The process management triangle: An empirical investigation of process trade‐offs
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
Abstract Advancing theory and understanding of process management issues continues to be a central concern for operations management research and practice. While an insightful body of knowledge – based primarily on studies at the process‐level – exists on the management of capacity and inventory, the dynamism characterizing most operating and competitive systems poses an ongoing challenge for managers having to mitigate the impact of variability across different levels of operating systems (e.g., production processes, facilities, and supply chains). This paper builds on a conceptual framework, derived from queuing theory and termed the “process management triangle”, to explore the extent to which fundamental trade‐offs between capacity utilization, variability and inventory (CVI) generalize to complex operations and business systems. To do so, empirical analyses utilizing comparatively unique data for the study of these process management issues – and collected from two distinct, vastly different levels of analysis – are presented. First, a simulation‐based facility‐level analysis using teaching case study data is presented. Second, an industry‐level analysis employing archival economic data spanning three multi‐year periods is considered. Collectively, these empirical analyses provide exploratory support for the generalization and extension of analytical insights on CVI trade‐offs to both complex operations and business systems, although with decreasing explanatory power. The implications of these studies for furthering process management theory and understanding are framed around additional research propositions intended to guide future investigation of CVI trade‐offs.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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