Knowledge worker performance analysis using DEA: an application to engineering design teams at Bell Canada
Bibliographic record
Abstract
Knowledge worker productivity measurement is a very difficult undertaking, but implementing improvement suggestions is even more challenging for management. Data envelopment analysis (DEA) was used to examine the productivity, efficiency, and effectiveness of one such knowledge worker group-the Engineering Design Teams (EDT) at Bell Canada, the largest telecommunications carrier in Canada. Two functional models of the EDTs were developed and analyzed using input oriented constant returns to scale (CRS) and variable returns to scale (VRS) DEA models. First left free, the multipliers were then constrained using DEA Assurance Region models based on economic prices and managerial preferences. This study offers an excellent example where inefficient decision making units (DMU)-i.e., EDTs-could be made more efficient by improving their scale efficiency simply by reassigning work amongst the units. Bell divides its EDTs along provincial boundaries into Ontario and Quebec teams and each EDT is responsible for a specific geographic area in the province assigned to it. The results of the DEA analysis indicated that redrawing the geographical boundaries of the market area served by the EDTs could move both increasing and decreasing returns to scale EDTs toward CRS behavior. Substantial performance improvements are possible over the entire system, resulting in significant savings in costs without people dislocation or branch closings.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| 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".