EcoSupply: A machine learning framework for analyzing the impact of ecosystem on global supply chain dynamics

Vikas K. Garg, N. Viswanadham

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

1 Citation (Scopus)


A global supply chain spans several regions and countries across the globe. A tremendous spurt in the extent of globalization has necessitated the need for modeling global supply chains in place of the conventional supply chains. In this paper, we propose a framework, EcoSupply, to analyze the supply chain ecosystem in a probabilistic setting unlike the existing methodologies, which presume a deterministic context. EcoSupply keeps track of the previous observations in order to facilitate improved prediction about the influence of uncertainties in the ecosystem, and provides a coherent mathematical exposition to construe the new associations, among the different supply chain stakeholders, in place of the existing links. To the best of our knowledge, EcoSupply is the first machine learning based paradigm to incorporate the dynamics of global supply chains.

Original languageEnglish
Title of host publicationSimulated Evolution and Learning - 8th International Conference, SEAL 2010, Proceedings
Number of pages10
Publication statusPublished - 2010
MoE publication typeA4 Conference publication
EventInternational Conference on Simulated Evolution and Learning - Kanpur, India
Duration: 1 Dec 20104 Dec 2010
Conference number: 8

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6457 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Simulated Evolution and Learning
Abbreviated titleSEAL


  • Global Sourcing
  • Machine Learning
  • Supply Chains


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