Learnings From Implementing a Management System Approach to Managing Research and Development (R&D): A Case Study on Implementing Structured Processes
Bibliographic record
Abstract
Enbridge Pipelines believes that a strong research and development (R&D) program is critical to ensure leading-edge safety and operational practices. As such, the Pipeline Integrity Department has launched an initiative to clearly identify, develop, and manage processes and practices to improve the transfer of knowledge from its R&D program to key operational challenges. The Integrity Solutions group within the Pipeline Integrity Department supports the R&D program from idea generation to operationalization (including knowledge integration) into existing programs. This approach begins with scanning the horizon to enable future opportunity areas and challenges to be captured through facilitation of “blue sky sessions”. Technology roadmaps are developed for each major threat category and used to prioritize R&D projects. Project execution is managed from project proposal to close-out through a stage-gate style process that allows the department to plan, organize, and execute projects that directly link to integrity-related threats. The final stage is operationalization, where R&D knowledge is transferred into pipeline operation practices and project execution lessons learned are captured and addressed. Through execution of the overall process supporting multi-year initiatives, Pipeline Integrity has gained experience and insight into specific strategies and tactics that are effective in overcoming the barriers presented in a well-managed R&D Program. This insight will be shared as a summary of a successful and practical management system approach to R&D initiatives. This paper describes the Management System approach: • Requirements: expectations and requirements for managing R&D. • Design: developing the system. • Implementation: deployment of the system. • Performance: data collection and reporting. • Continuous Improvement: analysis of results and lessons learned, including next steps.
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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 | 0.051 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".