Systematic Literature Review AI pada WMS untuk Akurasi Stok dan Produktivitas Pergudangan

Wahyudin Ahmadi; Anton Sutriyana; Tita Puspita Sari; Muhammad Afrija Agung Nurpratama

doi:10.31004/riggs.v5i2.11818

Authors

Wahyudin Ahmadi Universitas Panca Sakti Bekasi
Anton Sutriyana Universitas Panca Sakti Bekasi
Tita Puspita Sari Universitas Panca Sakti Bekasi
Muhammad Afrija Agung Nurpratama Universitas Panca Sakti Bekasi

DOI:

https://doi.org/10.31004/riggs.v5i2.11818

Keywords:

Artificial Intelligence, Warehouse Management System, Akurasi Stok, Produktivitas Pergudangan, Systematic Literature Review

Abstract

Perkembangan teknologi Artificial Intelligence (AI) telah membawa transformasi signifikan dalam pengelolaan sistem pergudangan modern, terutama dalam mendukung proses operasional yang lebih akurat, efisien, dan berbasis data. Penelitian ini bertujuan untuk melakukan tinjauan literatur secara sistematis mengenai penerapan AI pada Warehouse Management System (WMS) dalam mengoptimalkan akurasi stok dan meningkatkan produktivitas pergudangan. Penelitian menggunakan pendekatan Systematic Literature Review (SLR) dengan mengacu pada kerangka PRISMA 2020 sebagai pedoman dalam proses identifikasi, seleksi, dan analisis literatur. Sebanyak 25 artikel ilmiah yang diterbitkan pada periode 2023–2026 telah diidentifikasi, diseleksi berdasarkan kriteria yang ditetapkan, dan dianalisis untuk memperoleh gambaran mengenai perkembangan penerapan AI dalam sistem pergudangan. Hasil kajian menunjukkan bahwa teknologi yang dominan diterapkan meliputi Machine Learning (ML), Deep Reinforcement Learning (DRL), Internet of Things (IoT) yang terintegrasi dengan AI, dan Digital Twin. Pada studi yang menyajikan data kuantitatif, rata-rata peningkatan akurasi stok yang dilaporkan mencapai 28,2%. Penerapan teknologi tersebut juga berkontribusi terhadap optimalisasi proses operasional, peningkatan efisiensi aktivitas pergudangan, percepatan pengambilan keputusan berbasis data, serta pengurangan pemborosan energi dan sumber daya. Meskipun demikian, implementasinya masih menghadapi sejumlah tantangan, seperti keterbatasan infrastruktur teknologi, kompleksitas integrasi dengan sistem yang telah tersedia, kebutuhan terhadap data berkualitas tinggi, serta kesiapan sumber daya manusia. Temuan penelitian ini diharapkan dapat menjadi landasan bagi peneliti, praktisi industri, dan pengembang sistem dalam merancang serta mengembangkan WMS berbasis AI yang lebih efektif, adaptif, dan berkelanjutan

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