Modernizing Procurement in Supply Chain with AI and Machine Learning Techniques

Abstract

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.