Implementation Of Lean Six Sigma To Improve The Quality Of Service Of A Company In The Restaurant Sector
Bibliographic record
Abstract
The research proposed to implement Lean Six Sigma in a restaurant company to improve the service quality of Siu Mai snacks delivered via delivery.The study was based on a pre-experimental design with a quantitative approach and evaluating customer perception before and after using the Servqual method.In the initial analysis, lack of employee induction, poor order management and lack of process evaluation were found as causes of low quality.Using DMAIC, processes were improved with BPMN and Kanban to follow the order flow, which optimized processes, reduced waiting times and improved service quality.The results showed that the Servqual score improved from 4.07 to 6.09, while the CP and CPK indicators improved significantly.It was concluded that the implementation of Lean Six Sigma using tools such as VSM, Kanban and SIPOC helped improve service quality and customer satisfaction by optimizing processes, reducing waiting times and improving the ability to respond empathetically to customers.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".