Celebrating Impactful Research in (JFEA): A Spotlight on Demand Forecasting with Big Data and Fuzzy Logic
At REA Publishing, we take pride in highlighting exceptional academic contributions that push the boundaries of knowledge and practice. As part of our ongoing focus on impactful research in JFEA, today we’re excited to spotlight one of the most-read and most-cited articles in the Journal of Fuzzy Extension and Applications (JFEA) — a Scopus-indexed, Q1-ranked journal specializing in fuzzy systems and smart technologies.
The featured article:
Big Data and Fuzzy Logic for Demand Forecasting in Supply Chain Management: A Data-Driven Approach
By: Subramanian B., Mishra A., Bharathi V. R., Mandala G., Kathamuthu N. D., Srithar S.
This work has gained rapid traction within the research community, with high citation counts and engagement metrics just weeks after publication. As part of our ongoing effort to elevate and promote high-impact studies across our journal ecosystem, we are pleased to give this paper the recognition it deserves.
A Closer Look at the Research
Demand forecasting in supply chains is a complex, high-stakes challenge—especially in today’s volatile global markets. Traditional forecasting models often fall short when confronted with ambiguity, incomplete data, or sudden shifts in consumer behavior.
This study, as part of the impactful research in JFEA, introduces a novel, hybrid framework that integrates big data analytics with fuzzy logic methodologies. The goal: to improve forecast accuracy while maintaining adaptability in real-world supply chain environments.
By leveraging large, real-time datasets and fuzzy inference systems, the authors have designed a model that not only captures trends but also accounts for uncertainty and variability in demand patterns. This blend of quantitative precision and flexible logic allows organizations to respond faster and smarter—leading to fewer stockouts, lower waste, and improved customer satisfaction.
Why It Matters
This research stands at the intersection of data science, logistics, and computational intelligence—an increasingly vital nexus as industries evolve toward automation and intelligent decision-making.
Some key reasons this paper has drawn attention:
- 🔹 Practical value: The model can be implemented across various supply chain settings, from retail to manufacturing.
- 🔹 Innovative methodology: The fusion of big data with fuzzy logic represents a fresh approach to managing uncertainty.
- 🔹 Scientific rigor: The work is grounded in solid statistical validation and real-world use cases.
In the broader context of smart applications and intelligent systems, this article exemplifies the type of cross-disciplinary innovation that JFEA aims to publish and promote.
About the Journal: JFEA
The Journal of Fuzzy Extension and Applications (JFEA) is an international, open-access journal dedicated to publishing cutting-edge research in fuzzy systems, decision science, AI applications, and related fields. Key highlights:
- ✅ Indexed in: Scopus
- ✅ Ranked: Q1 in its subject category
- ✅ Publisher: REA Publishing
- ✅ Access model: 100% open access
- ✅ Focus areas: Fuzzy logic, soft computing, smart applications, decision-making under uncertainty
Access the Article
This featured article appears in:
📚 JFEA – Volume 6, Issue 2 (June 2025)
Read the article via REA Press Journals (replace with actual article link when available)
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