Logistic Postponement as a Risk Management Tool: A Real Options Valuation (ROV) Approach to Evaluate the Effectiveness of a Logistic Postponement Strategy in Mitigating the Demand Variability Risk in Global Supply Chains
Bibliographic record
Abstract
Recent world events such as the coronavirus pandemic and the war in Ukraine have caused increases in supply chain disruptions along global supply chains. The resulting supply chain challenges necessitate an increased effort in improving supply chain risk management for companies around the world. One source of uncertainty that is increasingly difficult to deal with is demand variability. With both supply and demand becoming increasingly difficult to predict, companies need tools to manage demand variability. Our work evaluates a logistic postponement solution to demand variability where safety stock is shipped from an overseas supplier to a distribution center instead of being shipped directly to retailers. By taking advantage of risk pooling, the proposed strategy aims at reducing stockouts at retailers well also reducing the present value of total costs incurring along the supply chain. A real options valuation (ROV) approach is used in this thesis to present both a theoretical model and a computational model. The theoretical model aims to provide an approach for supply chain practitioners to compare the logistic postponement strategy to their current strategy using historical data. On the other hand, the computational model incorporates some simplifications in the theoretical model to avail it for simulation. Sensitivity analyses conducted aim to provide an analysis on the potential cost savings and stockout reductions a logistic postponement strategy can provide.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".