---
title: "Supply Chain Analytics: 4 recent papers (last 30 days)"
description: "Supply Chain Analytics published 4 papers in the last 30 days (2026-09-28 to 2026-10-27), ranking #184 of 339 journals."
url: https://scholar4ai.com/journals/supply-chain-analytics
markdown_url: https://scholar4ai.com/journals/supply-chain-analytics.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "Supply Chain Analytics published 4 papers in the last 30 days (2026-09-28 to 2026-10-27), ranking #184 of 339 journals."
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-10-27T14:11:44+09:00
window: 2026-09-28 to 2026-10-27
source: OpenAlex (CC0)
---

# Supply Chain Analytics

- 0 papers in the last 7 days
- category: Operations Research
- publisher: Elsevier BV
- ISSN 2949-8635

Back to the ranking: https://scholar4ai.com/rankings/journals.md

## Key findings

- Supply Chain Analytics published 4 papers in the last 30 days (2026-09-28 to 2026-10-27), ranking #184 of 339 journals.
- Leading institutions: BMW (Germany) (1), BMW Group (Germany) (1) and Constructor University (1).
- Countries: Germany (1), Iran (1) and Sweden (1).
- From Oct 2023 to Sep 2026, Supply Chain Analytics published 197 papers (5 a month on average, by publication date in OpenAlex).
- Its most frequent keywords over that period were “supply chain resilience” (34), “supply chain sustainability” (24) and “blockchain” (16).
- Its most-cited paper of the period is “A hybrid multi-criteria decision-making and machine learning approach for explainable supplier selection” (2024-06-29), with 84 citations.

## The last 36 months

*Papers by publication date, Oct 2023 to Sep 2026*

| Oct 23–Sep 24 | Oct 24–Sep 25 | Oct 25–Sep 26 | 36 months |
|---:|---:|---:|---:|
| 40 | 79 | 78 | 197 |

*Most frequent keywords, Oct 2023 to Sep 2026*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | supply chain resilience | 34 |
| 2 | supply chain sustainability | 24 |
| 3 | blockchain | 16 |
| 4 | [artificial intelligence](https://scholar4ai.com/keywords/artificial-intelligence.md) | 15 |
| 5 | supply chain management | 15 |
| 6 | Internet of Things | 14 |
| 7 | circular economy | 13 |
| 8 | demand forecasting | 13 |
| 9 | multi-objective optimization | 13 |
| 10 | inventory management | 12 |

*Most-cited papers published Oct 2023 to Sep 2026*

| # | Citations | Per month | Title | Journal | Published | Authors |
|---:|---:|---:|---|---|---|---|
| 1 | 84 | 3.0 | [A hybrid multi-criteria decision-making and machine learning approach for explainable supplier selection](https://doi.org/10.1016/j.sca.2024.100074) | [Supply Chain Analytics](https://scholar4ai.com/journals/supply-chain-analytics.md) | 2024-06-29 | Ahmad Abdulla, George Baryannis |
| 2 | 74 | 2.7 | [Unlocking the potential of digital twins in supply chains: A systematic review](https://doi.org/10.1016/j.sca.2024.100075) | [Supply Chain Analytics](https://scholar4ai.com/journals/supply-chain-analytics.md) | 2024-07-29 | Syed Adeel Haneef Zaidi, Sharfuddin Ahmed Khan, Amin Chaabane |
| 3 | 57 | 1.8 | [A structural equation modeling framework for exploring the industry 5.0 and sustainable supply chain determinants](https://doi.org/10.1016/j.sca.2024.100060) | [Supply Chain Analytics](https://scholar4ai.com/journals/supply-chain-analytics.md) | 2024-02-23 | Md. Asfaq Jamil, Ridwan Mustofa, Niamat Ullah Ibne Hossain et al. |
| 4 | 47 | 1.8 | [An explainable artificial intelligence model for predictive maintenance and spare parts optimization](https://doi.org/10.1016/j.sca.2024.100078) | [Supply Chain Analytics](https://scholar4ai.com/journals/supply-chain-analytics.md) | 2024-08-26 | Ufuk Dereci, Gülfem Tuzkaya |
| 5 | 47 | 2.0 | [A conceptual digital twin framework for supply chain recovery and resilience](https://doi.org/10.1016/j.sca.2024.100091) | [Supply Chain Analytics](https://scholar4ai.com/journals/supply-chain-analytics.md) | 2024-11-19 | Oluwagbenga Victor Ogunsoto, Jessica Olivares-Aguila, Waguih H. ElMaraghy |

*Papers, newest first.*

| Date | Title | Journal | Authors | Affiliations |
|---|---|---|---|---|
| 2026-10-15 | [A causal analytics approach for interpretable supply chain risk and supplier performance evaluation](https://doi.org/10.1016/j.sca.2026.100248) | [Supply Chain Analytics](https://scholar4ai.com/journals/supply-chain-analytics.md) | Ishwar Gupta, Harshitha Konduru, Sergio Correa et al. | BMW (Germany); BMW Group (Germany); Constructor University |
| 2026-10-07 | [A hybrid analytics framework for prioritizing artificial intelligence risks in circular digital supply chains](https://doi.org/10.1016/j.sca.2026.100247) | [Supply Chain Analytics](https://scholar4ai.com/journals/supply-chain-analytics.md) | Mohit Sharma, Mohit Tyagi, R S Walia |  |
| 2026-10-01 | [A Stackelberg Game Analytics Model for Pricing and Coordination in a Three-Channel Renewable Microgrid Supply Chain](https://doi.org/10.1016/j.sca.2026.100250) | [Supply Chain Analytics](https://scholar4ai.com/journals/supply-chain-analytics.md) | Mohammad Amin Maftoon, Razieh Keshavarzfard, Narges Norouzi | Islamic Azad University, Tehran |
| 2026-10-01 | [Predictive and Prescriptive Analytics for Net-Zero Transitions in Supply Chains Editorial](https://doi.org/10.1016/j.sca.2026.100249) | [Supply Chain Analytics](https://scholar4ai.com/journals/supply-chain-analytics.md) | Shahryar Sorooshian, Elmira Parviziomran | University of Gothenburg |

## Cite this page

Scholar4AI. (2026). Supply Chain Analytics. Journal Research Rankings. Retrieved October 27, 2026, from https://scholar4ai.com/journals/supply-chain-analytics

```bibtex
@misc{scholar4ai-journals-supply-chain-analytics,
  author = {{Scholar4AI}},
  title = {Supply Chain Analytics},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/journals/supply-chain-analytics}},
  urldate = {2026-10-27},
  note = {Journal Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).