Peramalan Permintaan Roti Harian dengan Simulasi Monte Carlo di Reza Bakery
Article Sidebar
Main Article Content
Abstract
Article Summary
The fluctuating daily demand at Reza Bakery leads to an imbalance between production capacity and consumer needs. This study aims to forecast the daily demand of the three best-selling bread products using the Monte Carlo simulation method: Small White Bread, Mocha Bread, and French Chocolate Bread. Sales data from April 2025 covering 30 days were used as the simulation basis. The simulation involves calculating the probability distribution of demand, generating random numbers, and mapping them to demand predictions. Results show that daily demand predictions range from 33–40 units for Small White Bread, 16–22 units for Mocha Bread, and 7–12 units for French Chocolate Bread. This method helps in formulating more efficient production strategies, with good accuracy estimates based on the match between simulation results and historical trends. The simulation offers potential to reduce overstock risks and improve operational efficiency.
Keywords
Article Keywords
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-BY 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
License: All articles are licensed under 
Aziza, T. N. (2021). Upaya penguatan kelembagaan pangan. Jurnal Ekonomi Pertanian dan Agribisnis, 3(1), 204-217. https://doi.org/10.21776/ub.jepa.2019.003.01.20.
Dewi, L. K., & Budiarti, A. (2021). Pengaruh kualitas produk dan harga terhadap loyalitas pelanggan dimediasi kepuasan pelanggan pada Queenstreetstore. Jurnal Ilmu dan Riset Manajemen (JIRM), 10(5). https://doi.org/10.24912/jirm.v10i5.4035.
Geni, B. Y., & Santony, J. (2020). Prediksi pendapatan terbesar pada penjualan produk cat dengan menggunakan metode Monte Carlo. Jurnal Informatika Ekonomi Bisnis, 15-20. https://doi.org/10.37034/infeb.v1i4.5.
Ilyas, T. R. (2024). Strategi pengembangan usaha mikro, kecil, dan menengah (UMKM) lokal berbasis platform digital di Kota Malang. Indonesian Journal of Humanities and Social Sciences, 5(2), 1083-1092. https://doi.org/10.33367/ijhass.v5i2.5833.
Naim, M. A., & Donoriyanto, D. S. (2020). Pengendalian persediaan obat di apotek XYZ dengan menggunakan simulasi Monte Carlo. JUMINTEN, 1(2), 1-11. https://doi.org/10.33005/juminten.v1i2.11.
Natalia, C., & Oktavia, C. W. (2023). Penentuan jumlah penggunaan bahan baku tepung terigu berdasarkan simulasi Monte Carlo. Jurnal Penelitian dan Aplikasi Sistem dan Teknik Industri (PASTI), 17(1), 68-78.
Oktavia, C. W., Natalia, C., & Hindratmo, A. (2020). Analisis dan implementasi simulasi Monte Carlo untuk prediksi kebutuhan gula berdasarkan penjualan bumbu tabur XYZ. Metris: Jurnal Sains dan Teknologi, 21(02), 103-110. https://doi.org/10.25170/metris.v21i02.2494.
Rahmawati, Y., Tsani, G. M., Sanyoto, O. D., & Agustin, S. (2024). Prediksi Penjualan Roti Menggunakan Metode Monte Carlo (Studi Kasus pada Roti Daffa). Jurnal Inovatif, 3(3), 312-323.
Ramadhani, S. (2024). Analisis perkiraan produksi kopi di Provinsi Kalimantan Barat menggunakan metode simulasi Monte Carlo. JATI (Jurnal Mahasiswa Teknik Informatika), 8(4), 4515-4519. https://doi.org/10.36040/jati.v8i4.9988.
Rohmah, F. N., Dermawan, D. A., & Andriani, A. (2021). Memprediksi jumlah produksi roti dengan menerapkan metode Monte Carlo. Inovate: Jurnal Ilmiah Inovasi Teknologi Informasi, 5(2), 29-36. https://doi.org/10.33752/inovate.v5i2.3120.
Sutanti, E., & Ananda, R. (2025). Implementasi metode Monte Carlo untuk memprediksi penjualan di toko roti Dita. JATI (Jurnal Mahasiswa Teknik Informatika), 9(2), 2601-2605. https://doi.org/10.36040/jati.v9i2.13166.
Utami, M. R., & Nasution, M. I. P. (2025). Analisis literatur prediksi tren penjualan e-commerce berbasis data time-series: Metode statistik & machine learning. Jurnal Ilmiah Penelitian Mahasiswa, 3(2), 331-339. https://doi.org/10.61722/jipm.v3i2.844.
Vermila, C. W. (2020). Analisis tingkat loyalitas konsumen terhadap produk roti di toko Rotte Bakery Garuda Sakti Kecamatan Tampan Kota Pekanbaru Provinsi Riau. OPTIMA, 3(2), 1-7. https://doi.org/10.33366/optima.v3i2.1752.
Weenty, I., Patricia, R., & Wowor, W. (2024). Rekayasa menu: Penerapannya pada produk kue. Jurnal Sains dan Teknologi, 6(3), 261-268. https://doi.org/10.55338/saintek.v6i3.3899.