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Supply Chain Analytics is a peer-reviewed journal published by Elsevier, covering Sustainable Supply Chain Management, Supply Chain Resilience and Risk Management, Supply Chain and Inventory Management.
| Publisher | Elsevier |
|---|---|
| Reference style | Numbered (Elsevier) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, C. Lee, A representative article title, Supply Chain Analytics 12 (2023) 45–58.
Formats any DOI in Supply Chain Analytics style. No sign-up. |
| Publishes research in | Sustainable Supply Chain Management Supply Chain Resilience and Risk Management Supply Chain and Inventory Management Quality and Supply Management Big Data and Business Intelligence |
| ISSN | 2949-8635 |
| Citation impact (2-yr) | 6.45 |
| h-index | 25 |
| i10-index | 68 |
| Total citations | 2,195 |
| Open access | Yes |
| Top institutions publishing here | University of Tehran |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
This paper presents a systematic review of the literature on Supply Chain Risk (SCR) research, focusing on content-based analysis. The study comprehensively examines the general factors associated with key themes and trends in supply chain risk management, encompassing the identification and assessment of risks, risk mitigation strategies, and the influence of emerging technologies on Supply…
Forecasting demand and determining safety stocks are key aspects of supply chain planning. Demand forecasting involves predicting future demand for a product or service using historical data and other external and internal drivers. Stockouts and excess production can be reduced by accurately forecasting demand. This allows companies to plan production, inventory, and logistics more effectively.…
Supplier selection is a difficult task imposing significant challenges for supply chain managers in today's competitive environment. Sustainability adds another layer of complexity to this already difficult problem, given the global concerns on social, economic, and environmental impacts, especially in emerging economies. Many multi-criteria decision-making (MCDM) methods have been proposed for sustainable supplier selection. However,…
Supplier selection has become increasingly complex regarding selection criteria caused by expanded data collection processes and supplier numbers due to globalisation effects. This complexity has led to the consideration of Artificial Intelligence (AI) techniques to facilitate and enhance supplier selection. However, the AI techniques most often applied are unfamiliar to stakeholders and have limited explainability,…
Sustainable Supply Chain and Industry 5.0 are two important concepts reshaping how businesses operate in the modern world. Together, these two concepts drive the advancement of a highly sustainable and robust worldwide economy. Companies are now becoming more sustainable in supply chain management, using technologies like blockchain and co-bots to track the origin of goods,…
