Data-Driven Identification of Industrial Localization Opportunities in Saudi Arabia: A Big Data Framework for Vision 2030

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@article{1723181,
      author = {Syed Mohammad Ali},
      title = {Data-Driven Identification of Industrial Localization Opportunities in Saudi Arabia: A Big Data Framework for Vision 2030},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {3},
      pages = {2545-2560},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723181.pdf},
      abstract = {Saudi Arabia is undertaking a large-scale economic and industrial transformation through Vision 2030, with industrial localization, local-content development, supply-chain resilience and non-oil growth forming important components of the national agenda. Yet localization decisions are often complicated by fragmented information across customs records, industrial production, procurement, company registries, supplier capabilities, technology requirements and future investment pipelines. This paper develops a data-driven framework for identifying and prioritizing industrial localization opportunities in Saudi Arabia through the integration of big data analytics, trade intelligence, demand modelling, supplier-ecosystem assessment, value-chain analysis and multi-criteria decision support. The study adopts a structured review and conceptual-framework methodology and synthesizes recent literature with official Saudi policy and trade evidence. Six analytical dimensions are proposed: import dependency, market attractiveness, supplier capability, value-chain localization potential, strategic importance and economic impact. These dimensions are combined through a transparent Localization Opportunity Index (LOI), which can be applied at product, industry or value-chain level. The paper further outlines a practical data architecture incorporating data engineering, SQL/Python analytics, business-intelligence dashboards, forecasting, machine learning and network analysis. The framework demonstrates why import value alone is an insufficient basis for localization decisions and shows how product-level, supplier-level and value-chain-level analytics can improve industrial prioritization. For Saudi Arabia, the approach can support more evidence-based investment attraction, local-content development, industrial resilience, SME participation and technology transfer under Vision 2030. The paper concludes with implementation, governance and research recommendations for building a continuously updated localization decision-support capability.},
      keywords = {Saudi Vision 2030; industrial localization; big data analytics; local content; supply chain; import dependency; supplier ecosystem; value-chain analysis; industrial policy; data-driven decision-making.},
      month = {September},
  }