SC-Literature Intelligence: A Retrieval-Augmented Generation Framework for Multi-Category AI Literature Synthesis in Supply Chain

Authors

  • Setio Basuki Universitas Muhammadiyah Malang
  • Amelia Khoidir Universitas Muhammadiyah Malang
  • Muhammad Ilham Perdana Universitas Muhammadiyah Malang
  • Muhammad Daffa Nugraha Universitas Muhammadiyah Malang
  • Masatoshi Tsuchiya Toyohashi University of Technology

DOI:

10.33395/sinkron.v10i3.16477

Keywords:

retrieval-augmented generation, large language model, research synthesis, supply chain, embedding model, literature analysis

Abstract

This paper develops SC-Literature Intelligence, a retrieval-augmented generation (RAG) framework for research synthesis of scientific literature on artificial intelligence (AI) in the supply chain domain. The study addresses the fragmentation of scientific findings, which makes cross-document understanding difficult by supporting four categories of literature-analysis queries: trend analysis, gap detection, comparative synthesis, and evidence-based question answering (QA). The primary novelty lies in introducing a category-aware research synthesis framework capable of evaluating RAG performance across multiple literature-analysis tasks rather than conventional question answering. The framework is built from Scopus-indexed abstracts through pre-processing, chunk-based embedding using BGE-M3 and LaBSE, vector storage, semantic retrieval, and prompt-guided generation evaluated using the RAGAS framework across 640 experimental runs. The results show that BGE-M3 consistently outperforms LaBSE on all RAGAS indicators with the best configuration (chunk size 64, Top-K 5) achieving scores between 0.722 and 0.856 across faithfulness, answer relevancy, context precision, and context recall. Gap detection emerges as the best-supported query category, whereas comparative synthesis remains the most challenging. Failure analysis further reveals that retrieval-stage issues dominate over generation-stage issues, identifying embedding quality as the primary bottleneck. These findings demonstrate that category-aware RAG-based synthesis can support structured, evidence-grounded literature analysis in the supply chain AI domain.

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Basuki, S., Khoidir, A., Perdana, M. I., Nugraha, M. D., & Tsuchiya, M. (2026). SC-Literature Intelligence: A Retrieval-Augmented Generation Framework for Multi-Category AI Literature Synthesis in Supply Chain. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 10(3), 1422-1437. https://doi.org/10.33395/sinkron.v10i3.16477

Issue

Vol. 10 No. 3 (2026): Article Research July 2026

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Articles

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Copyright (c) 2026 Setio Basuki, Amelia Khoidir, Muhammad Ilham Perdana, Muhammad Daffa Nugraha, Masatoshi Tsuchiya

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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