Please use this identifier to cite or link to this item:
Krol, Felix
Saeed, Muhammad Amad
Kersten, Wolfgang
Year of Publication: 
[Editor:] Kersten, Wolfgang [Editor:] Blecker, Thorsten [Editor:] Ringle, Christian M. [Title:] Data Science and Innovation in Supply Chain Management: How Data Transforms the Value Chain. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 29 [ISBN:] 978-3-7531-2346-2 [Year:] 2020 [Pages:] 797-847
Purpose: The aerospace supply chain is characterized by a high degree of small andmedium-sized suppliers. To stay competitive, suppliers are facing high pressures todigitalize their business but have limited resources available. Furthermore, aerospacesuppliers lack a framework to measure their current state of digitalization.Therefore, this paper provides a holistic digitalization KPI framework for manufacturingaerospace companies. Methodology: The framework is based on a top-down and bottom-up developmentapproach. Within the top-down approach, 42 digitalization maturity models are beinganalyzed to identify relevant dimensions. To reveal digitalization indicators, acomprehensive literature review is being used as a bottom-up approach. Indicatorsare then assigned to the sub-dimension. Finally, indicators are grouped to similar indicatorsand merged to digitalization KPI. Findings: The developed KPI framework encompasses 89 digitalization KPI amongnine dimensions: Strategy and Organizational Leadership, Governance and TransformationManagement, Digital Skills/Human Capital, Smart Product, Customer Focus,Smart Process/Operations, Digital Technology, Financial Focus, and Network and Security. Originality: The presented digitalization KPI framework provides a scientific foundationfor measuring the digitalization maturity level of aerospace companies.Therefore, maturity models and benchmarking tools can incorporate the developed(sub-) dimensions and KPI to measure and compare the digital readiness of aerospacecompanies as well as to derive guidance for areas of improvement.
Industry 4.0
Supply Chain Management
Artificial Intelligence
Data Science
Persistent Identifier of the first edition: 
Creative Commons License:
Document Type: 
Conference Paper

Files in This Item:

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.