Evaluating Canadian Bank Branch Operational Efficiency from Staff Allocation: A DEA Approach
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".