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.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".