Resilience Analysis of Additive Manufacturing-enabled Supply Chains: An Exploratory Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it