Cooperative Planning, Uncertainty, and Managerial Control in Concurrent Design
Bibliographic record
Abstract
We examine whether cooperative planning and uncertainty affect the magnitude of rework in concurrent engineering projects with upstream and downstream operations, and explore the impact of such rework on project delays. Using survey data from a sample of 120 business process (BP) redesign and related information technology (IT) development projects in healthcare and telecommunications, our results indicate that upstream (BP) rework and downstream (IT) rework is mediated and mitigated by cooperative planning through upstream/downstream strategy coupling and cross-functional involvement. In addition, uncertainty related to a lack of firm or industry experience with such projects increases the magnitude of upstream rework but not downstream rework or the amount of cooperative planning. After accounting for project scope, implementation horizon and whether delays are anticipated, we find that project delay is primarily influenced by the magnitude of downstream rework and downstream delay: the magnitude of both upstream and downstream rework significantly increases downstream delay, which significantly increases project delay. However, the magnitude of upstream rework does not directly affect project delay. These results suggest that project delay is under managerial control as cooperative planning is a managerial function that reduces downstream rework, while uncertainty from a lack of experience with the design affecting upstream rework is not directly under managerial control.
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.
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 | 0.017 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".