Evaluating Canadian Bank Branch Operational Efficiency from Staff Allocation: A DEA Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We examine the operational efficiency of one of the large Canadian banks’ branches, which is primarily affected byits strategy of allocating staff and the service quality provided to customers. Two Data Envelopment Analysis (DEA)models are proposed in this research: (1) a staff allocation evaluation model pertinent to employee numbers andtransaction volumes, and (2) a customer satisfaction benchmark model to check if the staff allocation scheme meetsthe expectations of the bank's management. Constant Returns to Scale (CRS) and Variable Returns to Scale (VRS)model results for different branch sizes and geographical regions are presented for analysis. The findings arecompared to the bank’s current models, validating the use of the proposed DEA models for evaluating operationalefficiency from a staff allocation viewpoint in the banking industry. One of the interesting aspects of this work is thatthe requirement for best practice is not full efficiency but something less. The rationale is that if staff is pushed to thelimit, they break and leave – the costs of training and integrating new staff is very high and service levels suffer.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it