Managing and Controlling Public Sector Supply Chains
Bibliographic record
Abstract
. For example, countries such as the UK, US and Canada have for long employed SCM in the management of their procurement and logistics (OCG, 2005) as well as South Africa (Ambe, 2009) among others. Despite the interest and employment of SCM in public institutions, Humphries and Wilding (2004) assert that much has not been done compared to the private sector. According to Notwithstanding this, many professional government organizations have indicated that SCM could hold great promise in enhancing public procurement systems. However, Essig & Dorobek (2006:1) argue that the management of public supply chain raises various research questions that need to be answered. The chapter explore the concept of supply chain management in the public sector. The chapter utilises a case study of the SCM in the South African public sector to differentiate between public versus private sectors supply chains. It presents the critical components, features and importance of public sector supply chains. Furthermore, the chapter portray the need for supply chain improvement and the employment of performance measures in the public sector. A balanced scorecard as a supply chain performance indicator is suggested for application to the public sector supply chain. The chapter contributes to literature on the application of public sector supply chains.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".