Bibliographic record
Abstract
Abstract Over the past decade, electronic commerce (e‐commerce) has started to revolutionize business processes and transactions. The emergence of user‐friendly browsers for the World Wide Web (WWW), and consequently the increased viability and usage of the Internet, has influenced top executives to initiate business‐to‐business (B2B) e‐commerce and supply chain management (SCM) technology solutions. One aspect of e‐business and SCM is electronic procurement (e‐procurement) that currently entails purchasing items and services through the Internet. Substantial cost savings may easily be realized in both the public sector (government entities and public schools) and the private sector (hospitals and nonprofit organizations). E‐procurement continues to evolve quickly as enterprise resource planning (ERP) vendors either partner with software procurement specialists or develop new procurement modules for the already existing ERP software. Today's e‐procurement solutions are replacing electronic data interchange (EDI) and closing the gap on division of purchasing. Indirect and direct procurement are slowly becoming a single entity therefore causing operating resource management (ORM) as well as maintenance, repair, and operations (MRO) purchasing processes to merge. The evolution of technology has allowed procurement to be a straight‐through process from the time a person browses a catalog until the invoice is paid and all information is stored for future use. This core of this chapter will focus on the strategic aspects of electronic procurement from a management viewpoint. It will concentrate on business‐to‐business purchasing, the e‐procurement decision, and the benefits and challenges of an e‐procurement solution. It touches on some of the technical detail and difficulties in e‐procurement but is not a “deep technical” document on choosing or implementing e‐procurement solutions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.287 | 0.136 |
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".