Methodological Evaluation of Manufacturing Systems in South Africa Using Time-Series Forecasting Models for Risk Reduction Assessment
Bibliographic record
Abstract
Manufacturing systems in South Africa face significant operational risks that can impact productivity and profitability. These risks include supply chain disruptions, equipment failures, and labour shortages. A comprehensive search was conducted across academic databases, including Scopus and Web of Science, using keywords related to manufacturing systems, risk assessment, time-series forecasting, and South Africa. Studies published between and were included in the review. The analysis revealed that while many studies applied ARIMA models for forecasting, there was a lack of consensus on which model provided the most accurate predictions across different manufacturing sectors. The average prediction error ranged from -4.6% to +5.1%, with some models showing higher variability in their forecasts. Despite the variability observed, time-series forecasting models can be effective tools for risk reduction if tailored appropriately to specific industry contexts and data characteristics. Manufacturers should consider conducting pilot studies using different models before full-scale implementation. Data quality improvement and model calibration are also recommended to enhance forecast accuracy. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
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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 | 0.011 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".