Success Breakdown Structure: Application to Petrochemical Projects Success Assessment
Bibliographic record
Abstract
Project success is not fulfilled unless it lives up to stakeholders’ expectations. The fact that project success barely can be envisaged and construed, along with want of agreed upon success dimensions and criteria, augment the convolutedness of studying the concept. This paper focuses on assessing the success of petrochemical projects which not only engender employment and wealth but also underlie significant added-value to economies. This momentum unequivocally necessitates investigating petrochemical projects success. Therefore, through reviewing the seminal literature and consulting with experts, four success dimensions, four main stakeholders, and eighteen success criteria for petrochemical projects were discovered. Using these findings and results of a subsequent questionnaire survey, two petrochemical projects Success Breakdown Structures (SBSs) have been advanced. SBS1 breaks down the structure of success for petrochemical projects and clarifies that the success of these projects has four dimensions each of which is perceived differently by the stakeholders. In other words, by this SBS, it is possible to measure the “project’s efficiency”, “business success”, “preparing for future” and “impact on end-user” for each project. Having the success criteria rated, SBS2 or stakeholders-based SBS was developed. SBS2 not only contains the traditional success elements of cost, time, and quality but also reveals significant connections between stakeholders’ attitudes and petrochemical projects success. To validate the SBSs, the Delphi method was used. Based on SBS2 a mathematical model was structured to quantify the success of the petrochemical projects. A case study was conducted to test the serviceability of the model. The case study showcased that the stakeholders’ expectations directly affect the success of a petrochemical project. For instance, if contractors and consultants are unhappy with their profit margin or if the credit they gain is unsatisfactory, the success rate of the project declines. Similarly, from the viewpoints of end-users of petrochemical products supplying sufficient quantities of high-quality products is the most important contributor to the project success to which the project client has to pay attention.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".