強靭で持続可能なサプライチェーン構築における人工知能の役割：パキスタンの繊維・製造業への拡張を伴う複数ケーススタディ

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:AIとサステナビリティの関連を実証する枠組みと、新興国での適用条件を提供。

🏢実務担当者:サプライチェーンでのAI投資をどの層（技術・データ・ガバナンス）に集中すべきかの判断材料。

🏛政策担当者:新興国でのAI活用格差を是正するためのインフラ・スキル政策の重要性を示す。

Purpose This study examines how artificial intelligence (AI) contributes to building resilient and sustainable supply chains, investigating the mechanisms through which AI generates these outcomes and the organizational conditions that moderate their realization. The research extends its inquiry to Pakistan's textile and manufacturing sectors to assess how these mechanisms translate into resource-constrained emerging-market settings. Design/Methodology/Approach. A qualitative, exploratory multiple-case study design was used to conduct this study. Findings. The findings are striking in their consistency: optimization-driven AI cuts emissions or resource waste in 5 of 6 global cases, predictive analytics and digital twins compress disruption response time in half the sample, and the strongest performers achieve both outcomes at once, from a single system, rather than through separate initiatives. The Pakistan extension confirms the same mechanisms at the firm level, most vividly in a documented 30 percent reduction in material waste at a major textile exporter, but reveals a sharper constraint: infrastructure, energy, cost, and analytical-skills gaps concentrate these gains among a handful of well-resourced firms, with prescriptive analytics capability, not AI access alone, emerging as the true differentiator. Practical Implications. From these findings, the study builds a three-layer framework, technology, data and infrastructure, and governance, that reframes AI investment decisions around a single question: which layer is constraining your resilience and sustainability outcomes? The paper closes with concrete, sequenced recommendations for Pakistani practitioners and policymakers seeking to close that gap. Originality/Value. 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