Data-driven risk forecasting applications to supply chain management
Bibliographic record
Abstract
Effective risk management is crucial for identifying, assessing, and monitoring risks in supply chain management and enables preventative action to protect businesses from financial losses. This thesis introduces novel data-driven strategies to enhance risk forecasting in supply chain operations. The first part of the study focuses on demand forecasting using the simple moving average (SMA), and the Bollinger bands. The research highlights the suitability of the data-driven approach utilizing $t$ distribution for constructing resilient Bollinger bands.~A novel data-driven resilient Bollinger band based on correlation-based estimating functions (EF) is proposed for demand forecasting.~The volatility estimates incorporating different correlation-based estimating functions (EF) are also discussed in some detail. Risk-adjusted forecasts (RAFs) are computed considering three risk measures based on sign correlation, skew correlation, and volatility correlation estimates. The resilient forecasts are derived using data-driven weighted moving average (DDWMA) and SMA methods, providing forecast intervals with coverage probabilities that evaluate the performance of the models. The second part of the study focuses on forecasting slow-moving items in supply chain management and introduces a data-driven exponentially weighted moving average (DDEWMA) model with seasonal index s = 30 to model the monthly seasonal demand for forecasting. The novelty of the proposed approach is to forecast demand for count time series of slow-moving items. The correlation-based approach effectively mitigates the effect of extreme values on seasonal demands and enhances the accuracy of risk forecasting (particularly for slow-moving seasonal items). A data-driven volatility estimate (DDVE) is introduced. Furthermore, the correlation-based DDVE provides better volatility forecasting for the neural network-based forecasts compared to the seasonal model and traditional Croston model. The proposed data-driven approaches offer valuable insights into supply chain management, enabling businesses to decide and mitigate potential financial losses.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".