Empirical Investigation on Fuzzy-Supported HRM for Supply Chain Management Concerns

DOI

10.2991/978-2-38476-559-1_23How to use a DOI?

Keywords

Fuzzy logic; Human Resource Management; Supply Chain Management; Decision support; Organizational agility

Abstract

In an increasingly dynamic and complex global market, the integration of Human Resource Management (HRM) with Supply Chain Management (SCM) has emerged as a strategic imperative. However, uncertainties in human-centered operations present persistent challenges. This study investigates the viability of Fuzzy-Supported HRM (F-HRM) as a solution for enhancing SCM effectiveness. The research is guided by the problem of fragmented HRM-SCM alignment, particularly in volatile environments where human judgment, resource allocation, and responsiveness are difficult to quantify. The objective is to develop a conceptual framework for integrating fuzzy logic into HRM functions to improve supply chain responsiveness, cost-efficiency, and innovation. Relying on qualitative secondary sources—including peer-reviewed journals, academic books, conference proceedings, and policy documents—the study synthesizes existing knowledge across HR, SCM, and artificial intelligence. Findings suggest that F-HRM improves decision-making accuracy, aligns talent management with operational goals, and supports real-time adaptability in supply chains. The paper recommends that organizations adopt intelligent HR dashboards and cross-functional training modules, while researchers pursue quantitative validation. The study concludes that F-HRM is a viable pathway to strategic agility in SCM. Limitations include the absence of primary data and sector-specific simulations.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - R. Sundara Boopathi
AU  - Shanmugam Sundararajan
PY  - 2026
DA  - 2026/04/19
TI  - Empirical Investigation on Fuzzy-Supported HRM for Supply Chain Management Concerns
BT  - Proceedings of the Global Innovation and Technology Summit “AAROHAN 3.0”_HSS track (GITS-HSS 2025)
PB  - Atlantis Press
SP  - 328
EP  - 345
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-559-1_23
DO  - 10.2991/978-2-38476-559-1_23
ID  - Boopathi2026
ER  -