Resilience Analysis of Additive Manufacturing-enabled Supply Chains: An Exploratory Study
Bibliographic record
Abstract
Unparalleled level of globalization and fierce competition have made supply chains (SCs) exceedingly complex and fragile as ever before. Increased incidences of natural disasters and unprecedented COVID-19 have highlighted the significance of improving supply chain resilience (SCR) by divulging its susceptibility to the external events. Additive manufacturing (AM) is envisioned as the disruptive technology that allows layer-wised fabrication and has been claimed to be an important contributor to the improved SCR as it could bring new opportunities through expanded design freedom, improved material efficiency, shortened supply chains, and decentralized manufacturing. Nonetheless, rare research has quantitatively measured the impacts of AM on SCR. To fill this research gap, the indices for assessing SCR of AM-enabled supply chains (AM-SCs) are first proposed, and then, the technique for order of preference by similarity to ideal solution (TOPSIS) is employed to derive a quantifiable SCR score that can be used to measure the performance of different SCs. A case study of a gas pedal assembly is presented with three different SC configurations: the original assembly with conventional manufacturing, original assembly with AM, and redesigned assembly with AM. The exploratory study shows that the redesigned assembly with AM considerations could improve the SCR by 200%. Sensitivity analysis also revealed that part count and reaction time of suppliers are influential factors of improving SCR. Last, challenges and limitations of the proposed framework are also deliberated upon alongside future research scope.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.002 | 0.000 |
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".