Use of DMAIC to Elaborate a Proposal to Improve the Purchase Processes of the Material Department of the Federal University of Amazonas: A Study on Public Procurement Management
Bibliographic record
Abstract
The public sector is a dynamic system composed of a web of tools and models for administrative and accounting management. In the midst of a tangle of processes, public procurement emerges as an important mechanism for the management, movement and application of financial resources destined to serve society. The purpose of this article is to present a proposal for a Manual of Procedures and Guidelines (MPG), aimed at promoting improvements to the public procurement process that is under the responsibility of the Material Department of the Federal University of Amazonas. The study methodology was developed from a bibliographic, documentary, and observational research, based on the application and analysis of the DMAIC tool. The study methodology was developed from a bibliographic, documentary, and observational research, based on the application and analysis of the DMAIC tool. The study presented as a result a viable proposal for improving procedures through the elaboration of a manual of rules and procedures for the optimization of public procurement management carried out by the materials department. The study presented as a result a viable proposal for improving procedures through the elaboration of a manual of rules and procedures for the optimization of public procurement management carried out by the materials department. It was concluded that greater efficiency in public procurement management is able to reduce expenses, allows the systematization of procedures and reduces the processing time of the purchase processes in their different phases until the purchase and availability of the purchased item to the requester.
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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 | 0.021 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".