Article section
Application of Artificial Intelligence in Supply Chain Management: A Review on Strengths and Weaknesses of Predictive Modeling Techniques
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.
Keywords:
Artificial Intelligence Decision Making Prediction Modeling Supply Chain Management Systematic Review
Article information
Journal
Scientific Journal of Engineering, and Technology
Volume (Issue)
1(2), (2024)
Pages
1-18
Published
Copyright
Copyright (c) 2024 Aminu Adamu Ahmed, Ali Usman Abdullahi, Abdulsalam Ya’u Gital, Abubakar Yusuf Dutse (Author)
Open access
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
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.