Statistical Quality Control (SQC) and Six Sigma Methodology: An Application of X-Bar Chart on Kuwait Petroleum Company
Bibliographic record
Abstract
An x-bar chart is a statistical device used for the study and control of a process. Control charts based on the three sigma limits were produced and have been used effectively for a long time. In today’s developed and developing countries, companies have introduced Six Sigma initiatives in their manufacturing processes which results in fewer defects, and identifies the causes of process variations. Companies employing Six Sigma initiatives are expected to produce 3.4 or less defects per million opportunities (DPMO) using the control limits suggested by Shewhart; then no point will fall outside the control boundaries because of reduction in variation. In this paper an attempt is made to construct a Six Sigma based on data collected from a petroleum company in Kuwait to produce an x-bar chart. Unfortunately, it seems there are some serious deficiencies in the production process since the value of Cp and Cpk are less than 1 which means the process is not capable of meeting its specifications.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| 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 | 0.000 |
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 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".