Development of a DR-ARMA-Based Inventory Forecasting System for Inventory Management - Velita, Velita; Tendean, Sandi (Komunitas Dosen Indonesia, 2026)
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.