Strategic framework for resilient manufacturing: concurrent product design and supplier selection
Bibliographic record
Abstract
This paper introduces a practical framework to enhance manufacturing resilience and product reliability by integrating supply chain management with product design. The proposed approach optimises product reliability through redundancy allocation within the product design phase, with the consideration of compatibility between the product’s components. Concurrently, the methodology improves supply chain resilience by implementing multi-sourcing strategies and establishing backup supplier contracts to mitigate potential disruptions. Moreover, our numerical analysis demonstrates that integrating product design and supplier selection in a single optimisation framework yields significant cost reductions, as opposed to a traditional two-stage approach where product design and resilient supply chain decisions are made independently. A mathematical optimisation model is presented to formulate the described problem and genetic algorithm (GA) and particle swarm optimisation (PSO) methods are adopted as solution algorithms for a numerical example. Sensitivity analysis highlights that product reliability and supply chain resilience can be negatively correlated, between which finding the balance requires the manufacturer’s attention. The results indicate that incorporating both product design and supply chain considerations can significantly enhance overall manufacturing resilience, making the approach highly beneficial for industries aiming to improve their operational stability and reliability.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".