Use Case Decomposition for Multi-Standard Management to Enable Flexible Supply Chain
Bibliographic record
Abstract
Abstract Flexibility is identified as a mandatory characteristic of today’s supply chains. There are a few aspects of supply chain flexibility identified in the literature, and one of them is flexible supply chain integration. The standard for exchanging supply chain data plays a crucial role in that aspect. Today, business systems operate in different geopolitical, business process, and industry settings, and there is a great chance that they are using different Data Exchange Standards (DESes). Various approaches to deal with this heterogeneity exist; however, they can be exponentially time-consuming and expensive or may not meet desirable characteristics such as security constraints and varying use cases. In our recent paper, we proposed a novel approach based on the Core Component Specification (CCS) and its concept of the Business Context. Its central idea is the super (or federated) data model that gets updated as relevant DESes and enterprise information objects grow in the enterprise integration ecosystem. It provides a semantic layer to link different DESes. Along the super data model, two additional models were employed – the staging data model that enables the import of any type of data structure definitions, and the library-specific data model that enables their representations in the CCS-like form. This paper brings further investigation into the complexity of various industry use cases in dealing with multiple standards to achieve flexible supply chain integration. Our research collaboration with industry partners found that the industry may need up to six use cases, but commercial tools typically address only one of these use cases, data mapping. This paper formalizes and illustrates these use cases using two DESes – connectSpec and QIF. While the main contribution is the use case identification and decomposition, which will make the industry’s needs more explicit, manageable, and automatable, the paper also outlines the user interfaces and backend needed to make the multi-standard management more productive. The future plan is the definition of automation, and the backend RESTful APIs that are needed to create user-friendly interfaces. The paper points out the issues that arose in the initial validation stage and improvements that need to be tackled in the future.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".