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International Journal of Computer Integrated Manufacturing Volume 33, 2020 - Issue 3
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Articles

Enterprise interoperability assessment: a requirements engineering approach

Gabriel S. S. LealITIS, Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg;CNRS, CRAN, Université de Lorraine, Nancy, FranceView further author information
,
Wided GuédriaITIS, Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg;CNRS, CRAN, Université de Lorraine, Nancy, FranceCorrespondence[email protected]
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Hervé PanettoCNRS, CRAN, Université de Lorraine, Nancy, FranceORCID Iconhttps://orcid.org/0000-0002-5537-2261View further author information
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Pages 265-286 | Received 11 Mar 2019, Accepted 20 Feb 2020, Published online: 09 Mar 2020

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Read on this site (2)

Abdelhadi Belfadel, Emna Amdouni, Jannik Laval, Chantal Bonner Cherifi & Nejib Moalla. (2022) Towards software reuse through an enterprise architecture-based software capability profile. Enterprise Information Systems 16:1, pages 29-70.
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Andrea Benešová, Josef Basl, Jiří Tupa & František Steiner. (2021) Design of a business readiness model to realise a green industry 4.0 company. International Journal of Computer Integrated Manufacturing 34:9, pages 920-932.
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Articles from other publishers (3)

Luiz Roberto Moreira de Castilho Junior, Eduardo de Freitas Rocha Loures, Eduardo Alves Portela Santos & Fernando Deschamps. (2023) Digital Transformation from Planning to Execution — A Strategic Framework based on Ambidexterity and Enterprise Architecture and Interoperability. Journal of Industrial Integration and Management.
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Jose María Alvarez-Rodríguez, Roy Mendieta, Eduardo Cibrián & Juan Llorens. (2023) Towards a method to quantitatively measure toolchain interoperability in the engineering lifecycle: A case study of digital hardware design. Computer Standards & Interfaces 86, pages 103744.
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Alexandros Bousdekis & Gregoris Mentzas. (2021) Enterprise Integration and Interoperability for Big Data-Driven Processes in the Frame of Industry 4.0. Frontiers in Big Data 4.
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