A digital maturity model for assessing SMEs in the manufacturing sector
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents the development of a Digital Maturity Model (DMM) designed to support small and medium-sized manufacturing enterprises (SMEs) in their transition towards Industry 4.0. SMEs face distinct challenges compared to large enterprises, mainly due to financial constraints, limited digital skills and reliance on short-term operational priorities, which necessitate flexible and modular solutions adapted to their context. Existing DMMs show critical limitations, including weak practitioner involvement, lack of multidimensional integration, insufficient consideration of lower maturity levels and absence of actionable strategic outputs. To address these gaps, a six-phase methodology was followed, including qualitative case studies with three Canadian manufacturing SMEs. The final DMM includes five dimensions, 34 subdimensions and 49 indicators covering technological, managerial and organizational aspects. Case studies revealed an overview of digital maturity in SMEs with a strong strategic awareness of digital transformation and early-stage integration of emergent technologies. They also highlighted persistent barriers such as limited digital capabilities, resistance to change and regulatory challenges. The DMM provides practical value as a descriptive tool for assessing maturity and guides both ecosystem positioning and the development of digital transformation roadmaps. It also contributes to the academic field as a replicable and adaptable approach for evaluating digital maturity in SMEs and fostering innovation in manufacturing ecosystems. Future research should focus on developing a scoring methodology and conducting quantitative validation to enhance reliability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it