Risk management models for supply chain: a scenario analysis of outsourcing to China
Bibliographic record
Abstract
Purpose A key process involved in supply chains is a priori evaluation of potential partners, not only in terms of expected cost (which includes exchange rate risk), but also in terms of other risks. These risks can include product failure, producing company failure (such as bankruptcy), and even political risk. This paper aims to compare tools to aid supply chain organizations in measuring, evaluating, and assessing risk in this environment. Design/methodology/approach The authors demonstrate the use of DEA, followed by a DEA simulation model and also a Monte Carlo simulation using a risk‐adjusted cost concept. Once non‐dominated partners are identified by DEA, simulation analysis is applied to compare expected performance of vendors, and the range of expected outcomes can be identified, aiding supply chain core organizations to better select producing partners. Findings The authors consider strategies of outsourcing to China, as well as other nations under various forms of risk. A scenario analysis using risk management models indicates outsourcing to Great China is a good strategy. Originality/value The authors conducted a thorough review of supply chain risk management and identified criteria and various risk performance measures for outsourcing under risk and uncertainty in a supply chain. The benefit of outsourcing to China is discussed. The authors have designed an international outsourcing problem, where foreign exchange risk, product failure, organizational failure, and political risks are considered.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".