Artificial Intelligence And Machine Learning In Procurement Decision-Making
DOI:
https://doi.org/10.53819/81018102t7098Abstract
This systematic literature review examines the role of artificial intelligence (AI) and machine learning (ML) in transforming procurement decision-making processes across public and private sector organisations. Guided by the PRISMA protocol, the review involved a structured search of Scopus, Web of Science, IEEE Xplore, and EBSCO databases for studies published between 2013 and 2024. From an initial pool of 1,247 articles, 87 met the final inclusion criteria after rigorous screening and quality appraisal. The findings indicate that AI and ML applications in procurement are mainly concentrated in supplier selection and evaluation, demand forecasting, contract management, and spend analytics. The dominant algorithmic approaches include natural language processing, deep learning, and ensemble methods, which have contributed to improved cost reduction, shorter procurement cycle times, and enhanced visibility of supplier risks. Despite these benefits, adoption remains constrained by poor data quality, algorithmic bias, organisational resistance, and unclear regulatory frameworks. The review is limited to English-language peer-reviewed publications, which may exclude relevant evidence from non-Anglophone contexts. It recommends that practitioners and policymakers strengthen data infrastructure, ethical governance, and regulatory clarity to maximise the value of AI and ML in procurement. Overall, the review offers a comprehensive synthesis of AI and ML applications in procurement decision-making and provides a clear research agenda for scholars as well as an implementation roadmap for practitioners.
Keywords: artificial intelligence, machine learning, procurement, supply chain management, supplier selection, spend analytics, natural language processing, digital procurement transformation
References
Adjei-Bamfo, P., Maloreh-Nyamekye, T., & Ahenkan, A. (2019). The role of e-government in public procurement regulation in developing countries. Resources, Conservation and Recycling, 142, 189–203.
Arora, R., & Kaur, S. (2020). Anomaly detection in procurement data using isolation forests. Journal of Enterprise Information Management, 33(4), 891–910.
Bals, L., Schulze, H., Kelly, S., & Stek, K. (2019). Purchasing and supply management competencies: Current and future requirements. Journal of Purchasing and Supply Management, 25(5), 100572.
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120.
Beil, D. R. (2010). Supplier selection. In Wiley encyclopedia of operations research and management science. Wiley.
Bharadwaj, A. S. (2000). A resource-based perspective on information technology capability and firm performance. MIS Quarterly, 24(1), 169–196.
Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
Bommarito, M. J., & Katz, D. M. (2018). A natural language processing approach to the analysis of commercial contract risk. Journal of Legal Analysis, 10, 117–153.
Carbonneau, R., Kerr, G., & Laframboise, K. (2021). Reinforcement learning for dynamic procurement negotiation. Supply Chain Management: An International Journal, 26(3), 301–316.
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.
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, 49, 86–97.
Chai, J., Liu, J. N., & Ngai, E. W. (2013). Application of decision-making techniques in supplier selection: A systematic review of literature. Expert Systems with Applications, 40(10), 3872–3885.
Chalkidis, I., Androutsopoulos, I., & Michos, A. (2019). Extracting contract elements. Proceedings of the International Conference on Artificial Intelligence and Law, 19–28.
Chatterjee, S., Chaudhuri, R., & Vrontis, D. (2022). Does data-driven culture impact innovation and performance? Information Technology and Management, 23(3), 171–185.
Chen, F., Drezner, Z., Ryan, J. K., & Simchi-Levi, D. (2000). Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information. Management Science, 46(3), 436–443.
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188.
Cox, A. (2001). Understanding buyer and supplier power: A framework for procurement and supply competence. Journal of Supply Chain Management, 37(2), 8–15.
Croom, S., & Brandon-Jones, A. (2007). Impact of e-procurement: Experiences from implementation in the UK public sector. Journal of Purchasing and Supply Management, 13(4), 294–303.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
Deloitte. (2021). 2021 global chief procurement officer survey. Deloitte Consulting LLP.
Denyer, D., & Tranfield, D. (2009). Producing a systematic review. In D. Buchanan & A. Bryman (Eds.), The SAGE handbook of organizational research methods (pp. 671–689). SAGE.
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT 2019, 4171–4186.
Dickson, G. W. (1966). An analysis of vendor selection systems and decisions. Journal of Purchasing, 2(1), 5–17.
DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147–160.
Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv. https://arxiv.org/abs/1702.08608
Elmaghraby, W., & Keskinocak, P. (2003). Dynamic pricing in the presence of inventory considerations. Management Science, 49(10), 1287–1309.
Ellram, L. M., & Zsidisin, G. A. (2002). Factors that drive purchasing and supply management’s use of information technology. IEEE Transactions on Engineering Management, 49(3), 269–281.
European Commission. (2021). Proposal for a regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). COM(2021) 206 final.
Fazekas, M., & Kocsis, G. (2020). Uncovering high-level corruption: Cross-national objective corruption risk indicators using public procurement data. British Journal of Political Science, 50(1), 155–164.
