Skip to main content
SpringerLink
Log in

Machine Learning and Supply Chain Management

  • Living reference work entry
  • First Online:
The Palgrave Handbook of Supply Chain Management

  • 204 Accesses

Abstract

Scholars have turned to highly capable machine learning (ML) approaches for analyzing and interpreting huge amounts of data due to the limitations of older methodologies. There has been a recent uptick in using machine learning algorithms in supply chain management (SCM). This chapter uses some literature and a bibliometric analysis to provide an overview of the field. Overall, ML is applied for supplier management, risk management, transport and distribution, and the circular economy. Some of the areas of study we review, based on a bibliometric analysis, include frameworks, performance management, and artificial intelligence (AI) challenges for supply chain management. Conversely, issues rarely discussed include the selection of ML techniques for supply chain management (SCM), sustainability issues, the future of ML in supply chain management, and system requirements for ML in supply chain management. Based on these issues, we provide insights for managers, interesting research areas for future research directions for SCM researchers, and application insight for SCM practitioners.

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

Access this chapter

Log in via an institution

Institutional subscriptions

References

  • Akbari, M., & Do, T. N. A. (2021). A systematic review of machine learning in logistics and supply chain management: Current trends and future directions. Benchmarking. https://doi.org/10.1108/BIJ-10-2020-0514

  • Al-Taie, M. Z., Kadry, S., & Lucas, J. P. (2019). Online data preprocessing: A case study approach. International Journal of Electrical and Computer Engineering. https://doi.org/10.11591/ijece.v9i4.pp2620-2626

  • Bai, C., Quayson, M., & Sarkis, J. (2021). COVID-19 pandemic digitization lessons for sustainable development of micro-and small-enterprises. Sustainable Production and Consumption. https://doi.org/10.1016/j.spc.2021.04.035

  • Bai, C., Quayson, M., & Sarkis, J. (2022). Analysis of Blockchain’s enablers for improving sustainable supply chain transparency in Africa cocoa industry. Journal of Cleaner Production, 358(October 2020), 131896. https://doi.org/10.1016/j.jclepro.2022.131896

    Article  Google Scholar 

  • Bai, C., Rezaei, J., & Sarkis, J. (2017). Multicriteria green supplier segmentation. IEEE Transactions on Engineering Management. https://doi.org/10.1109/TEM.2017.2723639

  • Barnes, J. (2015). Getting started with Azure Machine Learning. Azure Machine Learning Microsoft Azure Essentials.

    Google Scholar 

  • Baryannis, G., Dani, S., & Antoniou, G. (2019). Predicting supply chain risks using machine learning: The trade-off between performance and interpretability. Future Generation Computer Systems, 101, 993–1004. https://doi.org/10.1016/j.future.2019.07.059

    Article  Google Scholar 

  • Bodendorf, F., Merkl, P., & Franke, J. (2021). Intelligent cost estimation by machine learning in supply management: A structured literature review. Computers and Industrial Engineering, 160(July). https://doi.org/10.1016/j.cie.2021.107601

  • Brintrup, A., Pak, J., Ratiney, D., Pearce, T., Wichmann, P., Woodall, P., & McFarlane, D. (2020). Supply chain data analytics for predicting supplier disruptions: A case study in complex asset manufacturing. International Journal of Production Research. https://doi.org/10.1080/00207543.2019.1685705

  • Bukhari, Z., Yahaya, J., & Deraman, A. (2019). Metric-based measurement and selection for software product quality assessment: Qualitative expert interviews. International Journal of Advanced Computer Science and Applications. https://doi.org/10.14569/ijacsa.2019.0100732

  • Bunker, R. P., & Thabtah, F. (2019). A machine learning framework for sport result prediction. Applied Computing and Informatics. https://doi.org/10.1016/j.aci.2017.09.005

  • Burkart, N., & Huber, M. F. (2021). A survey on the explainability of supervised machine learning. Journal of Artificial Intelligence Research, 70, 245–317.

    Article  Google Scholar 

  • Carbonneau, R., Laframboise, K., & Vahidov, R. (2008). Application of machine learning techniques for supply chain demand forecasting. European Journal of Operational Research, 184(3), 1140–1154. https://doi.org/10.1016/j.ejor.2006.12.004

    Article  Google Scholar 

  • Cavalcante, I. M., Frazzon, E. M., Forcellini, F. A., & Ivanov, D. (2019). A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. International Journal of Information Management. https://doi.org/10.1016/j.ijinfomgt.2019.03.004

  • Chen, L., Lu, K., Rajeswaran, A., Lee, K., Grover, A., Laskin, M., Abbeel, P., Srinivas, A., & Mordatch, I. (2021). Decision transformer: Reinforcement learning via sequence modeling. ArXiv, abs/2106.0.

