IJSMT Journal

International Journal of Science, Strategic Management and Technology

An International, Peer-Reviewed, Open Access Scholarly Journal Indexed in recognized academic databases · DOI via Crossref The journal adheres to established scholarly publishing, peer-review, and research ethics guidelines set by the UGC

ISSN: 3108-1762 (Online)
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A STUDY ON DEMAND ANALYSIS AND FORECASTING AND ITS IMPACT ON INVENTORY MANAGEMENT IN SUPPLY CHAIN LOGISTICS

AUTHORS:
Vaibhav gupta
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CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract

Modern supply chains operate within an increasingly globalized and unpredictable marketplace where the margin for error is razor-thin. Demand Analysis and Forecasting have emerged as the backbone of strategic and operational success, serving as the primary link between market requirements and logistical execution. The ability to anticipate customer needs with high precision is fundamental to maintaining a lean supply chain, as it directly governs the most critical aspects of business operations: procurement, production scheduling, and inventory optimization.

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gupta, V. (2026). A Study on Demand Analysis and Forecasting and its Impact on Inventory Management in Supply Chain Logistics. International Journal of Science, Strategic Management and Technology, 02(04). https://doi.org/10.55041/ijsmt.v2i4.010

gupta, Vaibhav. "A Study on Demand Analysis and Forecasting and its Impact on Inventory Management in Supply Chain Logistics." International Journal of Science, Strategic Management and Technology, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i4.010.

gupta, Vaibhav. "A Study on Demand Analysis and Forecasting and its Impact on Inventory Management in Supply Chain Logistics." International Journal of Science, Strategic Management and Technology 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i4.010.

References
1.Borghesi, G. (2023). The Role of Cobots in Warehouse Operations: A Study on Human-Robot Collaboration. International Journal of Automation and Logistics, 15(2), 112–128.

2.Chen, X., Zhao, Y., & Miller, J. (2024). Blockchain for Operational Transparency: Reducing Lead Times in Pharmaceutical Supply Chains. Journal of Business

3.Logistics, 45(1), 54–71.

4.Choi, T. M. (2025). Big Data Analytics in Operations Management: A Review of AI-Driven Demand Sensing in Retail. International Journal of Production

5.Economics, 268, 109–124.

6.Chopra, S., & Meindl, P. (2024). Supply Chain Management: Strategy, Planning, and Operation (8th ed.). Pearson Education.

7.Christopher, M. (2025). Logistics & Supply Chain Management: Creating Value-Adding Networks (6th ed.). Financial Times Publishing.

8.Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). SAGE Publications.

9.Gartner. (2024). Top Strategic Technology Trends in Supply Chain for 2024. Gartner Research Reports.

10.Hyndman, R. J., & Athanasopoulos, G. (2026). Forecasting: Principles and Practice (4th ed.). OTexts.

 
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This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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