Supply chain management: a comparison of Scandinavian and American perspectives
Bibliographic record
Abstract
Purpose The purpose of this paper is to advance understanding of international differences in supply chain management (SCM) perspectives and practices, by comparing perceptions of Scandinavian and American supply chain managers. Design/methodology/approach The survey focused on the definition of SCM, along with facilitators of and barriers to SCM implementation. A four‐page questionnaire was designed and sent by mail. After follow‐ups, 23 Scandinavians and 104 Americans returned completed questionnaires. Findings While many similarities were found between Scandinavians and Americans, several differences were also identified. Both groups have adopted broad, multiple function perspectives on SCM; and both groups perceive SCM implementation to be slower and more difficult than expected. Two differences are the Americans' greater concern about incompatible systems and implementation costs as barriers to SCM, compared to the Scandinavians. Research limitations/implications The study is based on relatively small samples, of limited functional (logistics) and geographic (Scandinavia and America) scope. Future research should expand the functional focus into purchasing, operations, and marketing; and the geographic coverage to other parts of the world. Practical implications Internal resistance is more of a barrier than external (customer or supplier) resistance to SCM. Thus, organizations should focus first on internal (functional) integration, and then move onto inter‐organizational integration. However, employees working with customers and suppliers should use these external relationships to inspire closer internal relationships. Further, people are more critical than technology in implementing SCM. Organizations should get the right people in place first, and then think about technology. Originality/value There is little empirical research on SCM implementation. Practitioners and researchers should find value in this unique comparative study.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".