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Artificial intelligence implementation in manufacturing SMEs: A resource orchestration approach
Open accessAbstract
Artificial intelligence (AI) is playing a leading role in the digital transformation of enterprises, particularly in the manufacturing industry where it has been responsible for a profound transformation in key business and production operations. Despite the accelerated growth of AI technologies, knowledge of the implementation of AI by small and medium-sized enterprises (SMEs) remains underexplored. Thus, this study seeks to examine how manufacturing SMEs orchestrate resources for AI implementation. Building on the resource orchestration (RO) theory and recent work on AI implementation, we investigate multiple case studies involving manufacturing SMEs in Sweden operating in the packaging, plastic, and metal sectors. Our findings indicate that SMEs structure a portfolio based on acquiring and accumulating AI resources. AI resources are bundled into learning and governance capabilities to leverage configurations for AI implementation. Through a dynamic process of AI resource orchestration, SMEs effectively leverage AI resources and capabilities by mobilising technologies, coordinating manufacturing processes, and empowering skilled people. This research contributes to existing practice and the academic literature on AI implementation, highlighting how SMEs orchestrate AI resources and capabilities to drive an organisation’s digital transformation whilst creating a competitive advantage.
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“process innovation was also found to have a significant impact on the sustainability of SMEs”
From Innovation as A Driver of SMEs Sustainability: A Systematic Review of the Literature (2025) · cites this paper as Peretz-Andersson et al., 2024
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“Based on research (Peretz-Andersson et al., 2024) which examines Innovationinnovation for SMEs sustainability, especially through the application of AI that drives company productivity and performance and forms business models and core processes that increase competitiveness.”
From Innovation as A Driver of SMEs Sustainability: A Systematic Review of the Literature (2025) · cites this paper as Peretz-Andersson et al., 2024
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“Technologies such as 3D printing, automation, and artificial intelligence (AI) can be used to increase production speed and precision, thereby reducing waste and improving product consistency (Peretz-Andersson et al., 2024).”
From Innovation as A Driver of SMEs Sustainability: A Systematic Review of the Literature (2025) · cites this paper as Peretz-Andersson et al., 2024
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“The application of AI drives enterprise productivity and performance and shapes core business models and processes that enhance competitiveness.”
From Innovation as A Driver of SMEs Sustainability: A Systematic Review of the Literature (2025) · cites this paper as Peretz-Andersson et al., 2024
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Metadata
- Authors
- Peretz-Andersson, E., Tabares, S., Mikalef, P., & Parida, V.
- Affiliations
- Jönköping University (SE)
- Luleå University of Technology (SE)
- Norwegian University of Science and Technology (NO)
- Corresponding author
- Peretz-Andersson, Einav
- Venue
- International Journal of Information Management
- Publisher
- Elsevier BV
- Published
- 2024-08
- Volume
- 77
- Pages
- 102781
- ISSN
- 0268-4012 (ISSN-L)
- OpenAlex
- W4393910198
- Cited by
- 218
- References
- 65
- Citations by year
- 2026: 93 · 2025: 98 · 2024: 27
- FWCI
- 66.31
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Information Management (publishedVersion, cc-by)
- Osuva (University of Vaasa) (submittedVersion, cc-by)
- Epubl LTU (submittedVersion, cc-by)
- Funders
- Knowledge Foundation
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Social Sciences › Business, Management and Accounting › Strategy and Management › Innovation and Knowledge Management
- Keywords
- AI implementation, small and medium-sized enterprises, manufacturing SMEs, resource orchestration theory, learning ability, governance capacity, digital transformation, competitive advantage, packaging industry, plastics industry, metal industry
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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