{
    "@context": "https://schema.org",
    "@type": "ScholarlyArticle",
    "identifier": {
        "jsc_id": 34,
        "DOI": "10.5281/zenodo.23266305",
        "DOI_url": "https://doi.org/10.5281/zenodo.23266305"
    },
    "title": "Smart Supply Chain Management: A Comprehensive Framework Integrating Artificial Intelligence, Blockchain, and Big Data Analytics",
    "abstract": "Industry 4.0 technologies have changed how firms plan, execute, and monitor supply chain operations, increasing the need for frameworks that are simultaneously predictive, auditable, and analytically grounded. This paper proposes a Smart Supply Chain Management (SSCM) framework that combines artificial intelligence (AI), blockchain, and big data analytics. The architecture comprises three modules: an LSTM-based demand-forecasting engine, a smart-contract traceability platform, and a real-time analytics layer for operational decision-making. In a 24-month simulation of a multi-echelon network, the integrated design achieved 95.8% demand-forecast accuracy, reduced inventory holding costs by 31.2%, shortened the average lead time by 27.7%, and raised the composite transparency score by 141.5% relative to a conventional ERP baseline. Comparisons with AI-only and blockchain-only configurations indicate that the gains are complementary rather than interchangeable. The study contributes an empirically evaluated architecture and a set of implementation implications for firms deploying Industry 4.0 tools in multi-tier chains.",
    "authors": [
        {
            "sequence": "first",
            "given": "Amir",
            "family": "Amin",
            "name": "Amir Amin",
            "affiliation": [
                {
                    "name": "Department of industrial engineering,college of engineering , university of Tehran, Tehran, Iran"
                }
            ],
            "email": "Amir.amin@ut.ac.ir",
            "is_corresponding": true
        }
    ],
    "keywords": [
        "Smart Supply Chain Management Artificial Intelligence",
        "Blockchain Technology",
        "Big Data Analytics",
        "Industry 4.0"
    ],
    "section": "Research Article",
    "language": "en",
    "journal": {
        "name": "Journal of Smart Computing",
        "abbreviation": "JSC",
        "issn": "2979-8752",
        "publisher": "Center of Intelligent Operations and Technology Research, United Kingdom",
        "url": "https://ciotr.com/jsc/"
    },
    "issue": {
        "volume": 2,
        "number": 1,
        "year": 2026,
        "url": "https://ciotr.com/jsc/issue/8"
    },
    "pages": {
        "first": "53",
        "last": "64",
        "range": "53-64"
    },
    "dates": {
        "submitted": "2026-07-20",
        "published": "2026-06-07"
    },
    "urls": {
        "landing_page": "https://ciotr.com/jsc/article/34",
        "pdf": "https://ciotr.com/jsc/pdf/article-34.pdf",
        "metadata": "https://ciotr.com/jsc/article/34.json"
    },
    "license": {
        "name": "Creative Commons Attribution 4.0 International",
        "code": "CC BY 4.0",
        "url": "https://creativecommons.org/licenses/by/4.0/"
    },
    "citation": {
        "apa": "Amin, A. (2026). Smart Supply Chain Management: A Comprehensive Framework Integrating Artificial Intelligence, Blockchain, and Big Data Analytics. Journal of Smart Computing, 2(1), 53-64. https://doi.org/10.5281/zenodo.23266305"
    }
}