    Google Scholar 

  • Choy, K. L., Lee, W. B., & Lo, V. (2003). Design of an intelligent supplier relationship management system: A hybrid case based neural network approach. Expert Systems with Applications, 24(2), 225–237. https://doi.org/10.1016/S0957-4174(02)00151-3

  • Ding, Y., Chowdhury, G. G., & Foo, S. (2001). Bibliometric cartography of information retrieval research by using co-word analysis. Information Processing and Management. https://doi.org/10.1016/S0306-4573(00)00051-0

  • Donthu, N., Kumar, S., & Pattnaik, D. (2020). Forty-five years of Journal of Business Research: A bibliometric analysis. Journal of Business Research, 109(October 2019), 1–14. https://doi.org/10.1016/j.jbusres.2019.10.039

    Article  Google Scholar 

  • Dora, M., Kumar, A., Mangla, S. K., Pant, A., & Kamal, M. M. (2021). Critical success factors influencing artificial intelligence adoption in food supply chains. International Journal of Production Research. https://doi.org/10.1080/00207543.2021.1959665

  • Dubey, R., Gunasekaran, A., Childe, S. J., Blome, C., & Papadopoulos, T. (2019). Big data and predictive analytics and manufacturing performance: Integrating institutional theory, resource-based view and big data culture. British Journal of Management, 30(2), 341–361. https://doi.org/10.1111/1467-8551.12355

    Article  Google Scholar 

  • Dubey, R., Gunasekaran, A., Childe, S. J., Bryde, D. J., Giannakis, M., Foropon, C., Roubaud, D., & Hazen, B. T. (2020). Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organizations. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2019.107599

  • Efendigil, T., Önüt, S., & Kahraman, C. (2009). A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis. Expert Systems with Applications, 36(3 PART 2), 6697–6707. https://doi.org/10.1016/j.eswa.2008.08.058

  • Effah, D., Chunguang, B., Appiah, F., Agbley, B. L. Y., & Quayson, M. (2022). Carbon emission monitoring and credit trading: The blockchain and IOT approach. 106–109. https://doi.org/10.1109/iccwamtip53232.2021.9674144.

  • Eshaghi, A., Young, A. L., Wijeratne, P. A., Prados, F., Arnold, D. L., Narayanan, S., Guttmann, C. R. G., Barkhof, F., Alexander, D. C., Thompson, A. J., Chard, D. T., & Ciccarelli, O. (2021). Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data. Nature Communications, 12.

    Google Scholar 

  • Feizabadi, J. (2022). Machine learning demand forecasting and supply chain performance. International Journal of Logistics Research and Applications. https://doi.org/10.1080/13675567.2020.1803246

  • Gambella, C., Ghaddar, B., & Naoum-Sawaya, J. (2021). Optimization problems for machine learning: A survey. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2020.08.045

  • Gunawan, T. S., Ashraf, A., Riza, B. S., Haryanto, E. V., Rosnelly, R., Kartiwi, M., & Janin, Z. (2020). Development of video-based emotion recognition using deep learning with Google Colab. Telkomnika (Telecommunication Computing Electronics and Control). https://doi.org/10.12928/TELKOMNIKA.v18i5.16717

  • Hospedales, T., Antoniou, A., Micaelli, P., & Storkey, A. (2022). Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/TPAMI.2021.3079209

  • Imambi, S., Prakash, K. B., & Kanagachidambaresan, G. R. (2021). PyTorch. EAI/Springer Innovations in Communication and Computing. https://doi.org/10.1007/978-3-030-57077-4_10

  • Javadpour, L. (2022). Using RapidMiner for executing queries and visualization in a traditional database course. Journal of Education for Business. https://doi.org/10.1080/08832323.2021.1924106

  • Joshi, A. V. (2020). Azure Machine Learning. Machine Learning and Artificial Intelligence. https://doi.org/10.1007/978-3-030-26622-6_22

  • Jurczyk, T. (2021). Clustering with scikit-learn in Python. Programming Historian. https://doi.org/10.46430/phen0094

  • Kamble, S. S., Gunasekaran, A., Kumar, V., Belhadi, A., & Foropon, C. (2021). A machine learning based approach for predicting blockchain adoption in supply chain. Technological Forecasting and Social Change, 163(November 2020), 120465. https://doi.org/10.1016/j.techfore.2020.120465

    Article  Google Scholar 

  • Ketkar, N., & Moolayil, J. (2021). Introduction to PyTorch. Deep Learning with Python. https://doi.org/10.1007/978-1-4842-5364-9_2

  • Kosasih, E. E., & Brintrup, A. (2022). A machine learning approach for predicting hidden links in supply chain with graph neural networks. International Journal of Production Research. https://doi.org/10.1080/00207543.2021.1956697

