<![CDATA[Production planning at PT Anaya Sinar Jiwanta is still based on estimates, resulting in discrepancies between production quantities and sales levels and potentially causing excess raw material inventory. This study aims to apply the K-Means Clustering method to support production planning and raw material inventory control for Nona Cookies products. The research data were obtained from the 2023–2025 period and processed until the clustering results reached a convergent condition. Production data were analyzed based on Production Quantity (JP), Sales Quantity (JT), and Unsold Quantity (JTT), while raw material data used Initial Stock (SA), Quantity Used (JD), and Remaining Stock (SB). The clustering results produced three groups: low, medium, and high. For production data, the low group had average values of JP 0.0159, JT 0.0445, and JTT 0.0445; the medium group had JP 0.0952, JT 0.0858, and JTT 0.7850; while the high group had JP 0.8810, JT 0.8827, and JTT 0.4000. For raw material data, the low group had average values of SA 0.0068, JD 0.0060, and SB 0.1495; the medium group had SA 0.2047, JD 0.1937, and SB 0.6786; while the high group had SA 0.9008, JD 0.8997, and SB 0.5714. The clustering results can support the company in adjusting production quantities and raw material requirements, as well as controlling inventory in a more structured and data-driven manner.]]>
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