Measuring performance in multi‐stage service operations: An application of cause selecting control charts
Bibliographic record
Abstract
Abstract Many multistage service operations exhibit the cascade property, where performance at one stage is statistically correlated with performance at the preceding stage. Prior research on multistage services has analyzed each process stage independently or in an additive manner. Increased emphasis on Six Sigma initiatives in services has rekindled interest in the use of control charts to monitor and control service processes. This study examines the cause selecting control chart as a methodology to monitor and identify potential problem areas in an actual cascade service process and compares the diagnostic capability of the cause selecting chart to that of a traditional Shewhart chart. A grocery store whose parent company was implementing efficient consumer response (ECR) serves as the research context. This study models the grocery store as a two‐stage cascade process and uses operating data from the store to construct a cause selecting chart and a traditional Shewhart chart for the front‐end operation. Analysis of the two charts reveals that the cause selecting chart outperforms the traditional control chart as tool for signaling unusual variation in performance at the front‐end stage. The analysis demonstrates that service managers can receive misleading or erroneous information from traditional control charts if the service process being monitored is a cascade process.
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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 | 0.012 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".