Supply Chain Risk in Eyeglass Manufacturing: An Empirical Case Study on Lens Inventory Management During Global Crises
Bibliographic record
Abstract
The eyeglass lens manufacturing industry has become increasingly vulnerable to supply chain risks due to overlapping global disruptions, including the COVID-19 pandemic, the Suez Canal blockage, the Russia–Ukraine conflict, Red Sea shipping insecurity, and recent U.S. import tariffs. These events have challenged inventory planning, supplier coordination, and cost control across the industry. This study aims to evaluate how five operational constructs—stock system, inventory optimization, standardized methodology, production capability, and logistics performance—influence inventory resilience during global crises. Using an empirical case study, data were collected from 215 supply chain professionals at a multinational lens manufacturer in Southeast Asia and analyzed via Structural Equation Modeling (SEM). The results show that inventory optimization (β = 0.93) is the most influential factor in mitigating supply–demand imbalances, followed by logistics performance and production capability. This study offers practical recommendations, including real-time demand tracking, modular production systems, and scalable logistics strategies, to enhance inventory resilience. These findings contribute to both theory and practice by providing a validated framework tailored to high-precision manufacturing under persistent global risk.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".