The process management triangle: An empirical investigation of process trade‐offs
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.
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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 | 0.031 | 0.155 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".