Designing a Self-service Analytics System for Supply Base Optimization

Reducing the number of suppliers – a process known as supply base optimization – is crucial for organizations to achieve better quality, higher service levels, and lower prices. The buyers in the role of the business analyst in corporate purchasing departments are responsible for this process and usually consider various selection criteria. Their decisions rely on accessing and analyzing large amounts of data from different source systems, but typically, they lack the necessary technological and analytical knowledge, as well as adequate tools, to do this effectively. In this paper, we present the design and evaluation of a self-service analytics (SSA) system that helps business analysts manage the maintenance, repair, and operations (MRO) supply base. The system recommends shifting purchasing volume between suppliers based on a machine learning (ML) algorithm. The results demonstrate the potential of SSA systems in facilitating ML model consumption by business analysts to perform supply base optimization more effectively.