Building data-driven procurement: Best practices for leveraging data and emerging technologies

Abstract

Modern supply chains are increasingly complex and subject to global disruptions, which has grown the need for companies to use data and new technologies to improve efficiency and resilience. This shift has made the role of procurement function more strategically important than ever before and increased the need to explore new technologies and better ways of working. This thesis explores effective ways for companies to use data and emerging technologies in procurement and aims to identify best practices for approaching these technologies and factors supporting their implementation. The research combined a review of existing literature with empirical research, including interviews with 13 industry professionals and representatives of solution providers. Based on the literature review, an initial framework for building data-driven procurement was created, outlining all the important aspects of using data and emerging technologies effectively in procurement. The framework was validated and updated according to empirical research findings. Key areas discussed in the research include data management practices, organizational structures and roles, tools and types of analytics solutions that facilitate the creation of data-driven insights. The findings of this study reveal the significant potential of technologies like AI, advanced analytics, and automation to transform procurement practices. A practically applicable framework is presented to support organizations aiming to build or update their procurement function or processes. While previous research has mainly concentrated on specific technologies in procurement and factors supporting the digitalization of procurement, this research has given an overview of what should be considered when building a procurement function that effectively utilizes data and advanced technologies