauchi, Nigeria

Abstract

This study examines and summarizes the body for studies on supply chain management (SCM) applications of artificial intelligence (AI) predictive modeling techniques. It does this by examining 55 pertinent publications that were published between July 2024 and 2015. Sources for the literature included reputable research resources such as Google Scholar, IEEE Xplore, Web of Science, Science Direct, and Scopus. With an emphasis on machine learning algorithms, deep learning frameworks, and conventional statistical techniques, the paper demonstrates the wide variety of predictive modelling techniques used in the field. The results show that the use of machine learning techniques is becoming more common, and these techniques have been shown to offer a great deal of promise for enhancing SCM decision-making and forecasting accuracy. Deep learning frameworks are becoming increasingly potent instruments as well, especially for handling big datasets and identifying intricate linkages in the supply chain. This review provides a thorough resource for researchers and practitioners who wish to understand the advantages and disadvantages of various prediction modeling techniques in AI for SCM. It also highlights some noteworthy weaknesses, such as issues with data quality, model interpretability, and the requirement for domain-specific knowledge. Lastly, the synthesis of findings shows that although AI-driven prediction models can improve efficiency and responsiveness in SCM, their successful implementation requires careful consideration of organizational context and operational constraints.

Article information

Journal

Scientific Journal of Engineering, and Technology

Volume (Issue)

1 (2)

Pages

1-18

Published

11-10-2024

How to Cite

Ahmed, A. A., Abdullahi, A. U., Gital, A. Y., & Dutse, A. Y. (2024). Application of Artificial Intelligence in Supply Chain Management: A Review on Strengths and Weaknesses of Predictive Modeling Techniques. Scientific Journal of Engineering, and Technology, 1(2), 1-18. https://doi.org/10.69739/sjet.v1i2.131

References

Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A. E., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11), e00938. https://doi.org/10.1016/j.heliyon.2018.e00938

Alam, M., Haque, A., Khan, A. I., Kasim, S., Pasha, A. A., Zafar, A., Irshad, K., Chaudhary, A. A., & Azim, R. (2023). Metasurface-Based Solar Absorption Prediction System Using Artificial Intelligence. Journal of Mathematics, 2023(9489270), 1–18. https://doi.org/https://doi.org/10.1155/2023/9489270

Alavidoost, M. H., Jafarnejad, A., & Babazadeh, H. (2020). A novel fuzzy mathematical model for an integrated supply chain planning using multi-objective evolutionary algorithm. Soft Computing, 6. https://doi.org/10.1007/s00500-020-05251-6

Alomar, M. A. (2022). Performance Optimization of Industrial Supply Chain Using Artificial Intelligence. Computational Intelligence and Neuroscience, 2022(9306265), 1–10. https://doi.org/https://doi.org/10.1155/2022/9306265

Amellal, I., Amellal, A., Seghiouer, H., & Ech-charrat, M. R. (2024). An integrated approach for modern supply chain management: Utilizing advanced machine learning models for sentiment analysis, demand forecasting, and probabilistic price prediction. Decision Science Letters, 13(2024), 237–248. https://doi.org/10.5267/dsl.2023.9.003

Arora, S., & Singh, S. (2019). Butterfly optimization algorithm: a novel approach for global optimization. Soft Computing, 23(3), 715–734. https://doi.org/10.1007/s00500-018-3102-4

Banu, J. F., Neelakandan, S., Geetha, B. T., Selvalakshmi, V., Umadevi, A., & Martinson, E. O. (2022). Artificial Intelligence Based Customer Churn Prediction Model for Business Markets. Computational Intelligence and Neuroscience, 2022(1703696), 1–14. https://doi.org/https://doi.org/10.1155/2022/1703696

Belhadi, A., Kamble, S., Fosso Wamba, S., & Queiroz, M. M. (2022). Building supply-chain resilience: an artificial intelligence-based technique and decision-making framework. International Journal of Production Research, 60(14), 4487–4507. https://doi.org/10.1080/00207543.2021.1950935

