Is Machine Learning Revolutionizing Supply Chain?
Bibliographic record
Abstract
The current supply chain ecosystem benefits from a great dynamic: the digitalization of companies and exchanges. For all the players in the sector, this is a real revolution, and machine learning is at the heart of this revolution. It has radically transformed companies: the evolution of communication media, the automation of many processes, the growing importance of information systems, etc. However, this fundamental transformation of work environments and organizational modes is far from over. In the current economic context of globalization of trade and increased competition, the greatest attention is focused on the objective of continuously reducing cost prices. Optimization requires efforts from all links in the supply chain to ensure very fine management. In this context, machine learning and the data on which it is based, is a real opportunity. In more recent years, a series of practical supply chain applications of machine learning (ML) have been introduced. By interconnecting the ML methods applied to the SC, the document indicates current SC applications and visualizes potential research gaps. In this article, we examine the applicability of machine learning techniques to the supply chain. The main objective of this paper is therefore to study how Machine Learning can be integrated into the range of tools available to Supply Chain decision-makers to take advantage of the increase in the volume of available data, through these tools particularly adapted to this type of processing.
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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.023 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".