Bibliographic record
Abstract
Purpose The purpose of this paper is to report results of a survey in support of re‐designing the Purchasing Management Association of Canada (PMAC) professional accreditation program, the Certified Professional Purchaser (CPP). Design/methodology/approach A questionnaire was developed, embedded in e‐mail messages via hyperlink, and transmitted to the PMAC membership. The questionnaire included 54 topics, tools and techniques for supply chain management (SCM). Over 2,000 PMAC members commented on CPP program design, by returning the questionnaire electronically. Data analysis culminated with a principal components analysis of the 54 items, from which seven distinct components emerged. Findings The paper finds that PMAC members lack a common view of SCM. While 63 percent have adopted a broad SCM perspective; 37 percent have a more narrow perspective. However, the most important topics for supply chain professionals are robust across the perspectives. These topics pertain to general managerial skills (e.g. communication, leadership and relationship building); rather than specific functional or analytical tools and techniques. Research limitations/implications While the current study focuses exclusively on Canadian supply chain professionals, it would be very interesting and worthwhile to expand this research to other geographic locations. Practical implications A renewed CPP program, taken out of the somewhat narrow and tactically‐oriented purchasing area – and into the broader and more strategic SCM space, seems to be in order. The survey provides valuable information to support design and development of this new program. Originality/value Based on electronic survey returns from more than 2,000 supply chain professionals, insights are gained into various perspectives on SCM, along with important knowledge (topics, tools and techniques) for effective SCM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".