Organizational changes approaches to facilitate the management of Industry 4.0 transformation in manufacturing SMEs
Bibliographic record
Abstract
Purpose This paper aims to investigate the characteristics and dynamics of the organizational changes needed to facilitate the management of an Industry 4.0 transformation in manufacturing SMEs and propose an approach to manage them. Design/methodology/approach This research focuses on a single manufacturing SME in North America, and data were collected using a research intervention method. Data were collected through observation and intervention within the SME over 27 months. Findings The research has shown that organizational changes are required in manufacturing SMEs to better manage their Industry 4.0 transformation projects. Research limitations/implications Using the case study method limits the generalization of the results. The organizational changes observed, and their characteristics might be specific to the studied manufacturing. Although results could vary in different contexts, many manufacturing SMEs have similar characteristics to those observed in this study. Practical implications This research provides preliminary evidence of an iterative organizational change management approach that manufacturing SMEs must adopt to facilitate the management of their digital transformation. Originality/value This research provides a better understanding of how a manufacturing SME can improve its capabilities to manage its digital transformation by introducing iterative organizational changes. From these results, a link to the organizational learning literature can be drawn and developed upon.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".