Effectiveness of policies for mitigating supply disruptions
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
Purpose The purpose of this paper is to examine supply‐side disruptions in a supply chain, and to analyse the effectiveness of two inventory‐based policies for mitigating the impact of supply disruptions: maintaining strategic inventory reserves (the R ‐policy), and using larger orders (the Q ‐policy). Design/methodology/approach The paper assess the effectiveness of two inventory‐based mitigating policies implemented at a reseller when end customer demand is stable but supply can be disrupted. An analytical model is provided, and numerical experiments are conducted to evaluate the effectiveness of the policies for mitigating the impact of disruption under different disruption scenarios. Findings Results indicate that the R ‐policy performs consistently better than the Q ‐policy in terms of product availability measures, as tested under a wide range of frequency and duration of supply disruptions. Practical implications Supply chain trends of lean operations and global sourcing have exposed business organizations to a greater risk and have further raised the need to protect businesses against random supply disruptions. Originality/value The paper intends to contribute to the narrowing of the gap in the research of supply‐side disruptions. Further, the topic of inventory reserves has been discussed to date in only a very general sense; the paper proposes conditions for practical implementation and provides unique insights into the effectiveness of the use of strategic inventory reserves as a supply disruption mitigation policy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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