A sustainable continuous improvement methodology at an aerospace company
Bibliographic record
Abstract
Purpose The purpose of this paper is to present a continuous improvement methodology developed in an aerospace company that is successfully being used by other companies in various industries. Design/methodology/approach A case study was undertaken at a medium‐sized aerospace company for over a span of one year. Data was collected through in‐depth interviews, attendance at formal and informal meetings, observation, and company documentation. Findings The paper provides an overview of a continuous improvement methodology known as Achieving Competitive Excellence (ACE™), which aims to achieve world‐class quality in products and processes. The paper describes in detail the tools and techniques needed to implement and maintain the methodology. It was found that the company is very successful in addressing a wide range of aspects in the organization, always with the viewpoint that the customer is number one. This methodology is successful to the point that it is being used by other companies in various industries. Practical implications The approach of the ACE™ methodology can be applied to a variety of companies. Originality/value This paper presents for the first time the comprehensive Continuous Improvement methodology ACE™. The paper should be of value to practitioners of continuous improvement programs who are interested in a comprehensive approach to achieving excellence.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".