In the current industrial landscape, digitization is essential for the survival and growth of companies of all sizes. The adoption of new technologies and organizational structures is critical as companies navigate complex digital transformations. While Maturity Models (MMs) offer significant support in these endeavors, existing models often overlook the integration of Information Technology systems, leading to gaps in assessing digital maturity. This paper addresses these shortcomings by (1) proposing a Fuzzy Cognitive Maps based MM which adeptly captures the causal relationships among IT systems and enabling technologies, and (2) defining and testing a methodology for constructing such a model with inputs from both industry experts and academic researchers. The new MM provides a holistic assessment of smart manufacturing maturity and outlines clear, context-specific pathways for digital transformation. A key feature of the proposed model is its practical applicability, demonstrated through a detailed case study, which validates the MM by applying it to a real-world manufacturing setting, assessing current digital maturity levels, and simulating various improvement scenarios. The results illustrate significant enhancements in digital maturity, affirming the model’s practical value. This evidence supports the MM’s utility in guiding companies toward informed digital advancements, with potential adaptations for specific markets and needs.

Assessing smart manufacturing by combining it systems and enabling technologies in a fuzzy cognitive maturity model / Andreou, Andreas S.; Bernabei, Margherita; Christoforou, Andreas; Colabianchi, Silvia; Costantino, Francesco; Leotta, Francesco; Mathew, Jerin George; Mecella, Massimo; Monti, Flavia. - In: INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING. - ISSN 0951-192X. - (2025). [10.1080/0951192X.2025.2501582]

Assessing smart manufacturing by combining it systems and enabling technologies in a fuzzy cognitive maturity model

Bernabei, Margherita;Colabianchi, Silvia;Costantino, Francesco;Leotta, Francesco;Mathew, Jerin George;Mecella, Massimo;Monti, Flavia
2025

Abstract

In the current industrial landscape, digitization is essential for the survival and growth of companies of all sizes. The adoption of new technologies and organizational structures is critical as companies navigate complex digital transformations. While Maturity Models (MMs) offer significant support in these endeavors, existing models often overlook the integration of Information Technology systems, leading to gaps in assessing digital maturity. This paper addresses these shortcomings by (1) proposing a Fuzzy Cognitive Maps based MM which adeptly captures the causal relationships among IT systems and enabling technologies, and (2) defining and testing a methodology for constructing such a model with inputs from both industry experts and academic researchers. The new MM provides a holistic assessment of smart manufacturing maturity and outlines clear, context-specific pathways for digital transformation. A key feature of the proposed model is its practical applicability, demonstrated through a detailed case study, which validates the MM by applying it to a real-world manufacturing setting, assessing current digital maturity levels, and simulating various improvement scenarios. The results illustrate significant enhancements in digital maturity, affirming the model’s practical value. This evidence supports the MM’s utility in guiding companies toward informed digital advancements, with potential adaptations for specific markets and needs.
2025
fuzzy cognitive maps; maturity model; smart manufacturing
01 Pubblicazione su rivista::01a Articolo in rivista
Assessing smart manufacturing by combining it systems and enabling technologies in a fuzzy cognitive maturity model / Andreou, Andreas S.; Bernabei, Margherita; Christoforou, Andreas; Colabianchi, Silvia; Costantino, Francesco; Leotta, Francesco; Mathew, Jerin George; Mecella, Massimo; Monti, Flavia. - In: INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING. - ISSN 0951-192X. - (2025). [10.1080/0951192X.2025.2501582]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1738604
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