Che, C., Liu, B., Li, S., Huang, J., & Hu, H. (2023). Deep Learning for Precise Robot Position Prediction in Logistics. Journal of Theory and Practice of Engineering Science, 3(10), 36–41. https://doi.org/10.53469/jtpes.2023.03(10).05

Chong, A. Y. L., Lo, C. K. Y., & Weng, X. (2017). The role of big data analytics in supply chain management: A review and future research directions. International Journal of Production Research, 55(17), 5325-5339. https://doi.org/10.1080/00207543.2017.1290194

Cica, D., Sredanovic, B., & Tesic, S. (2020). Predictive modeling of turning operations under different cooling / lubricating conditions for sustainable manufacturing with machine learning techniques. Applied Computing and Informatics, 20(1), 162–180. https://doi.org/10.1016/j.aci.2020.02.001

Cotrufo, N., Saloux, E., Hardy, J. M., Candanedo, J. A., & Platon, R. (2019). A practical Artificial Intelligence-based approach for predictive control in commercial and. Energy & Buildings, 109563. https://doi.org/10.1016/j.enbuild.2019.109563

Dabbas, H., & Friedrich, B. (2022). Benchmarking machine learning algorithms by inferring transportation modes from unlabeled GPS data. Transportation Research Procedia, 62(Ewgt 2021), 383–392. https://doi.org/10.1016/j.trpro.2022.02.048

Deb, I., & Gupta, R. K. (2023). A genetic algorithm based heuristic optimization technique for solving balanced allocation problem involving overall shipping cost minimization with restriction to the number of serving units as well as customer hubs. Results in Control and Optimization, 11(June 2022), 100227. https://doi.org/10.1016/j.rico.2023.100227

Dokeroglu, T., Sevinc, E., Kucukyilmaz, T., & Cosar, A. (2019). A survey on new generation metaheuristic algorithms. Computers and Industrial Engineering, 137(May). https://doi.org/10.1016/j.cie.2019.106040

Dosdoğru, A. T., İpek, A. B., & Göçken, M. (2020). A novel hybrid artificial intelligence-based decision support framework to predict lead time. International Journal of Logistics Research and Applications, 24(3), 5567. https://doi.org/10.1080/13675567.2020.1749249

Du, M., Luo, J., Wang, S., & Liu, S. (2019). Genetic algorithm combined with BP neural network in hospital drug inventory management system. Neural Computing and Applications, 3. https://doi.org/10.1007/s00521-019-04379-3

Dubey, R., Gunasekaran, A., Bryde, D. J., & Fynes, B. (2019). Big data analytics and organizational culture as complements to Swift Trust and collaborative performance in the Humanitarian supply chain. International Journal of Production Economics, 210, 120-130. https://doi.org/10.1016/j.ijpe.2019.01.003

Duc, D. N., & Nananukul, N. (2020). A Hybrid Methodology Based on Machine Learning for a Supply Chain Optimization Problem A Hybrid Methodology Based on Machine Learning for a Supply Chain Optimization Problem. Journal of Physics: Conference Series, 1624(052022). https://doi.org/10.1088/1742-6596/1624/5/052022

Elbasi, E., Zaki, C., Topcu, A. E., Abdelbaki, W., & Zreikat, A. I. (2023). Crop Prediction Model Using Machine Learning Algorithms. Applied Sciences, 13(9288), 1–20. https://doi.org/https://doi.org/10.3390/ app13169288

Elsken, T., Metzen, J. H., & Hutter, F. (2019). Survey on neural architecture search. Journal of Machine Learning Research, 20(2019), 1–21. https://doi.org/10.11834/jig.200202

Gunasekaran, A., Subramanian, N., & Rahman, S. (2017). The role of big data analytics in supply chain management: A review and future research directions. International Journal of Production Economics, 176, 98-110. https://doi.org/10.1016/j.ijpe.2016.03.014

Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72-80. https://doi.org/10.1016/j.ijpe.2014.04.018

Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice (2nd ed.). OTexts. https://otexts.com/fpp2/

Kamble, S. S., Gunasekaran, A., & Sharma, R. (2020). Industry 4.0 and the supply chain: A systematic literature review and future research directions. International Journal of Production Research, 58(16), 5035-5056. https://doi.org/10.1080/00207543.2020.1743534

Kamble, S. S., Gunasekaran, A., & Sharma, R. (2021). Artificial intelligence in supply chain management: A systematic literature review. International Journal of Production Research, 59(11), 3530-3547. https://doi.org/10.1080/00207543.2020.1720750

Kasthuri, E., Subbulakshmi, S., & Sreedharan, R. (2024). Insightful Clinical Assistance for Anemia Prediction with Data Analysis and Explainable AI. Procedia Computer Science, 233, 45–55. https://doi.org/10.1016/j.procs.2024.03.194

Kourentzes, N., Petropoulos, F., & Spiliotis, E. (2020). Forecasting with machine learning: A review of the empirical literature. International Journal of Forecasting, 36(1), 90-103. https://doi.org/10.1016/j.ijforecast.2019.05.003

Kudama, G., Dangia, M., Wana, H., & Tadese, B. (2021). Artificial Intelligence in Agriculture Will digital solution transform Sub-Sahara African agriculture ? Artificial Intelligence in Agriculture, 5, 292–300. https://doi.org/10.1016/j.aiia.2021.12.001

Liao, M., & Yao, Y. (2021). Applications of artificial intelligence- ­ based modeling for bioenergy systems : A review. GCB-Bioenergy, 2021(January), 774–802. https://doi.org/https://doi.org/10.1111/gcbb.12816

Lin, H., Lin, J., & Wang, F. (2022). An innovative machine learning model for supply chain management. Journal of Innovation & Knowledge, 7(2022) 100276). https://doi.org/10.1016/j.jik.2022.100276

Lokare, V. T., & Jadhav, P. M. (2024). An AI-based learning style prediction model for personalized and effective learning. Thinking Skills and Creativity, 51(November 2023), 101421. https://doi.org/10.1016/j.tsc.2023.101421

Lipton, Z. C. (2016). The mythos of model interpretability. Communications of the ACM, 59(10), 36-43. https://doi.org/10.1145/2998481

Luo, S., Xing, M., & Zhao, J. (2022). Construction of Artificial Intelligence Application Model for Supply Chain Financial Risk Assessment. Scientific Programming, 2022(4194576), 1–8. https://doi.org/https://doi.org/10.1155/2022/4194576

MD Rokibul Hasan MBA, PMP, CSM, Rejon Kumar Ray, and F. R. C. (2024). Employee Performance Prediction: An Integrated Approach of Business Analytics and Machine Learning. Journal of Business and Management Studies, 6(1), 215–219. https://doi.org/10.32996/jbms.2024.6.1.14

Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002

Mishra, D., Singh, P., & Singh, R. (2017). A review of artificial intelligence in supply chain management. International Journal of Logistics Research and Applications, 20(3), 285-308. https://doi.org/10.1080/13675567.2016.1176798

Modesti, P., & Borsato, M. (2022). Artificial intelligence-based method for forecasting flowtime in job shops. VINE Journal of Information and Knowledge Management Systems, 54(2), 452-472. https://doi.org/10.1108/VJIKMS-08-2021-0146

Mohammadi-Balani, A., Dehghan Nayeri, M., Azar, A., & Taghizadeh-Yazdi, M. (2021). Golden eagle optimizer: A nature-inspired metaheuristic algorithm. Computers and Industrial Engineering, 152, 107050. https://doi.org/10.1016/j.cie.2020.107050

