Developing assembly line layout for delayed product differentiation using phylogenetic networks
Bibliographic record
Abstract
Effective formation of product platforms helps adapt to product demand changes and decrease time-to-market and lead time. The product platform groups the core elements of product family members into a common module used to derive different product variants by combining it with different components. A new delayed product differentiation (DPD) platform network model, which applies median-joining phylogenetic networks (MJPN), is proposed. It is used for forming product platforms and determining the assembly line layout of modular product families. The MJPN is traditionally used for DNA sequences’ mapping, analysis, clustering and tracing evolutionary trends. The concept of assembly/disassembly modular platforms, whereby both assembly and disassembly of components are used to derive the final product variants from the platform, is utilised. The proposed model determines the required number and composition of a product platform and defines the DPD points. The developed dynamic assembly/disassembly platforms enhance routing and product mix flexibility due to having different platforms that can be used to produce the same product variant. A family of household kettles is used to demonstrate the application of the proposed model. A metric is presented for determining the effectiveness of a given platform in delaying the product differentiation, hence increasing the efficiency of mass customisation. The proposed metric, applied to the case study, demonstrated that the proposed platform formation model using MJPN is more capable of postponing the product differentiation point.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".