Revolution of South African public procurement in the Industry 4.0 era
Bibliographic record
Abstract
Background: Public procurement in South Africa is challenged by conventional methods that pave the way for human interference resulting in fraud and corruption, delays, unaccountability and poor performance of the value chain in the procurement process. Objectives: This study aimed to investigate the Industry 4.0 capabilities for public procurement improvement. To address the challenges presented by the traditional manual procurement systems, the study embarked on a transformative journey by identifying the prospects and benefits of Industry 4.0 technologies in public procurement in South Africa, and the significance and application thereof. Method: The study followed a six-step qualitative research methodology of content and thematic analysis which facilitated an understanding of the procurement process in South Africa and how it can be automated using Industry 4.0 technologies. Results: The study revealed that Industry 4.0 technologies are crucial as they present digitalisation opportunities through platforms such as e-design, e-inform, e-sourcing, e-evaluation and e-contract. The platform will improve the process, encourage legislation compliance and achieve its goals as outlined in the constitution and Public Finance Management Act of 1996. Conclusion: Implementing digital procurement will assist the government in achieving its policy requirements of value for money, open and effective competition, ethics and fair dealings, accountability and reporting, and equity. The technologies represent a strategic response to the challenges facing public procurement. Contribution: The study contributed to the body of knowledge by presenting the prospects and benefits of Industry 4.0 technologies. In addition, it highlighted the significance and application to the South African public sector.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".