Elevating employee competency and engagement through the design, development and implementation of a procurement and supply chain academy
Bibliographic record
Abstract
As part of a five-year transformation plan, Bombardier Aviation procurement and supply chain identified an opportunity to address employee engagement, enablement and development opportunities through the design and implementation of a procurement and supply chain learning academy. After consulting with external entities, a team of cross-functional internal stakeholders assessed the needs of the procurement organisation and designed a structured plan to build and roll out the learning academy. Careful planning and design resulted in a robust curriculum to address the needs of all levels in the organisation. Many forms of training are provided through the academy, including classroom training led by both internal and external instructors and customisable online self-led courses. The success of the academy will rely on on-going support and prioritisation from the organisation’s management. The academy is in its relative infancy but initial internal feedback is positive. The results of the programme implementation will continue to be assessed over time. Plans are in place to continue to grow the programme by expanding the scope of its offerings. The benefits of attraction, engagement and retention are expected to be enjoyed by both the employees and the organisation for years to come. This paper intends to relay the process that was followed to innovate and implement the procurement academy at Bombardier Aviation and share the keys to success and lessons learned along the journey.
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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 | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".