Product Architecture and Supply Chain Management Design in Emerging Markets: A Case Study of Japanese Firms in Brazil
Bibliographic record
Abstract
No firm can control all areas of their supply chain. Product architecture is a crucial element of value analysis in supply chain management (SCM). In this article, we explore the following research questions: (1) What is the relationship between product architecture and SCM practices? (2) How can we apply this framework to business practices? To analyze our research topics, we will show a framework for relationship of product architecture and sourcing. To make our specific framework applicable to firms, we do case studies of Japanese firms in Brazil, comparing with Korean electronic firms in Brazil. This paper mainly examines the case of Japanese firms in Brazil from the standpoint of product architecture and SCM. With a relatively weak Brazilian domestic supplier network, not all component parts can be deployed there. Global supply chain management is useful for cost reduction for certain raw materials. Technologically, some parts need to be procured from outside of Japan. In particular, many component parts for motorcycle manufacturers require integral architecture in contrast to electronics products. Firm H is successful in SCM integration through helping Japanese suppliers relating to integral architecture to move into Brazil and at the same time raising the internal production ratio. Lessons and implications are briefly discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".