Data-driven foresight in life cycle management: an interview study

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Referenz

Scheuffele, M., Bayrle-Kelso, N., & Brecht, L. (2024). Data-driven foresight in life cycle management: an interview study. In D. Schallmo, A. Baiyere, F. Gersten, C. A. Foss Rosenstand & C. A. Williams (Eds.), Digital Disruption and Transformation - Case Studies, Approaches, and Tools (1 ed., pp. 131-151). Cham: Springer Cham.

Publikationsart

Beitrag in Sammelband

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.

Forschung

Foresight for a (product) lifetime: developing a lifecycle- encompassing foresight process
FFF-Förderprojekt, Juni 2023 bis April 2023

Der Umgang mit Diskontinuitäten im Markt, sich wandelnden Wettbewerbsvorteilen und bestehenden Informationsunsicherheiten in Entscheidungs- und Innovationsprozessen stellt eine tägliche ... mehr

Mitarbeiter

Einrichtungen

  • Liechtenstein Business School
  • Entrepreneurship
  • Technology & Innovation

Original Source URL

Link

DOI

http://dx.doi.org/10.1007/978-3-031-47888-8_7