Prioritization of Six-Sigma project selection
Bibliographic record
Abstract
Purpose With increasing choice from a range of programs, improvement project selection within broader supply chain context and resource constraints has become a major research challenge. The purpose of this paper is to investigate the different criteria for selecting Six-Sigma (SS) projects based on previous studies. The study is supported by two grounded theories: resource-based view and institutional norms. The criteria include: first, business drivers for improvement and the common performance metrics deployed; second, the organization’s stakeholders needs; and third, process owner’s needs. Design/methodology/approach To determine the relative importance of influencing factors, opinions were collected from 30 experienced practitioners including SS champions/master black-belts, company directors, consultants, and process owners through a series of interviews in small, medium, and large organizations including multi-national organizations. The evaluation of criteria is based on analytical hierarchy process. Findings The results show that impact on customer, financial impacts, and impact on operational goals are the most significant factors in selecting SS improvement project. Originality/value This study is a first attempt to determine the relative weight among SS project selection criteria, which help the practitioner to allocate their limited resources in implementing SS project.
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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 | 0.053 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".