  • Kulkarni, A., & Shivananda, A. (2021). Natural language processing recipes. Natural Language Processing Recipes. https://doi.org/10.1007/978-1-4842-7351-7

  • Kumar, V., Pallathadka, H., Kumar Sharma, S., Thakar, C. M., Singh, M., & Kirana Pallathadka, L. (2021). Role of machine learning in green supply chain management and operations management. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2021.11.625

  • Lee, I., & Mangalaraj, G. (2022). Big data analytics in supply chain management: A systematic literature review and research directions. Big Data and Cognitive Computing. https://doi.org/10.3390/bdcc6010017

  • Malviya, L., Shreemali, J., Ojha, R., Chakrabarti, P., & Poddar, S. (2021). Transformer prediction in the supply chain using machine learning. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2020.12.625

  • Maraza-Quispe, B., Valderrama-Chauca, E. D., Cari-Mogrovejo, L. H., Apaza-Huanca, J. M., & Sanchez-Ilabaca, J. (2022). A predictive model implemented in knime based on learning analytics for timely decision making in virtual learning environments. International Journal of Information and Education Technology. https://doi.org/10.18178/ijiet.2022.12.2.1591

  • Merlini, D., & Rossini, M. (2021). Text categorization with WEKA: A survey. Machine Learning with Applications. https://doi.org/10.1016/j.mlwa.2021.100033

  • Milad, A., Yusoff, N. I. M., Majeed, S. A., Ibrahim, A. N. H., Hassan, M. A., & Ali, A. S. B. (2020). Using an Azure Machine Learning approach for flexible pavement maintenance. Proceedings – 2020 16th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2020. https://doi.org/10.1109/CSPA48992.2020.9068684

  • Mohamed-Iliasse, M., Loubna, B., & Abdelaziz, B. (2020). Is machine learning revolutionizing supply chain? Proceedings – 2020 5th International Conference on Logistics Operations Management, GOL 2020. https://doi.org/10.1109/GOL49479.2020.9314713

  • Nagar, D., Raghav, S., Bhardwaj, A., Kumar, R., Lata Singh, P., & Sindhwani, R. (2021). Machine learning: Best way to sustain the supply chain in the era of industry 4.0. Materials Today: Proceedings, 47, 3676–3682. https://doi.org/10.1016/j.matpr.2021.01.267

    Article  Google Scholar 

  • Nayal, K., Raut, R. D., Queiroz, M. M., Yadav, V. S., & Narkhede, B. E. (2021). Are artificial intelligence and machine learning suitable to tackle the COVID-19 impacts? An agriculture supply chain perspective. International Journal of Logistics Management. https://doi.org/10.1108/IJLM-01-2021-0002

  • Ng, W., Minasny, B., de Sousa Mendes, W., & Melo Demattê, J. A. (2020). The influence of training sample size on the accuracy of deep learning models for the prediction of soil properties with near-infrared spectroscopy data. The Soil. https://doi.org/10.5194/soil-6-565-2020

  • Ni, D., Xiao, Z., & Lim, M. K. (2020). A systematic review of the research trends of machine learning in supply chain management. International Journal of Machine Learning and Cybernetics. https://doi.org/10.1007/s13042-019-01050-0

  • Ninasivincha-Apfata, J. E., Quispe-Figueroa, R. C., Valderrama-Solis, M. A., & Maraza-Quispe, B. (2021). Dashboard proposal implemented according to an analysis developed on the KNIME platform. World Journal on Educational Technology: Current Issues. https://doi.org/10.18844/wjet.v13i4.6267

  • Pang, B., Nijkamp, E., & Wu, Y. N. (2020). Deep learning with TensorFlow: A review. Journal of Educational and Behavioral Statistics. https://doi.org/10.3102/1076998619872761

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

  • Quayson, M., Bai, C., & Osei, V. (2020). Digital inclusion for resilient post-COVID-19 supply chains: Smallholder farmer perspectives. IEEE Engineering Management Review, 8581(c). https://doi.org/10.1109/EMR.2020.3006259

  • Quayson, M., Bai, C., & Sarkis, J. (2020). Technology for social good foundations: A perspective from the smallholder farmer in sustainable supply chains. IEEE Transactions on Engineering Management, 68(3), 894–898.

    Article  Google Scholar 

  • RapidMiner. (2016). RapidMiner. https://doi.org/10.1201/b16023.

