Implementing Craft Productivity to Improve Project Performance
Bibliographic record
Abstract
Abstract Compared to Traditional Project Delivery, Craft Productivity Project Delivery improves total installed cost (TIC), schedule and throughput significantly without degrading quality, function or operability. Senior engineering, project and construction managers and engineers recognize the value of Craft Productivity Project Delivery but delay implementing claiming too many other initiatives and because the clients keep paying the bills. The oil and gas industry associates their facility based major capital projects (MCP) as Harry C. Stonecipher, President, Boeing described manufacturing airplanes in their aerospace industry in 1998, "All of us have grown up with a cost-plus or a performance-at-any-price mentality. Instead of driving down costs relentlessly from one year to the next, we have been used to steady increases in the cost and price of just about everything. The real world -- whether military or commercial -- won't support it any more." Boeing moved to Craft Productivity Manufacturing improving cost, schedule and throughput without degrading quality, function or operability Learning from the aerospace industry, this paper discusses implementing Craft Productivity Project Delivery in labor intensive, highly customized and expensive MCPs world. Specifically, this paper discusses differences between Traditional and Craft Productivity Project Delivery and implementing the Craft Productivity processes in the construction phase of MCP including - Integrated Project Teams (IPT), field work-task packaging, workface flow, Execution Planning, pull production and waste reduction.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".