Feature Oriented Competitor Analysis pada Warehouse Management System di Industri

Razzan Carveyna (Universitas Muhammadiyah Malang)
Wahyu Andhyka Kusuma (Universitas Muhammadiyah Malang)

semanTIK
Vol. 11 No. 2 (2025): SemanTIK : Teknik Informasi

Warehouse Management System (WMS) memiliki peran penting dalam mendukung efisiensi manajemen stok gudang, namun analisis fitur kompetitor secara sistematis masih jarang dilakukan. Penelitian ini bertujuan untuk menganalisis fitur utama WMS dan memberikan rekomendasi pengembangan aplikasi ScanMP sebagai sistem manajemen stok dengan integrasi barcode scanner dan otomatisasi laporan. Metode yang digunakan adalah kualitatif deskriptif dengan pendekatan Feature Oriented Competitor Analysis (FOCA) berbasis App Store dan Large Language Model (LLM), melalui analisis metadata dari lima aplikasi kompetitor yang diperoleh dengan web scraping pada Google Play Store. Hasil penelitian mengidentifikasi lima fitur utama yang paling umum, yaitu Stock Tracking, Barcode Scanning, Report Export, Expense Tracking, dan User Access Control, yang dipetakan dalam bentuk hierarki fitur. Selain itu, penelitian ini juga menyoroti relevansi fitur tersebut terhadap kebutuhan industri yang dinamis. Kesimpulan dari penelitian ini menunjukkan bahwa pendekatan FOCA berbasis App Store dan LLM mampu memberikan wawasan strategis bagi pengembangan ScanMP agar lebih adaptif dan kompetitif dalam memenuhi kebutuhan industri logistik. Warehouse Management Systems (WMS) play a crucial role in enhancing warehouse stock efficiency, yet systematic competitor feature analysis remains limited. This study aims to analyze the core features of WMS applications and provide recommendations for developing ScanMP, a stock management system with barcode scanning and automated reporting capabilities. A descriptive qualitative method was applied using the Feature Oriented Competitor Analysis (FOCA) approach supported by a Large Language Model (LLM), by analyzing metadata from five competitor applications collected through web scraping of the Google Play Store. The results identified five core features—Stock Tracking, Barcode Scanning, Report Export, Expense Tracking, and User Access Control—which were mapped into a hierarchical structure. In addition, this study emphasizes the relevance of these features to the evolving demands of the industry. The findings conclude that the FOCA approach with LLM provides strategic insights to support the development of ScanMP as a more adaptive and competitive WMS for industrial needs.