Data Governance as the Digital Backbone of Proactive Obsolescence Management: A Design Science Case Study in Asset-Intensive Industries

Mircea R. Georgescu and Matthias Schmuck ()
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Mircea R. Georgescu: Faculty of Economics and Business Administration, Alexandru Ioan Cuza University, 700505 Iași, Romania
Matthias Schmuck: Doctoral School of Economics and Business Administration, Alexandru Ioan Cuza University, 700057 Iaşi, Romania

Economies, 2025, vol. 13, issue 9, 1-32

Abstract: Background: The service life and availability of electronic components are steadily declining, whereas the operational lifespan of industrial devices that incorporate such components often extends over several decades. This disparity creates a mismatch between the durability of individual components and the longevity of the overall systems in which they are embedded. Obsolescence Management (OM) addresses this issue by establishing a structured and controlled process aimed at anticipating and mitigating the impacts of component and product obsolescence. As defined by the international standard International Electrotechnical Commission [IEC] 62402:2019, obsolescence refers to the transition of an (electronic) item from availability to unavailability by the manufacturer, in accordance with the original specification. To implement proactive OM, obsolescence managers require data that are comprehensible, accurate, complete, trustworthy, secure, and discoverable. In this context, Data Governance (DG) offers a promising approach to enhance data literacy and intelligence within OM. Methods: This study employed a sequential mixed-methods design, integrating qualitative and quantitative approaches including a Systematic Literature Review (SLR), Expert Interviews (EIs), Focus Groups (FGs), Content Analysis (CA), and Workshops (WKSHs), within a case study informed by Design Science Research (DSR). Results: This paper proposes a DG structure tailored to support OM through data integration and business intelligence methods, drawing on established DG reference frameworks within an SME. The proposed structure encompasses a set of processes and knowledge domains recognized as best practices in the field. Furthermore, we present a model designed to facilitate the implementation of DG in OM and to assess the quality of the data required. This enables more reliable obsolescence processes across key functional areas such as product management, procurement, and product development, ultimately supporting data-driven and accurate decision-making.

Keywords: lifecycle management; supplier management; technology monitoring; proactive planning; risk management (search for similar items in EconPapers)
JEL-codes: E F I J O Q (search for similar items in EconPapers)
Date: 2025
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