AI-EcoSCM: A Lean and Agile Framework for Economic Excellence

Imene Rahal and Zayed Khalifa

MPRA Paper from University Library of Munich, Germany

Abstract: This paper explores the emerging framework of AI-driven Economic Supply Chain Management (EcoSCM), which adapts industrial supply chain optimization and artificial intelligence (AI) principles to the management of economic systems and institutions. EcoSCM emphasizes the systematic coordination of financial, administrative, and technological processes to deliver efficient, adaptive, and data-driven economic ecosystems. The study highlights how Lean principles (waste elimination, continuous improvement) enhance operational efficiency, while Agile principles promote flexibility and responsiveness to rapidly changing market and policy environments. In addition, AI integration—through predictive modeling, data analytics, and intelligent automation—provides real-time insights, informed decision-making, and enhanced economic intelligence. The paper proposes Lean, Agile, and AI Scoring Models to evaluate institutional maturity across efficiency, adaptability, and intelligence dimensions. By aligning Lean efficiency, Agile flexibility, and AI-driven intelligence, EcoSCM establishes a Smart Leagile paradigm capable of sustaining innovation, quality assurance, and digital transformation in the economic sector.

Keywords: Economic Supply Chain Management (EcoSCM); Lean–Agile Economy; Artificial Intelligence (AI); Smart Economics; Sustainable Growth.; Economic Supply Chain Management (EcoSCM); Lean–Agile Economy; Artificial Intelligence (AI); Smart Economics; Sustainable Growth. (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025-09-15, Revised 2025-10-15
References: Add references at CitEc
Citations:

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/126740/1/MPRA_paper_126740.pdf original version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:126740

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().