Association Between Supply Chain Glitches and Operating Performance
Bibliographic record
Abstract
This paper empirically documents the association between supply chain glitches and operating performance. The results are based on a sample of 885 glitches announced by publicly traded firms. Changes in various operating performance metrics for the sample firms are compared against a sample of control firms of similar size and from similar industries. In the year leading up to the announcement, the control-adjusted mean percent changes in operating income, return on sales, and return on assets for the sample firms are −107%, −114%, and −92%, respectively. During this same period, the control-adjusted changes in the level of return on sales and return on assets are −13.78% and −2.32%, respectively. Relative to controls, firms that experience glitches report on average 6.92% lower sales growth, 10.66% higher growth in cost, and 13.88% higher growth in inventories. More importantly, firms do not quickly recover from the negative economic consequences of glitches. During the two-year time period after the glitch announcement, operating income, sales, total costs, and inventories do not improve. We also find that it does not matter who caused the glitch, what the reason was for the glitch, or what industry a firm belongs to—glitches are associated with negative operating performance across the board.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".