An objective evaluation of the Ivey Purchasing Managers Index
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide academic researchers and practitioners with a better understanding of the current Ivey Purchasing Managers Index (IPMI), with alternative IPMIs, and with their appropriateness as an indicator of the performance of the Canadian economy. Design/methodology/approach The paper makes use of principal component analysis to investigate the choice of principal variables for computing new IPMIs based on monthly data for five Ivey indexes for the period from December 2000 to May 2006. Statistical tests were made for the validity of the existing and new IPMIs using two major indicators of Canadian business and economic activities. Findings The results suggest that a new composite purchasing managers index for Canada similar to its US counterparts be computed based on four identified Ivey indexes. For constructing a simpler and parsimonious IPMI, the results support Ivey's current practice of using only one Ivey index, namely, the Purchases index. Research limitations/implications There was a limited amount of data used for the analysis (i.e. monthly data for less than six years). Also there are issues on data comparability between the Ivey data and the US data (i.e. the Ivey does not collect separate data for the manufacturing sector or the non‐manufacturing sector). Practical implications Using a composite index akin to the PMI, business organizations and policymakers will have an accurate sense of what is happening in the Canadian economy. Furthermore, enhancing the power and accuracy of such an index will benefit supply professionals, economic forecasters, and policy experts. Originality/value The present study offers additional insights to both practitioners and academics. It helps supply chain managers and practitioners come up with a more reliable business strategy by providing them with a weighted composite index for the Canadian economy. It also makes contributions to the academic community in the area of statistical theory applied to supply management as it has introduced the principal components variable selection analysis in the construction of a new IPMI.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".