Value Driver Trees for KPI-Based Decision Analytics: Process Performance in the Order-to-Delivery Process

Abstract

The order-to-delivery process is one of the most complex logistics processes. Knowing how to successfully satisfy customers through this process is a critical competitive factor for companies. However, there are no suitable methods for value-based decision-making in this process. One goal of this research is to systematically derive a value driver tree based on axiomatic design. Value driver trees are conceptual models that mathematically or logically explain the cause-and-effect relationships between value drivers and their key performance indicators. A systematic literature review and expert interviews in the German manufacturing industry were conducted to provide practitioners with a validated model. In addition, statistical certainty about the relationships between the drivers of the tree is required. A correlation analysis based on real-world case study data confirmed monotonic relationships between selected metrics extending decision analytics research.