Implementation of Transactional Planning Systems for the Plastics Industry
Bibliographic record
Abstract
Transactional systems are an alternative process improvement for any industrial sector; however, due to the rapid growth of the plastics industry worldwide, this industry requires the automation of production with agile systems. This document presents a procedure to implement transactional tools of the Master Production Schedule (MPS) and Materials Requirements Planning (MRP) for the automation and control of the operations area processes in an organization. These processes are part of Enterprise Resource Planning (ERP) tools that use connections to mobile devices and are often compatible with different customer support systems, allowing the integration of all business units to interact with the manufacturing control and purchasing. The method used in the research have a quantitative cut in which 15 companies were studied in the plastics sector in Mexico and was divided in three phases, these focused on the revision of the business processes and the analysis of the substantive processes of the organization that allowed the subsequent establishment of proposals for improvement. In the final proposal of implementation of the tool tansaccional included: the functional analysis of the systems, the planning and their evaluation.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".