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All Journal ComTech: Computer, Mathematics and Engineering Applications INFOMATEK: Jurnal Informatika, Manajemen dan Teknologi
Rambing, Danni
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Computer Science & IT Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Mechanical Engineering

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Comparative Analysis of Reconciliation Techniques: Bottom-Up, Top-Down, and MinT for Product Forecasting in Retail SMEs Rambing, Danni; Kusumaningrum, Retno; Sugiharto, Aris
ComTech: Computer, Mathematics and Engineering Applications Vol. 16 No. 1 (2025): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v16i1.12293

Abstract

Small and Medium Enterprises (SMEs) have experienced rapid growth, contributing approximately 95% to the global economy, 60% to global employment, and 50% to global GDP. This growth is accompanied by significant challenges, with approximately 70% of SMEs failing within the first three years, primarily due to poor inventory management. It emphasizes the crucial role of accurate demand forecasting for SMEs, particularly in the retail sector, where time series at various levels of hierarchical structure exhibit different scales and display diverse patterns. However, most existing research on demand forecasting for SMEs focuses on a single hierarchical level—either bottom, middle, or top—without addressing the entire hierarchy. The research sought to address this gap by forecasting across all hierarchical levels and evaluating different reconciliation techniques to generate coherent and accurate forecasts for multiple products in retail SMEs. The ETS state space model was used as the base forecasting model. This model was widely recognized as a benchmark in forecasting competitions. The reconciliation methods assessed were Bottom-Up, Top-Down based on historical proportions (average proportions), Top-Down based on forecast proportions, and Minimum Trace (MinT) (Ordinary Least Squares (OLS), OLS Non-Negative (OLS Non-Neg), Weighted Least Squares (WLS), and WLS Non-Negative (WLS Non-Neg)). The evaluation results show that the OLS Non-Negative method, with an average SMAPE value of 35.335%, produces more accurate reconciliation than other methods. In addition, this method also outperforms the base model with an increase in accuracy of 13%.
Rekonsiliasi Temporal dan Struktural Hierarkis untuk Meningkatkan Akurasi Peramalan Penjualan pada UKM Ritel Rambing, Danni; Tedy, Frengky; Tengangatu, Paul Filson M.; Bani, Januar Elfreed; Naifio, Raynaldi Bouk
INFOMATEK Vol 28 No 1 (2026): Juni 2026 (In Progress)
Publisher : Fakultas Teknik, Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/infomatek.v28i1.41554

Abstract

Usaha Kecil dan Menengah terutama pada sektor retail menghadapi tantangan tingginya jumlah produk, keterbatasan sumberdaya, serta pola karakteristik permintaan produk yang fluktuatif dan intermittent. Penelitian ini menginvestigasi peran struktural rekonsiliasi dan temporal rekonsiliasi dalam meningkatkan akurasi ramalan penjualan UKM Funan Mart, sebuah ritel sembako di Kabupaten Belu, Nusa Tenggara Timur. Model dasar yang digunakan dalam penelitian ini yaitu State Space Exponential Smoothing (ETS) yang banyak digunakan karena tidak memerlukan biaya komputasi yang tinggi dan dapat menyesuaikan dengan berbagai jenis data deret waktu. Hasil ramalan dasar dari ETS kemudian direkonsiliasi menggunakan pendekatan MinTrace (OLS), MinTrace dengan batasan negatif, Weighted Least Squares structural scaling (WLS-S), dan WLS-S non-negatif. Hasil penelitian ini menunjukkan bahwa rekonsiliasi dapat meningkatkan akurasi ramalan terutama pada level hierarki bawah dan agregasi temporal bulanan. Metode WLS-S dengan batasan negatif menghasilkan kinerja terbaik melalui penurunan RMSE dari model dasar ETS 0,638 menjadi 0,626. Pada level ProdukByMonth, kesalahan ramalan berkurang sebesar 6,7% terhadap model dasar ETS, sedangkan pada KategoriByMonth terjadi peningkatan akurasi sebesar 1,4%.
Co-Authors Aris Sugiharto Bani, Januar Elfreed Frengky Tedy Naifio, Raynaldi Bouk Tengangatu, Paul Filson M.