The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Additive manufacturing and the global factory: Disruptive technologies and the location of international business
Abstract
Abstract Additive manufacturing (AM) is an emergent technology that is shifting the nature of production, sourcing, and other value-chain activities. AM has the potential to substantially disrupt the structure and operations of international business. In this paper, we leverage the global factory concept (e.g., Buckley & Ghauri, 2004) to frame our discussion of the likely impact of AM on global production. We identify and conceptualize specific variables and relationships to offer a nuanced explanation that highlights the potential re-distribution of global production at four levels of analysis – global, country, local area, and household. We propose how key variables – Intellectual Property Status, Industrial Standards, Branding, Aesthetics, Authenticity, Material Type, Complexity, Customization, Size, Logistical Complexity, Delivery Timeliness, Demand, Access, and Technical Competence – likely will impact localization of production. We examine industries and production activities likely to be most affected by AM. We conclude with a discussion of managerial and practical implications and identify avenues for further research.
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The Governance of the Global Factory: Location and Control of World Economic Activity
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A literature review of disruptive innovation: What it is, how it works and where it goes
Si, S., & Chen, H. (2020) · Journal of Engineering and Technology Management · doi:10.1016/j.jengtecman.2020.101568 · Cited by 226
Additive manufacturing and supply chains–a systematic review
Kunovjanek, M., Knofius, N., & Reiner, G. (2022) · Production Planning & Control · doi:10.1080/09537287.2020.1857874 · Cited by 149
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Metadata
- Authors
- Hannibal, M., & Knight, G.
- Venue
- International Business Review
- Publisher
- Elsevier BV
- Published
- 2018-12
- Volume
- 27
- Issue
- 6
- Pages
- 1116-1127
- ISSN
- 0969-5931 (ISSN-L)
- OpenAlex
- W2801649197
- Cited by
- 191
- References
- 8
- FWCI
- 9.33
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.