<![CDATA[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.]]>
