Robust Supply Chain Design for Highly-Customized Manufacturing
Bibliographic record
Abstract
Customers in today's evolving markets are seeking options that best suit their specific needs; consequently, the demand for highly-customized and personalized products has been growing steadily. In order to facilitate customized manufacturing, the structure of the underlying supply chain (SC) needs to be enhanced in terms of flexibility and resilience. In this study, we consider an SC comprising a main manufacturer that produces custom-designed modularstructured products, featured with different design complexity levels. The products have a bill-of-material (BOM) that can be altered in terms of the design of a subset of sub-assemblies and components. It is further assumed that the company collaborates with a group of manufacturers and part suppliers (upstream SC entities), differentiated in terms of capacity, technological capabilities, and cost, along with a group of logistics carriers (downstream entities) distinguished in the sense of their cost and lead time. In other words, in addition to involving customers in the product design, the company also provides different modes of product delivery (e.g., fast, regular, and slow) offered by different logistics carriers. We also incorporate the uncertainty involved in the manufacturing of subassemblies and parts featured with complex designs. More specifically, to reflect the amount of effort required to manufacture complex designs, the production and procurement costs of (highly or moderately complex) items are considered as piece-wise linear functions of their order quantity. Furthermore, the manufacturing capability of upstream entities for producing complex items are assumed uncertain, and modeled as scenarios. More precisely, under some scenarios, the producers/suppliers will not be capable of fulfilling the order of highly/moderately complex items within the promised production lead time due to technological limitations.
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 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) | 0.000 | 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 teacher head, 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".