Why Projects Fail (and What We Can Do About It)
Bibliographic record
Abstract
Over two-thirds of all mega projects result in failure, meaning they significantly exceed budget, miss schedule targets, or fail to achieve production close to design capacity. The reasons for project failure have been well documented over the past fifty years. Despite this large body of empirical evidence, many executive and project leadership teams continue to repeat the mistakes made on past projects. This can be partially attributed to project teams believing that their projects are somehow different from past projects and that others’ project mistakes are not relevant to their project. This paper is a literature review that considers the seven common root causes of project failure and how these root causes relate to the pipeline industry. No new primary data will be presented. The seven common root causes for project failure and their approximate impact on budget variance are: 1. Failure to complete front end loading = 60–85% 2. escalation = Up to 12% 3. Regulatory regimes = Up to 12% 4. Plant complexity = Up to 20% 5. New technology = Up to 20% 6. Solid feedstock = Up to 10% 7. Complex ownership = Up to 24% This paper will also review and discuss seven common project traits closely associated with project failure, although not direct root causes. These traits are: 1. Concurrent detailed design and construction = up to four times greater risk profile 2. Non-integrated project team = up to three times greater risk 3. Contractual risk misallocation = up to two and a half times greater risk 4. Fast-tracking projects = up to two times greater risk 5. Lack of internal capacity = up to two times greater risk 6. Oil and Gas industry = up to two times greater risk 7. Brownfield vs. greenfield site = no direct impact With these root causes and traits identified, several methods of risk and contingency analysis will be examined. An evaluation of each method’s ability to increase the success rate of capital projects will be discussed; ultimately, resulting in a recommendation on the optimal risk and contingency framework for improving project success rates. The paper will conclude with a summary of how Stantec’s risk and contingency framework is being implemented on pipeline projects.
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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 | 0.017 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".