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Tendean, Sandi
Universitas Widya Dharma Pontianak
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Journal : bit-tech

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Prediksi Safety Stock Produk Filter Oli Sepeda Motor Berbasis Demand Response (DR) - ARMA Sandi Tendean; Jimmy Tjen; Riyadi Jimmy Iskandar
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2282

Abstract

Manajemen rantai pasokan merupakan hal krusial yang dibutuhkan dalam menjaga persediaan suatu produk supaya tetap tersedia selama masa tunggu. Hal ini bertujuan untuk menjaga keberlanjutan suatu bisnis sehingga penjualan produk tersebut tidak terganggu dengan permasalahan kurangnya persediaan. Namun, metode prediksi konvensional seperti ARMA-klasik dan ARMA-GARCH seringkali kurang akurat pada data riil yang bersifat sparse yang didominasi nilai nol dan fluktuatif. Penelitian ini bertujuan untuk menggagas sebuah metode Auto Regressive Moving Average (ARMA) baru yang menggabungkan konsep demand response dengan analisis galat yang bernama Demand Response-ARMA (DR-ARMA). Metode ini dikembangkan melalui tiga tahap, yaitu penurunan matematis berbasis RMSE dan analisis tren, adaptasi model untuk data sparse, dan validasi menggunakan data primer penjualan sparepart filter oli dari CV di Kalimantan Barat selama 60 hari. DR-ARMA mengoptimasi prediksi ARMA berdasarkan pada tren penjualan serta mengontrol ketidakpastian prediksi dengan memanfaatkan analisis galat, supaya kesalahan prediksi dapat berkurang selama perhitungan safety stock. Simulasi numerik dilakukan pada data penjualan filter oli dari sebuah perusahaan yang ada di Kalimantan Barat. Hasil simulasi menunjukkan bahwa metode DR-ARMA dapat memprediksi penjualan filter oli dengan akurasi 80%, lebih tinggi dibandingkan metode prediksi lainnya seperti ARMA-Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) (74%) dan ARMA-klasik (57%). Metode DR-ARMA juga dapat digunakan untuk memprediksikan safety stock untuk 60 hari kedepan dengan tingkat kesalahan prediksi sekitar 17%. Hal ini menunjukkan bahwa metode DR-ARMA cocok digunakan untuk memprediksikan safety stock dari data yang bersifat sparse. Metode DR-ARMA dapat membantu pengguna dalam mengatur jumlah persediaan barang yang dibutuhkan tanpa perlu melakukan pengisian gudang secara berlebihan.
An Optimized Demand Response-ARMA Model for Inventory Management Under Intermittent Product Demand Vedano Gustine; Sandi Tendean; Jimmy Tjen
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3326

Abstract

Forecasting accuracy serves a crucial role in supply chain management, especially in calculating safety stock and purchase limit, particularly under demand fluctuations such as those observed for air filter products, which are characterized as slow moving and intermittent. The DR-ARMA method was designed to model sparse data effectively, despite the model heavily relies on manual tuning factor selection. In this case, the model still face limitation in handling intermittent demand. To address such methodological gap, this study proposes an optimized version of the Demand Response-ARMA (DR-ARMA) model which is able to handle intermittent demand, named Optimized Demand Response-ARMA (ODR-ARMA) by applying optimization problems that lead to an adaptive error multiplier factor. Using air filter sales data with a sparsity level of 62.3% and a varying lead time assumption from a company located in Pontianak. The comparative analysis of ODR-ARMA against LSTM, GBRT, and DR-ARMA reveals that the ODR-ARMA model demonstrates the best performance for both safety stock and purchase limit calculations with an average accuracy of 81.11% and 96.09%, respectively. The optimization results in a significant improvement, as the DR-ARMA model achieves an average accuracy of 51.26% for safety stock calculation and 30.48% for purchase limit calculation. As the ODR-ARMA model has the capability to generate an accurate demand forecast and requires low computational resources, this model can be used as a basis for enterprises, especially SMEs in decision making related to inventory management, which allows enterprises to avoid the risks of stockout, excess stock, and dead stock.
Development of a DR-ARMA-Based Inventory Forecasting System for Inventory Management Velita, Velita; Tendean, Sandi
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3770

Abstract

Inventory management at Micro, Small, and Medium Enterprises in traditional markets remains heavily reliant on intuition, posing high risks of overstocking or stockouts. This study develops a data-driven inventory requirement forecasting system for Toko Mandiri by integrating the Demand Response–Autoregressive Moving Average (DR-ARMA) method into a desktop application using the Rapid Application Development (RAD) approach. One year of historical daily sales data from four fermented products, with sparsity levels ranging from 38% to 51%, was utilized for training and testing. The dataset was partitioned into training (60%), validation (20%), and testing (20%). The DR-ARMA model's performance was evaluated quantitatively, yielding average values of 1.632 units for RMSE, 1.012 units for MAE, and 29.89% for MAPE, demonstrating reliability on fluctuating and sparse data. System usability evaluation involved three respondents across four task scenarios. Results indicate significant improvements in operational efficiency: the time required to determine inventory requirements was reduced from 30 minutes, using manual intuition-based methods involving physical stock checks across different locations, to just 1 minute, based on direct stopwatch measurements for all scenarios. This represents a 96.7% reduction in processing time. Interaction steps were streamlined from an unstructured process to only 6–7 clicks. The system's effectiveness reached an 83.33% task success rate among non-technical users. Integrating DR-ARMA into a practical application effectively transforms inventory decision-making from intuition-based to data-driven, potentially reducing operational risks.
Co-Authors Aldo Anderson, Pandrian Amok Darmianto Antonius Antonius Balbo, Florensius Darmanto, Tony Darmianto , Amok Edryan, Darwin Genrawan Hoendarto Gultom, Eva Gultom, Manorang Gunawan Happy Natalin Hendra Wijaya Hendro Hendro Hendro, Hendro Jimmy Iskandar, Riyadi Jimmy Tjen Kartono , Kenrick Ortonsius Krisyesika Laurensa, Ayu Maria Carolina Riyadi Jimmy Iskandar Susana Susana Thomas Aldo Vedano Gustine Velita, Velita Wenxi, Wenxi Willim, Andre Prasetya Yogi Tinnando