Six sigma for gamma-distributed processes: a case study in oil and gas
Bibliographic record
Abstract
Purpose The purpose of this paper is to study the relationship between reported sigma levels and actual failure rates (FRs) of gamma-distributed processes. The added complexity of the non-normality behavior of the gamma distribution is analyzed for the case of the cycle time (CT) of a real procurement process from the oil and gas industry. Then, recommendations and guidelines for the application of Six Sigma methodology for the case study are proposed. Design/methodology/approach Sensitivity analysis is conducted to study the relationship between gamma distribution parameters and FRs considering different quality levels. Then, adjustments for implementing Six Sigma programs for gamma processes are proposed. These adjustments consist of first determining the appropriate probability distribution, the standard CT and the due date, followed by setting performance zones and improvement strategies on target gamma parameters that yield the minimal FR. Findings For gamma-distributed processes, simply reporting the sigma level is not sufficient to capture the main characteristics of the process. These characteristics include process FR, mean setting, shape, spread and amount of variation reduction (i.e. improvement effort) required. That is why caution must be exercised when dealing with one-sided non-normal quality characteristics such as CT. Originality/value To the authors’ knowledge, this is the first time that the Six Sigma performance has been evaluated for gamma processes to analyze the link between Six Sigma FRs and gamma distribution parameters leading to the development of a modified Six Sigma methodology for non-normal processes.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".