A holistic digitalization KPI framework for the aerospace industry

Felix Krol, Muhammad Amad Saeed and Wolfgang Kersten

A chapter in Data Science and Innovation in Supply Chain Management: How Data Transforms the Value Chain, 2020, pp 797-847 from Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management

Abstract: 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.

Keywords: Logistics; Industry 4.0; Digitalization; Innovation; Supply Chain Management; Artificial Intelligence; Data Science (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/228940/1/hicl-2020-29-797.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zbw:hiclch:228940

DOI: 10.15480/882.3109

Access Statistics for this chapter

More chapters in Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL) from Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().