A Hybrid Data-Driven Approach for Forecasting the Characteristics of Production Disruptions and Interruptions
Bibliographic record
Abstract
Manufacturing companies sometimes suffer from unexpected production disruptions/interruptions events (DIEs), affecting the production performance and cost. Since DIEs vary in type and cause, predicting the characteristics of their corresponding production downtimes is a challenging task. Although efforts have been devoted to forecast/prevent specific types of DIEs, such as machine-related events, it is still difficult to deal with the uncertainty caused by a combination of production DIEs of various types. Moreover, the absence of a realistic scenario generator incorporating DIEs has been a challenge in production scheduling under uncertainty. This study investigates the potential use of a hybrid data-driven approach in incorporating the uncertainties of a wide range of DIEs. In this approach, a random forest (RF) method and probability distributions are integrated to forecast the DIEs. The study was carried out based on the recorded DIEs in a Canadian company producing assembly parts for automotive industry. The performance of the proposed methodology for forecasting the production DIEs is evaluated by determining the predicted total downtime (TD) in percent of the expected processing time. The proposed hybrid model yields an overall accuracy of 92.82% in predicting the TD, compared to an overall accuracy of 75.64% when a single RF is used for prediction.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".