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

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dc.contributor.author Morgret, Linda
dc.contributor.author Feldmann, Carsten
dc.contributor.author Matthies, Benjamin
dc.date.accessioned 2023-12-26T18:37:03Z
dc.date.available 2023-12-26T18:37:03Z
dc.date.issued 2024-01-03
dc.identifier.isbn 978-0-9981331-7-1
dc.identifier.other b4461faf-c19f-4761-9ed4-8ed99e39072e
dc.identifier.uri https://hdl.handle.net/10125/106547
dc.language.iso eng
dc.relation.ispartof Proceedings of the 57th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Intelligent Decision Support for Logistics and Supply Chain Management
dc.subject correlation analysis
dc.subject decision analytics
dc.subject order-to-delivery process
dc.subject supply chain performance metrics
dc.subject value driver tree
dc.title Value Driver Trees for KPI-Based Decision Analytics: Process Performance in the Order-to-Delivery Process
dc.type Conference Paper
dc.type.dcmi Text
dcterms.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.
dcterms.extent 10 pages
prism.startingpage 1370
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