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Leveraging knowledge graphs in pharmaceutical supply chains: insights into key drivers of drug shortages

Eberhardt, Katharina ORCID iD icon 1; Schwärzel, Amelie ORCID iD icon 1; Kaiser, Florian Klaus 1; Rosenberg, Sonja ORCID iD icon 1; Schultmann, Frank ORCID iD icon 1
1 Institut für Industriebetriebslehre und Industrielle Produktion (IIP), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

Supply chain issues pose a serious challenge to the pharmaceutical industry. The COVID-19 pandemic, raw material and labour bottlenecks, and rising demand from an ageing population have exacerbated disruptions, sparking discussions on the resilience of pharmaceutical supply chains. In Germany, drug shortages have drastically increased, leading to several supply deficits for specific medicines. The complex environment and extensive information necessitate a unified view that is both scalable and flexible. Therefore, we systematically collect and fuse disparate and heterogeneous medical data sources to build a comprehensive knowledge graph. By integrating interconnections between entities, the graph facilitates the identification of critical suppliers and drug patterns. The results reveal a significant increase in shortages in Germany. The drivers of these shortages are multifaceted and primarily relate to production, market, and process issues. Furthermore, we quantify a shortage's severity and economic impact to support targeted measures. Based on our research, we provide decision support and mitigation strategies, enabling the identification and prioritisation of shortages and strengthening resilience. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000181579
Veröffentlicht am 08.05.2025
Originalveröffentlichung
DOI: 10.1080/00207543.2025.2496671
Scopus
Zitationen: 1
Web of Science
Zitationen: 1
Dimensions
Zitationen: 1
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Industriebetriebslehre und Industrielle Produktion (IIP)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 02.10.2025
Sprache Englisch
Identifikator ISSN: 0020-7543, 1366-588X
KITopen-ID: 1000181579
Erschienen in International Journal of Production Research
Verlag Taylor and Francis
Band 63
Heft 19
Seiten 7129–7152
Vorab online veröffentlicht am 08.05.2025
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Web of Science
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