Benchmarking criteria for evaluating third-party logistics providers in South Africa
Bibliographic record
Abstract
Background: In South Africa, deemed the ‘gateway to Africa’, there is limited evidence of the existence of a survey ranking third-party logistics providers (3PLs). This lack of comparative information of the major 3PLs based on key outsourcing and ranking criteria complicates the selection process for companies that intend to contract 3PLs.Objective: The purpose of this article was to determine the critical selection and ranking criteria for the creation of an index to evaluate 3PLs in South Africa for developing a 3PL benchmarking index.Method: Survey data were collected from 103 of the Top 500 Companies in Africa that use 3PLs and operate within the sectors that mostly outsource logistics services in South Africa. A factor analysis method was employed.Results: Three factors for 3PLs selection converged: service quality, information management and compliance, and collaboration. The top three ranked categories for 3PLs selection are cost and price structure, service delivery and the relationship with the 3PL provider. Most respondents (90%) confirmed a need for a 3PLs index in South Africa.Conclusion: The results help managers with the strategic selection of 3PLs as these critical logistics outsourcing selection criteria can be used to evaluate and rank 3PLs prior to contracting. Based on the selection criteria for logistics outsourcing identified and ranked in this article, a ranking index for 3PLs in South Africa can be developed.
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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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".