Concurrent Design of Product Modules Structure and Global Supply Chain Configuration
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Bibliographic record
Abstract
In globally distributed supply chains, the classical logistics decisions of facilities location, sourcing and distribution are greatly influenced by political and economic factors. The fierce competition, fluctuations in currency values, intellectual property considerations and international trade agreements, tariffs and laws and government's tax incentives all have a major impact on decisions made by manufactures regarding where to design, produce, assemble and market their products. The need to satisfy varying customers' demands gave rise to increased flexibility not only in manufacturing systems but also in the product structure through modularity and platform concepts. Mass customization and postponing or moving products differentiation as close as possible to point-of-sale, if applied carefully, can be very beneficial. The protection of intellectual properties and trade-secrets play a role in deciding how a product is broken down into modules, what is contained in each module and where it would be produced. Variations in the currency exchange rates require careful attention particularly in globally distributed supply chains. Since one of the major criteria for making strategic decisions in supply chain is the overall allocation costs (production, inventory, transportation), they should be calculated considering the in-site currency exchange rate forecasts. As shown in Figure Therefore, it is important to consider those currency trends and exchange rates where suppliers, Manufacturers and markets are located. Responsiveness and agility are becoming important competitive attributes in addition to quality, variety and price. This leads to employing the concept of 3-dimensional concurrent engineering (3D-CE), as a step beyond design for supply chain and concurrent engineering. This concept was first discussed by Fine (1998) to understand and coordinate the interdependencies among product and process design and supply chain decisions, to maximize the operational and supply chain performance. Since it is the product design that determines which materials, components, and finished products should flow through the 1 These information are provided from http://www.forecasts.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it