Adopting new ARIMA double layering technique in make-to-stock production policy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In supply chain management, the employed forecasting algorithm plays a very vital and crucial role in how a particular business performs well in the market. This study proposes a time series based forecasting model to help in designing an effective make-to-stock supply chain mechanism for tackling the large number of suppliers (suppliers diversion) and products (items diversion). The proposed technique extends the traditional ARIMA model to ARIMA Double Layering Technique (DLT) where the algorithm is implemented in two layers: first one is to obtain the number of orders level, and the other layer is implemented at the quantity level. The systematic implementation procedure of our approach is illustrated through a real case study. The accuracy evaluation and consistency of the results show that ARIMA -DLT provides superior forecasting when compared with ML-based models which provides insightful managerial views to other similar forecasting problems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it