Ordering Behavior Under Supply Risk:An Experimental Investigation
Bibliographic record
Abstract
As supply chains become increasingly complex and global in their scale, supplier selection and management in the face of disruption risk has become one of the most challenging tasks for modern managers. Several novel model-based approaches to managing such risks have been developed in the academic literature, but how behavioral tendencies may affect procurement decisions under such conditions has received relatively less attention. In this paper, we present results from a study where paid subjects were asked to place orders from two suppliers who differ in their costs and risks to satisfy a fixed amount of end-customer demand. We show that under such a scenario, it is theoretically optimal to sole source either from the more reliable (and more costly) supplier or from the more risky but cheaper supplier, depending on cost and risk parameters. Subjects in our experiment, however, show a systematic tendency to diversify their orders between the two sources. We document this diversification tendency in procurement decisions and its possible impact on profits under various cost and risk settings as well as comment on various ordering behavior observed during the experiments. We also establish that bounded rationality of subjects can provide a possible rationale for the above phenomenon.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".