Data-Driven Fuzzy Demand Forecasting Models for Resilient Supply Chains
Bibliographic record
Abstract
Uncertainty in supply chain leads to what is known as bullwhip effect (BE), which causes multiple inefficiencies such as higher costs of production (of more than what is needed), wastage and logistics. Though there are many studies reported in the literature, the impact of the quality of dynamic forecasts on the BE has not received sufficient coverage. In this paper, a fuzzy data-driven weighted moving average (DDWMA) forecasts of the future demand strategy is proposed for supply chain. Also, data-driven random weighted volatility forecasting model is used to study the fuzzy extended Bollinger bands forecasts of the demand. The main reason of using the fuzzy approach is to provide α-cuts for DDWMA demand forecasts as well as extended Bollinger bands forecasts. The proposed fuzzy extended Bollinger bands forecast is a two steps procedure as it uses optimal weights for both the demand forecasts as well as the volatility forecasts of the demand process. In particular, a novel dynamic fuzzy forecasting algorithm of the demand is proposed which bypasses complexities associated with traditional forecasting steps of fitting any time series model. The proposed data-driven fuzzy forecasting approach focuses on defining a dynamic fuzzy forecasting intervals of the demand as well as the volatility of the demand in supply chain. The performance of proposed approaches is evaluated through numerical experiments using simulated data and weekly demand data. The results show that the proposed methods perform well in terms of narrower fuzzy forecasting bands for demand as well as the volatility of the demand.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".