Bibliographic record
Abstract
The literature of Leagile supply chain (LASC) is lacking of the concurrence between supply chain (SC) design and product design, and missing the placement of decoupling point (DP) in the SC design. Therefore, the paper aims at presenting a novel framework to optimise LASC design while fulfilling the aforementioned gaps. The first step utilises Lean tools to identify the optimal architecture of product families through the so-called Leagile bill-of-material in product design. This phrase intends to reduce the storage keeping unit of components (leaner) while increasing their combining ability in a wider range of new products (more agile). Meanwhile, the second stage outlines the preliminary configuration of the future supply chain and transforms it into the Lean system. Next, the supplier network of this chain is matched with the product structure. The last step formulates the issue in one mathematical model to define the optimal LASC’s configuration, which includes positioning the best DP in various delivery lead time. In discussing the obtained solutions, the article complements to the theoretical basis by examining the locations of DP corresponding to the product’s complexity. The whole framework is illustrated by one specific example and solved by Priority Generic Algorithm Meta-Heuristic, programed with MATLAB.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".