<![CDATA[The rapid growth of e-commerce has considerably amplified the complexity of supply chain management, particularly for small and medium enterprises (SMEs) that sell fast-moving consumer goods. Gentle Living, a web-based baby product retailer, faces persistent operational challenges rooted in inaccurate raw material recording, the absence of demand forecasting, unstructured buffer stock management, and unsystematic stock verification. These deficiencies result in stockouts, overstock, and disrupted production continuity. Digital transformation of supply chain operations has emerged as a critical success factor for SMEs competing in dynamic e-commerce environments, where operational inefficiencies directly translate into customer attrition and revenue loss. This study develops a web-based information system that integrates three core supply chain modules: demand forecasting using the Autoregressive Integrated Moving Average (ARIMA) method, buffer stock calculation using the probabilistic safety stock model, and stock verification (stock opname) supported by inventory reconciliation workflows. System development follows the Agile methodology, with Laravel as the primary backend framework and Python for time-series processing. Functional testing employs Black-Box Testing, while user acceptance is evaluated through User Acceptance Testing (UAT). The ARIMA model demonstrated practically acceptable forecasting accuracy with MAPE values within industry-standard thresholds (<20%), and the probabilistic buffer stock formula successfully calibrated safety inventory thresholds to service-level requirements. Results indicate that the system accurately predicts demand trends, maintains appropriate buffer stock levels, and significantly reduces inventory discrepancy rates, thereby improving the overall efficiency and resilience of Gentle Living's supply chain.]]>
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