Fildes, R., Ma, S., & Kolassa, S. (2022). Retail forecasting: Research and practice. International Journal of Forecasting, 38(4), 1283–1318.
Fosso Wamba, S., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How “big data” can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234–246.
Gardner, E. S. (2006). Exponential smoothing: The state of the art—Part II. International Journal of Forecasting, 22(4), 637–666.
Gartner. (2023). Magic quadrant for procure-to-pay suites. Gartner Research.
Glas, A. H., & Kleemann, F. C. (2017). The impact of Industry 4.0 on procurement and supply management. Supply Chain Forum: An International Journal, 18(4), 218–231.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
Grandia, J., & Meehan, J. (2017). Public procurement as a policy tool: Using procurement to reach desired outcomes in society. International Journal of Public Sector Management, 30(4), 302–309.
Grant, R. M. (1996). Toward a knowledge-based theory of the firm. Strategic Management Journal, 17(S2), 109–122.
Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5–14.
Handfield, R., Jeong, S., & Choi, T. (2020). Emerging procurement technology: Data analytics and cognitive analytics. International Journal of Physical Distribution & Logistics Management, 49(10), 972–1002.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning (2nd ed.). Springer.
Ho, W., Xu, X., & Dey, P. K. (2010). Multi-criteria decision-making approaches for supplier evaluation and selection: A literature review. European Journal of Operational Research, 202(1), 16–24.
Hong, Q. N., Fàbregues, S., Bartlett, G., Boardman, F., Cargo, M., Dagenais, P., Gagnon, M. P., Griffiths, F., Nicolau, B., O’Cathain, A., Rousseau, M. C., Vedel, I., & Pluye, P. (2018). The Mixed Methods Appraisal Tool version 2018 for information professionals and researchers. Education for Information, 34(4), 285–291.
Huber, J., Gossmann, A., & Stuckenschmidt, H. (2019). Cluster-based hierarchical demand forecasting for perishable goods. Expert Systems with Applications, 76, 140–151.
Ivanov, D., & Dolgui, A. (2021). OR-methods for coping with the ripple effect in supply chains during the COVID-19 pandemic: Managerial insights and research implications. International Journal of Production Economics, 232, 107921.
Krichel, F., Möller, K., & Meckl, R. (2021). Deep learning for spend classification in procurement. Supply Chain Management: An International Journal, 26(5), 621–636.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097–1105.
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
Lee, H. L., Padmanabhan, V., & Whang, S. (1997). Information distortion in a supply chain: The bullwhip effect. Management Science, 43(4), 546–558.
Leukel, J., Kirn, S., & Schlegel, T. (2011). Supply chain as a service: A cloud perspective on supply chain systems. IEEE Systems Journal, 5(1), 16–27.
Liao, Z., & Rittscher, J. (2007). A multi-objective supplier selection model under stochastic demand conditions. International Journal of Production Economics, 105(1), 150–159.
Lippi, G., Contissa, G., Lagioia, F., Preite, V., Santin, A., Sartor, G., & Torroni, P. (2019). The CLAUDETTE system: Automating the detection of unfair clauses in online terms of service. Artificial Intelligence and Law, 27(2), 117–139.
Mak, B., & Ramaprasad, A. (2003). Knowledge supply network. Journal of the Operational Research Society, 54(2), 175–183.
MarketsandMarkets. (2023). Procurement analytics market: Global forecast to 2028. MarketsandMarkets Research.
McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (1955). A proposal for the Dartmouth Summer Research Project on artificial intelligence. Dartmouth College.
Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Research, 98, 261–276.
Min, H. (2022). Artificial intelligence in supply chain management: Theory and applications. International Journal of Logistics: Research and Applications, 13(1), 13–39.
Mitchell, T. M. (1997). Machine learning. McGraw-Hill.
Monczka, R. M., Handfield, R. B., Giunipero, L. C., & Patterson, J. L. (2015). Purchasing and supply chain management (6th ed.). Cengage Learning.
Mose, J., & Njihia, J. M. (2014). The critical success factors and challenges of implementing e-procurement in Kenya. European Scientific Journal, 10(13), 214–231.
Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. Journal of Big Data, 2(1), 1–21.
North, M. J., & Macal, C. M. (2007). Managing business complexity: Discovering strategic solutions with agent-based modeling and simulation. Oxford University Press.
Ntoutsi, E., Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal, M. E., Ruggieri, S., Turini, F., Papadopoulos, S., Krasanakis, E., Kompatsiaris, I., Kinder-Kurlanda, K., Wagner, C., Karimi, F., Fernandez, M., Alani, H., Berendt, B., Krueger, T., Heinze, C., … Staab, S. (2020).
Bias in data-driven artificial intelligence systems: An introductory survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1356.
Orlikowski, W. J. (2000). Using technology and constituting structures: A practice lens for studying technology in organizations. Organization Science, 11(4), 404–428.
