Data-Driven Foresight in Life Cycle Management: An Interview Study

Marie Scheuffele (), Niklas Bayrle-Kelso () and Leo Brecht ()
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Marie Scheuffele: Universität Liechtenstein, Fürst-Franz-Josef-Strasse
Niklas Bayrle-Kelso: Universität Liechtenstein, Fürst-Franz-Josef-Strasse
Leo Brecht: Universität Liechtenstein, Fürst-Franz-Josef-Strasse

A chapter in Digital Disruption and Transformation, 2024, pp 131-151 from Springer

Abstract: Abstract Discontinuities in the market create space for disruptive business opportunities. A promising approach for companies to proactively identify future competitive advantages is Data-Driven Foresight (DDF). By using different data sources from various perspectives, DDF can derive solid statements about trend-driven developments in the future. As technology life cycles accelerate, industrial firms increasingly want to incorporate foresight activities into their Life Cycle Management to foster digital transformation. This raises the following research question: How do companies obtain their data for DDF in Life Cycle Management, and what alternative data sources are recommended? By conducting a systematic literature review, the state-of-the-art data sources are described and classified along the life cycle. Twenty semi-structured expert interviews with practitioners from different types of companies show valid premises for data selection and for the practical implementation of DDF. Regarding this, a recognizable difference between technology leaders and followers exists, which opens another gap for future research.

Keywords: Data-driven foresight; Trends research; Data sources; Foresight methods; Technology life cycles; Life cycle management; Literature review; Expert interviews (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-47888-8_7

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