Cross-departmental collaboration in strategic sourcing as a catalyst for supplier development: The case of Eskom
Bibliographic record
Abstract
Objective: The objective was to analyse the integration of and collaboration between strategic sourcing and supplier development at Eskom, South Africa’s primary electricity supplier, and to determine how strategic sourcing can be a catalyst for supplier development.Problem investigated: To address fragmented and inefficient procurement, Eskom instituted two departments, Commodity Sourcing (CS), to drive strategic sourcing, and Supplier Development and Localisation (SD&L), to drive supplier development. The problem is that collaboration between CS and SD&L has not materialised and thus their mandates have not been entirely achieved.Research design: A case study research design was employed, drawing from multiple sources of data to triangulate findings. Managers from two departments, CS and SD&L, were separately surveyed, while face-to-face interviews were conducted with executive management.Results: The findings revealed a lack of planning, implementation and monitoring of supplier development in the strategic sourcing process of CS. Although the procurement spend in CS is used to drive supplier development objectives, from the perspective of SD&L, in practice this does not fully materialise.Originality and/or value of the research: The study is unique as it focuses on cross-departmental collaboration within the organisation, rather than collaboration across the supply chain with external partners. From insights into the dysfunctional relationship between the departments, solutions for increased collaboration and effective supplier development could be suggested.Conclusion: For strategic sourcing to be a catalyst of supplier development, it is essential that an integrated strategic sourcing operating model incorporating the objectives of both CS and SD&L 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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".