Call-type dependence in multiskill call centers
Bibliographic record
Abstract
The effect on multiskill call-center performance of pooling dependent call types is investigated. For this purpose, a copula-based modeling approach is used to provide multivariate models that take into account the call types’ asymmetric dependence structures found in empirical data. Then, the realistic input models of the call-type-dependent arrival processes are used in a simulation study to explore the sensitivity of the pooling decision to this dependence. We find that the widely used assumption of independence, as well as the misspecification of the dependence structure, can lead to substantial misestimation of call-center performance. This demonstrates the importance of modeling call-type dependence in stochastic simulation studies of call centers. We also show, through case studies, that pooling two asymmetric left-tail-dependent call types is more likely to lead to low agents occupancy; whereas the presence of right-tail dependence structure increases the risk of service-level shortfall. This work provides new managerial insights to improve decision making in determining which call types to merge in the same pool in multiskill call centers.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".