Design for mass customisation, design for manufacturing, and design for supply chain: A review of the literature
Bibliographic record
Abstract
Abstract This survey provides a review of the fundamental approaches to design for mass customisation (DFMC), design for manufacturing (DFM) and design for supply chains (DFSC). The key term here is design while mass customisation, manufacturing and supply chain are the contexts from which the respective design objectives are derived. While these three areas of design are closely related, they have different focusses, which is reflected in the broader range of approaches proposed in the literature. The authors look at the literature through the lens of the product, process, and supply chain optimisation, with a variety of objectives ranging from improving product quality and variety while reducing costs, minimising environmental impacts and optimising supplier manufacture cooperation. In addition to the reviews of the approaches to DFMC, DFM and DFSC, recommendations on their practical system implementations are provided. While the authors acknowledge that the richness of the literature of each of the three design areas warrants a dedicated literature review, the main purpose of this survey is to pursue an integrated view on the three design issues faced by modern manufacturers and provide them and other related practitioners with a summary of representative approaches in the literature. Although it was not intended to conduct an exhaustive literature review of the literature, researchers from academia may still find the work useful by looking at the interactions of the three design areas from the perspective of joint‐decision making, which is the angle from which the literature is approached.
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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".