  • Sharma, R., Kamble, S. S., Gunasekaran, A., Kumar, V., & Kumar, A. (2020). A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Computers and Operations Research, 119, 104926. https://doi.org/10.1016/j.cor.2020.104926

    Article  Google Scholar 

  • Sheng, H., Feng, T., Chen, L., & Chu, D. (2021). Responding to market turbulence by big data analytics and mass customization capability. Industrial Management and Data Systems. https://doi.org/10.1108/IMDS-03-2021-0160

  • Singh, P., & Manure, A. (2020). Learn TensorFlow 2.0. Learn TensorFlow 2.0. https://doi.org/10.1007/978-1-4842-5558-2

  • Strozzi, F., Colicchia, C., Creazza, A., & Noè, C. (2017). Literature review on the ‘smart factory’ concept using bibliometric tools. International Journal of Production Research. https://doi.org/10.1080/00207543.2017.1326643

  • Sun, S., Cao, Z., Zhu, H., & Zhao, J. (2020). A survey of optimization methods from a machine learning perspective. IEEE Transactions on Cybernetics. https://doi.org/10.1109/TCYB.2019.2950779

  • Tao, C., Gao, J., & Wang, T. (2019). Testing and quality validation for AI software-perspectives, issues, and practices. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2937107

  • Tirkolaee, E. B., Sadeghi, S., Mooseloo, F. M., Vandchali, H. R., & Aeini, S. (2021). Application of machine learning in supply chain management: A comprehensive overview of the main areas. Mathematical Problems in Engineering, 2021(Ml). https://doi.org/10.1155/2021/1476043

  • Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research. https://doi.org/10.1016/j.jbusres.2020.09.009

  • University of Waikato. (2016). Weka 3 – Data mining with open source machine learning software in Java. The University of Waikato.

    Google Scholar 

  • Villegas, M. A., & Pedregal, D. J. (2018). Supply chain decision support systems based on a novel hierarchical forecasting approach. Decision Support Systems, 114, 29–36.

    Article  Google Scholar 

  • Viswanathan, A., Wang, V., & Kononova, A. (2020). Controlling formality and style of machine translation output using AutoML. Communications in Computer and Information Science. https://doi.org/10.1007/978-3-030-46140-9_29

  • Walker, K. (2018). Google AI principles updates, six months in. Google.

    Google Scholar 

  • Waltman, L., van Eck, N. J., & Noyons, E. C. M. (2010). A unified approach to mapping and clustering of bibliometric networks. Journal of Informetrics. https://doi.org/10.1016/j.joi.2010.07.002

  • Wang, W., Rivera, D. E., & Kempf, K. G. (2007). Model predictive control strategies for supply chain management in semiconductor manufacturing. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2006.05.013

  • Wiederhold, G., & McCarthy, J. (2010). Arthur Samuel: Pioneer in machine learning. IBM Journal of Research and Development. https://doi.org/10.1147/rd.363.0329

  • Wuest, T., Weimer, D., Irgens, C., & Thoben, K. D. (2016). Machine learning in manufacturing: Advantages, challenges, and applications. Production and Manufacturing Research, 4(1), 23–45. https://doi.org/10.1080/21693277.2016.1192517

    Article  Google Scholar 

  • Yang, M., Fu, M., & Zhang, Z. (2021). The adoption of digital technologies in supply chains: Drivers, process and impact. Technological Forecasting and Social Change. https://doi.org/10.1016/j.techfore.2021.120795

  • Zhu, C., & Gao, D. (2016). Influence of data preprocessing. Journal of Computing Science and Engineering. https://doi.org/10.5626/JCSE.2016.10.2.51

  • Zhu, Y., Zhou, L., Xie, C., Wang, G. J., & Nguyen, T. V. (2019). Forecasting SMEs’ credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach. International Journal of Production Economics, 211(January), 22–33. https://doi.org/10.1016/j.ijpe.2019.01.032

Download references

Author information

Authors and Affiliations

  1. School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, China

    Matthew Quayson, Chunguang Bai & Derrick Effah

  2. Department of Logistics and Supply Chain Management, Ho Technical University, Ho, Ghana

    Matthew Quayson

  3. School of Business and Social Sciences, International University of Grand-Bassam, Grand-Bassam, Côte d’Ivoire

    Kwame Simpe Ofori

Authors
  1. Matthew Quayson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chunguang Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Derrick Effah
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kwame Simpe Ofori
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Matthew Quayson .

Editor information

Editors and Affiliations

  1. Business School, Worcester Polytechnic Institute, Worcester, MA, USA

    Joseph Sarkis

Rights and permissions

Reprints and permissions

Copyright information

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

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Quayson, M., Bai, C., Effah, D., Ofori, K.S. (2023). Machine Learning and Supply Chain Management. In: Sarkis, J. (eds) The Palgrave Handbook of Supply Chain Management. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-89822-9_92-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-89822-9_92-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Palgrave Macmillan, Cham

  • Print ISBN: 978-3-030-89822-9

  • Online ISBN: 978-3-030-89822-9

  • eBook Packages: Springer Reference Business and ManagementReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences

Publish with us

Policies and ethics

Search

Navigation