How can supply chain optimization and improvement be achieved in an automotive sector modular consortium?
Bibliographic record
Abstract
Purpose Anchored in transaction cost theory and the resource-based view, this study aims to present original insights from primary data on optimizing and improving supply chain management methods within a modular consortium in the automotive industry of an emerging economy (Brazil). Thus, this study explores aspects of supply chain firms and transactions that facilitate optimization and improvement in a modular consortium. Design/methodology/approach This study carried out a significant case study on a modular consortium from the automotive industry with a leading automotive assembler and ten of its leading suppliers in Brazil. This study interviewed the key stakeholders, such as the plant’s supply chain manager, managers of the leading automotive assembler and ten suppliers. This study performed the data analysis and coding using best practices for the selected content analysis method. Findings This study research presents several significant findings, such as: it elucidates the distinction between the concepts of optimization and improvement, demonstrating the specific techniques applied in the case study for each concept; it emphasizes the critical role of continuous improvement methodologies, particularly lean practices, within the context of a modular consortium; it reveals that the complexity of processes and the size of suppliers considerably impact the adoption and efficacy of optimization and improvement methods in such a consortium, offering valuable insights for supply chain managers in the automotive industry; and it provides a theoretical framework for managing suppliers based on their contribution, indicating that higher contributions warrant greater attention from the lead company. Research limitations/implications This paper indicates that the adoption and effectiveness of optimization and improvement programs in a modular consortium may depend on the supplier size and process complexity of supply chain transactions. Originality/value This study introduces a novel framework to evaluate the degree of supplier contribution to supply chain improvement and optimization. This framework is underpinned by two robust theoretical perspectives in transaction cost theory and the resource-based view, adding to the academic discourse on supply chain management in the automotive industry.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".