The weather report for the supply chain: a longitudinal analysis of the ISM/Forrester Research Reports on Technology in Supply Management, 2001‐2003
Bibliographic record
Abstract
This article presents an analysis of three years results from the quarterly Report on Technology in Supply Management, conducted through a joint effort of the Institute for Supply Management (ISM) (formerly the National Association of Purchasing Managers) and Forrester Research. This report provides the best snapshot on the growth of e‐procurement in the United States. However, the sponsors do not publicly provide any analysis on the trends the data show from quarter‐to‐quarter. Now, with three years of available data from the twelve quarterly surveys conducted to date, there is an opportunity to analyze the adoption rates of e‐procurement tools, techniques and protocols in the American marketplace. The author of this study has conducted just such a longitudinal analysis of the ISM/Forrester data, examining the trends for organizations across the U.S. marketplace. What is demonstrated is that overall, both in manufacturing and service‐oriented firms and in large and small purchasing organizations, e‐procurement methods are rising and reaching “critical mass” in most areas with the “e‐way” fast becoming “the way”. However, important differences due exist between the groups and their specific needs, motivations, and results in their shift to an electronic acquisition environment. These are highlighted and discussed in this article.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".