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
The effective utilization of capacity is an important operational goal that managers strive to achieve. Most textbooks use the following simple “bottleneck formula” to illustrate the calculation of process capacity: the capacity of each resource is first calculated by examining that resource in isolation; process capacity is then taken as the smallest (bottleneck) among the resource capacities. The bottleneck formula is, in fact, an approximation of the true process capacity and correctly calculates capacity only in some straightforward settings, for example, in processes where each activity requires only one resource and in processes where each resource is dedicated to only one activity. However, when activities require multiple resources simultaneously (collaboration) and when resources are capable of doing multiple activities (multitasking), the simple formula can be significantly inaccurate. Further, several commonly held managerial insights related to process capacity and least-capacity resources that emerge from the formula can be misleading. The main goal of this case is to alert students that, for processes with collaboration and multitasking, the use of the bottleneck formula brings the potential danger of reaching incorrect conclusions about capacity and what constitutes a bottleneck of a process and may eventually lead to erroneous decisions with significant financial impact, for example, investing in procuring an expensive resource without being able to realize the presumed increase in capacity. More generally, the case illustrates the principles of process capacity and bottleneck structures and clarifies some often-repeated misunderstandings on the relationship between process capacity and least-capacity resources. The case also illustrates the importance of using Gantt charts for conveniently displaying schedules of activities.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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