Identifying and Prioritizing Waste in OCTG Production Lines Through Value Stream Mapping and Borda Count Method
Bibliographic record
Abstract
The oil and gas industry faces perennial challenges related to cost reduction, product quality enhancement, and operational efficiency.To remain competitive in the market, companies must optimize their production lead times, reduce costs, and ensure high levels of customer service.This study aims to identify and prioritize critical wastes in OCTG production lines to inform operational improvements.This research thoroughly evaluates the existing state of operations and identifies critical areas for improvement by utilizing a combination of Value Stream Mapping (VSM) and Borda Count Methods (BCM).The VSM provides a clear understanding of the complex movement of resources and activities in manufacturing and distributing products, making it easier to identify any inefficiencies.The BCM offers a systematic method to prioritize discovered waste according to its impact and severity.The study's findings expose crucial obstacles and inefficiencies in the production lines of OCTG, with waiting times and defects surfacing as notable areas of concern.By employing BCM, it can prioritize these concerns and derive valuable insights to inform strategic decision-making and operational improvements.The long-term goal of this research is to advance the current efforts of the oil and gas industry in enhancing production processes, reducing costs, and enhancing overall operational efficiency.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".