Benchmarking supply chain management practices in a South African confectionery manufacturing organisation
Bibliographic record
Abstract
Background: In an increasingly competitive business world, businesses need to be able to measure the effectiveness of their supply chain management process practices against proven best practice frameworks. A number of these frameworks exist internationally but have to be used within the context of knowing the relative strengths and weaknesses of potential benchmarking frameworks. Two such frameworks were identified in the research and a case was made to use one such framework, the Global Supply Chain Forum (GSCF) framework, to measure the effectiveness of the supply chain practices of a leading confectionery manufacturing company in South Africa.Objective of the research: The purpose of the research was to identify an international best practice framework, which could be used by South African manufacturing organisations to benchmark their supply chain management (SCM) practices.Methodology: The methodology followed was a literature review of the existing SCM frameworks to identify a framework, which would be the most suited to the objective of the study, followed by a case study of a leading manufacturing organisation’s SCM practices benchmarked against those found in the framework.Results and conclusions: The main finding of the case study was that there is a high degree of adherence between the case study organisation’s SCM practices and those found in the SCM framework. There was also generally a high level of importance ascribed by respondents to the best practices contained by the GSCF framework. It was therefore concluded that the GSCF framework proved to be a useful instrument for a comprehensive analysis of supply chain management processes and practices for a manufacturer in the fast moving consumer goods industry, with potential for applications by organisations in the supply chains of other industries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".