Integrating Modular Design Concepts for Enhanced Efficiency in Digital and Sustainable Manufacturing: A Literature Review
Bibliographic record
Abstract
Small- and medium-sized manufacturing enterprises (SMMEs) face intense competitiveness, necessitating ever greater productivity. Enterprises struggle to meet the demand for customized products while maintaining their productivity. The transition from mass customization (MC) to mass personalization (MPe) leads to a further increase in product variety and, thus, complexity. Digital transformation alone is not sufficient to achieve MPe and traditional adoption of modularity no longer ensures enterprise competitiveness in this context of increased variety. The synergy between modularity concepts could enhance the efficiency of this design strategy. This study is part of a research plan to develop an effective modularity implementation strategy addressing MPe. The aim of this article is to identify the main concepts and tools to be considered in an implementation strategy. Concepts and tools are grouped into four combinations according to the level of product variety in different production strategies. This preliminary work serves as the foundational research for a larger research plan aimed at adapting and validating a modular product development strategy that incorporates these modularity concepts.
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.
How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".