<![CDATA[The increasing complexity of B2B lubricant distribution requires purchasing decisions that simultaneously consider sales targets, profitability, and inventory risk. However, purchasing policies are often based solely on historical demand, resulting in inefficient product portfolios and increased deadstock risk. This study aims to develop a product portfolio optimization model by integrating goal programming, hybrid demand forecasting, and inventory risk analysis. A quantitative operations research approach was employed using sales, inventory, profitability, and customer relationship management data from a lubricant distributor in Indonesia. Hybrid demand was estimated by combining historical demand with CRM-based opportunity demand using an expected value approach, while inventory risk was evaluated through inventory ageing and reinforced by integrated FSN–XYZ–ABC classification. The optimization model successfully achieved the purchase volume target of 2,000 barrels, generated an optimal profit of IDR 931.31 million, and maintained deadstock purchase value within the company’s risk tolerance. Compared with actual purchasing practices, the proposed model reduced the proportion of purchase value allocated to non-moving products while improving profitability and working capital efficiency. These findings demonstrate that integrating goal programming with hybrid demand provides an effective decision-support approach for balancing multiple purchasing objectives and optimizing product portfolio management in B2B lubricant distribution.]]>
