The project investigates from a research perspective how to effectively utilize big data, predictive analytics and data science in procurement processes and decisions. It analyses current initiatives and future plans and possibilities of big data analytics in procurement. It develops a method and a commercial software product for procurement management. The project relates to creating supply market intelligence (SMI); the ways to capture, maintain and use multisided knowledge from the supply market.
The project utilizes advanced data science, and big data databases, as well as, survey and case study data.
The project is expected to result in improvements in current best purchasing practices and procurement efficiency. It provides literature reviews and cutting edge empirical case study based results on how big data can be used, including analytics, to create supply market intelligence. It also describes challenges and drivers for implementing and applying big data analytics in procurement for different types of companies and supply chains.
The project selects, analyses and demonstrates innovative use cases. Use cases describe what the Big Data enabled supply management can achieve. The use cases describe present big data applications and future possible scenarios. They are expected to provide understanding on the preconditions to reach the supply intelligence desired, the actors, the stakeholders involved and their interests, triggers, basic information sources and flows, and success scenarios.
Full Professor at Chalmers, Technology Management and Economics, Supply and Operations Management
Visiting Researcher at Chalmers, Technology Management and Economics, Supply and Operations Management
Senior Researcher at Chalmers, Technology Management and Economics, Supply and Operations Management
Funding Chalmers participation during 2017–2019
Areas of Advance
Areas of Advance