Tool for Purchasing Positioning – Assessment of Purchasing Maturity and Performance Level
Bibliographic record
Abstract
This article provides a practical approach to evaluate the performance level of a purchasing organization before (ex ante) and after (ex post) the execution of a cost optimization project.A purchasing organization is often questioned, when performance (output) is not provided at the expected level (for example poor EBIT contribution). Provided there is a connection between performance level and the positioning of a purchasing organization, the interesting questions are: which aspects influence the performance level (output) and subsequently how can the effectiveness (input) of a purchasing organization be measured.The aim of this paper is to examine an applicable positioning tool, that helps to analyse and assess performance level and supports the selection of appropriate and tangible measures for sustainable cost optimization as an integrated tool of a cost optimization approach in the analysis phase.The utilized methodology considers an internet and literature research of typical evaluation methods and approaches, authors observation and synthesis of knowledge.Based on the research and subsequently logical reasoning a positioning tool is derived. Initially two formulated hypotheses are tested and confirmed through the application of the tool in three executed cost optimization projects and finally validated by semi-structured interviews with six purchasing experts.More accurate and precise evaluation of a purchasing organization prior to execution of a cost optimization project could lead to a better selection of cost optimization approaches and can help to reduce waste of time, capacity and expenditures. Assessment after project closure help to demonstrate the lift in performance.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".