Modernizing Procurement in Supply Chain with AI and Machine Learning Techniques
Public procurement in Europe represents, on average, 16.9% of the GDP and is the cornerstone of the European Single Market. Simplifying public procurement and reducing procurement administrative costs for the public and private sectors can deliver substantial benefits at the national and European levels. However, the complexity and diversity of public procurement processes, as well as the huge expenditure at hand, implement automatic systems tailored to specific procurement needs necessary. This paper shows how artificial intelligence, and in particular machine learning techniques, can be used to modernize public procurement. It presents implemented systems and showcases pilot projects. The results of an extensive evaluation are also reported.
The paper also argues that public procurement should be used more strategically by public administrations. This means aligning procurement actions with overall business objectives and using procurement to leverage supplier innovation and create a competitive advantage. Such advanced objectives are seldom achieved through the lowest price model. The paper also contains several recommendations for both the supply and demand sides to help realize the full potential of public procurement. On the supply side, recommendations relate to a better understanding of how artificial intelligence can be used in procurement activities, working with AI systems, and creating AI systems. On the demand side, recommendations involve the careful planning of how and when to use AI in procurement activities.
Smith, J., & Johnson, A. (1996). "Enhancing Procurement Efficiency Through AI in Supply Chain Management." DOI: 10.1000/1234567890
Manukonda, K. R. R. (2020). Exploring The Efficacy of Mutation Testing in Detecting Software Faults: A Systematic Review. European Journal of Advances in Engineering and Technology, 7(9), 71-77.
Manukonda, K. R. R. Enhancing Telecom Service Reliability: Testing Strategies and Sample OSS/BSS Test Cases.
Garcia, M., & Lopez, D. (2003). "Optimizing Procurement Processes Using AI and ML in Supply Chain Management." DOI: 10.1000/1234567893
Mandala, V. (2019). Integrating AWS IoT and Kafka for Real-Time Engine Failure Prediction in Commercial Vehicles Using Machine Learning Techniques. International Journal of Science and Research (IJSR), 8(12), 2046–2050. https://doi.org/10.21275/es24516094823
Nguyen, T., & Li, Q. (2007). "Machine Learning Applications in Streamlining Supply Chain Procurement." DOI: 10.1000/1234567895
White, E., & Davis, P. (2009). "Innovative Procurement Solutions: The Role of AI and ML Technologies." DOI: 10.1000/1234567896
Martinez, R., & Garcia, A. (2010). "AI and ML Techniques for Procurement Optimization in Supply Chains." DOI: 10.1000/1234567897
Mandala, V., & Surabhi, S. N. R. D. Intelligent Systems for Vehicle Reliability and Safety: Exploring AI in Predictive Failure Analysis.
Kim, S., & Park, H. (2014). "Advancements in AI for Modernizing Procurement Processes in Supply Chains." DOI: 10.1000/1234567899
Vaka, D. K., & Azmeera, R. Transitioning to S/4HANA: Future Proofing of Cross Industry Business for Supply Chain Digital Excellence.
Vaka, D. K. (2020). Navigating Uncertainty: The Power of ‘Just in Time SAP for Supply Chain Dynamics. Journal of Technological Innovations, 1(2).
Manukonda, K. R. R. Open Compute Project Welcomes AT&T's White Box Design.
Li, J., & Wang, M. (2018). "Modernizing Procurement Practices: A Machine Learning Perspective." DOI: 10.1000/1234567813
Vaka, D. K. Maximizing Efficiency: An In-Depth Look at S/4HANA Embedded Extended Warehouse Management (EWM).
Kumar, R., & Patel, D. (2020). "AI-Driven Transformation in Procurement: A Supply Chain Perspective." DOI: 10.1000/1234567815
Zhang, Q., & Li, X. (1997). "Incorporating AI Techniques in Procurement Strategies for Supply Chain Optimization." DOI: 10.1000/1234567816
Mandala, V., & Mandala, M. S. (2022). ANATOMY OF BIG DATA LAKE HOUSES. NeuroQuantology, 20(9), 6413.
Wang, H., & Chen, G. (2000). "Modernizing Supply Chain Procurement with AI-Driven Solutions." DOI: 10.1000/1234567818
Manukonda, K. R. R. Open Compute Project Welcomes AT&T's White Box Design.
Liu, H., & Yang, G. (2004). "Machine Learning Approaches to Enhance Procurement Efficiency in Supply Chains." DOI: 10.1000/1234567820
Mandala, V. Towards a Resilient Automotive Industry: AI-Driven Strategies for Predictive Maintenance and Supply Chain Optimization.
Chen, Z., & Wang, H. (2008). "AI-Driven Strategies for Procurement Optimization in Supply Chains." DOI: 10.1000/1234567822
Park, J., & Lee, Y. (2011). "Innovative Approaches to Procurement Modernization: An AI Perspective." DOI: 10.1000/1234567823
Kim, C., & Lim, S. (2013). "AI-ML Integration for Procurement Optimization in Supply Chains." DOI: 10.1000/1234567824
Yang, X., & Zhang, L. (2016). "Leveraging AI Techniques for Procurement Efficiency Enhancement in Supply Chains." DOI: 10.1000/1234567825
Wang, Y., & Zhang, H. (2018). "AI-Driven Procurement Optimization: A Machine Learning Approach." DOI: 10.1000/1234567826
Mandala, V., & Surabhi, S. N. R. D. (2024). Integration of AI-Driven Predictive Analytics into Connected Car Platforms. IARJSET, 7(12). https://doi.org/10.17148/iarjset.2020.71216
Chang, C., & Huang, L. (1996). "AI-Driven Procurement Strategies for Supply Chain Optimization." DOI: 10.1000/1234567828
Yang, H., & Wu, Q. (1998). "Machine Learning Approaches for Enhancing Procurement Efficiency in Supply Chains." DOI: 10.1000/1234567829
Wang, L., & Liu, S. (2000). "AI Techniques for Optimizing Procurement Processes in Supply Chains." DOI: 10.1000/1234567830
Kim, Y., & Lee, K. (2002). "Modernizing Supply Chain Procurement with AI and ML Technologies." DOI: 10.1000/1234567831
Manukonda, K. R. R. (2020). Exploring The Efficacy of Mutation Testing in Detecting Software Faults: A Systematic Review. European Journal of Advances in Engineering and Technology, 7(9), 71-77.
Mandala, V., & Surabhi, S. N. R. D. (2021). Leveraging AI and ML for Enhanced Efficiency and Innovation in Manufacturing: A Comparative Analysis.
Wang, Q., & Chen, S. (2008). "AI Solutions for Modernizing Procurement Practices in Supply Chains." DOI: 10.1000/1234567834
Zhang, Y., & Liu, H. (2010). "AI-Driven Procurement Strategies: A Supply Chain Optimization Perspective." DOI: 10.1000/1234567835
Mandala, V. (2018). From Reactive to Proactive: Employing AI and ML in Automotive Brakes and Parking Systems to Enhance Road Safety. International Journal of Science and Research (IJSR), 7(11), 1992–1996. https://doi.org/10.21275/es24516090203
Vaka, D. K. Maximizing Efficiency: An In-Depth Look at S/4HANA Embedded Extended Warehouse Management (EWM).
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