Estimation of Production Inhibition Time Using Data Mining to Improve Production Planning and Control
Bibliographic record
Abstract
To be competitive under the paradigm of Industry 4.0 (I4.0), companies must develop a Production Planning and Control (PPC) able to respond to disturbances. Notably, unexpected machine breaks leading to corrective maintenance actions drastically delay operations as they inhibit the machine production, affecting the production schedule. To create an adaptive PPC, data from the shop floor must be collected and analyzed. However, these data are often unstructured, as they come from either sensors or humans, which makes their use difficult. Notably, human based operations such as maintenance activities often produce textual data. Hence, the objective of this paper is twofold: firstly, it aims to propose a framework for a model capable of estimating the production inhibition time based on textual data. Secondly, it settles the basis for a Dynamic Production Schedule (DPS) model considering these estimations. To achieve this, an approach using Text Mining (TM) and Machine Learning (ML) techniques is proposed.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".