Monsalve, M. O., Caron-Munoz, M., Galeano-vasco, L., & Medina-Sierra, M. (2023). Use of Machine Learning Models for Prediction of Organic Carbon and Nitrogen in Soil from Hyperspectral Imagery in Laboratory Manuela Ortega Monsalve , Mario Cer on-Muñoz. Journal of Spectroscopy, 2023(4389885), 1–8. https://doi.org/https://doi.org/10.1155/2023/4389885

Naz, F., Kumar, A., Majumdar, A., & Agrawal, R. (2022). Is artificial intelligence an enabler of supply chain resiliency post COVID-19? An exploratory state-of-the-art review for future research. Operations Management Research, 15(1–2), 378–398. https://doi.org/10.1007/s12063-021-00208-w

Oh, S. W., Park, J. H., Jo, H. S., & Na, M. G. (2024). Development of an AI-based remaining trip time prediction system for nuclear power plants. Nuclear Engineering and Technology, 56(8), 3167–3179. https://doi.org/10.1016/j.net.2024.03.017

Pournader, M., Ghaderi, H., Hassanzadegan, A., & Fahimnia, B. (2021). Artificial intelligence applications in supply chain management. International Journal of Production Economics, 241(August), 108250. https://doi.org/10.1016/j.ijpe.2021.108250

Russell, S., & Norvig, P. (2016). Artificial intelligence: A modern approach (3rd ed.). Pearson.

Seidl, P., Vall, A., & Hochreiter, S. (2019). Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language. Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, USA. PMLR 202, 2023, 1–33.

Singh, R., Kumar, S., & Singh, P. (2019). A risk management framework for the supply chain: A review and future directions. International Journal of Production Research, 57(10), 2940-2956. https://doi.org/10.1080/00207543.2018.1554169

Sodhi, M. S., & Tang, C. S. (2019). The role of analytics in supply chain management: An overview. Journal of Operations Management, 65(3), 257-275. https://doi.org/10.1002/joom.1016

Šustrová, T. (2016). A Suitable Artificial Intelligence Model for Inventory Level Optimization. Trends Economics and Management, 8527(1), 48–55. https://doi.org/http://dx.doi.org/10.13164/trends.2016.25.48

Wang, G., Wei, J., & Yao, B. (2020). A Coalmine Water Inrush Prediction Model Based on Artificial Intelligence. International Journal of Safety and Security Engineering, 10(4), 501–508. https://doi.org/https://doi.org/10.18280/ijsse.100409 Received:

Wang, S. (2021). Artificial Intelligence Applications in the New Model of Logistics. Scientific Programming, 2021(5166993), 1–5. https://doi.org/https://doi.org/10.1155/2021/5166993

Wang, Y., Gunasekaran, A., & Ngai, E. W. T. (2020). Big data in logistics and supply chain management: Certain investigations for future research and applications. International Journal of Production Economics, 176, 98-110. https://doi.org/10.1016/j.ijpe.2016.03.014

Xu, A., Chang, H., Xu, Y., Li, R., Li, X., & Zhao, Y. (2021). Applying artificial neural networks (ANNs) to solve solid waste-related issues: A critical review. Waste Management, 124, 385–402. https://doi.org/10.1016/j.wasman.2021.02.029

Yuan, Y. (2022). Cognitive Heterogeneous Wireless Network and Artificial Intelligence-Based Supply Chain Efficiency Optimization Application. Computational Intelligence and Neuroscience, 2022(8482365), 1–10. https://doi.org/https://doi.org/10.1155/2022/8482365

Zhang, S., & Duan, C. (2022). Clustering Optimization Algorithm for Data Mining Based on. Wireless Communications and Mobile Computing, 2022(1304951), 1–16. https://doi.org/10.1155/2022/1304951.

Zhang, Y., Liao, H., & Wang, Y. (2019). A deep learning approach to demand forecasting in supply chains. International Journal of Production Economics, 208, 96-102. https://doi.org/10.1016/j.ijpe.2018.11.014.

Downloads

Views

48

Downloads

8

Keywords:

Artificial Intelligence Decision Making Prediction Modeling Supply Chain Management Systematic Review