Uptake and success factors of Six Sigma in the financial services industry
Bibliographic record
Abstract
Purpose The purpose of this paper is to present and discuss results from the first empirical study on the status and the success factors of Six Sigma in the financial services sector. Design/methodology/approach An empirical study using a comprehensive and tested survey instrument has been conducted in banks, insurance companies, and related service providers in Germany, Switzerland, Austria, and Great Britain. Findings One quarter of financial services providers has identified the Six Sigma methodology as being suited for their continuous process improvement efforts. Pressures to reduce costs, the desire to exploit market opportunities, and dissatisfied customers are the main drivers. However, the uptake of Six Sigma is still in the early stages. Most companies apply the methodology in pilot projects only. Nevertheless, respondents estimate a cost‐benefit ratio of 1‐4.3. Dissatisfaction with Six Sigma projects is often caused by insufficient data quality and data quantity, lack of resources, and missing support from top management. Originality/value Although Six Sigma has been successfully implemented in many industries, its application in the service sector is still in question. This paper presents for the first time results of a survey within the financial services industry with the aim to analyze the acceptance level of the Six Sigma methodology, the achieved results, and the factors that determine its successful implementation.
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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 | 0.016 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".