<![CDATA[The fashion industry is known for its volatility, characterized by rapid trend cycles and short product life spans, which pose operational challenges for businesses like La Crème, a B2B wholesaler in Indonesia. With over 300 SKUs and a production capacity of 6,000 pieces per week, La Crème serves a wide network across Southeast Asia and the Middle East. However, the company faces inefficiencies in supply-demand balance, leading to overstock and shortage issues that hurt profitability and customer satisfaction. In 2023, production volume dropped significantly, highlighting flaws in inventory planning. This research aims to develop a structured aggregate production planning model to mitigate these imbalances while enhancing service levels and cost efficiency. Using a mixed-method case study approach, quantitative data includes historical sales and production records, while qualitative insights are gathered from interviews and observations at La Crème's facilities. The study evaluates forecasting accuracy through methods like Linear Trend and Moving Average, revealing that effectiveness varies by SKU. The core analysis involves simulating four production planning models over three months: chase strategy, level strategy with stockouts, subcontracting, and overtime. Results indicate that the Level Strategy with Subcontracting is the most effective, achieving the lowest total cost while maintaining service levels. This study emphasizes the importance of data-driven planning to align forecasted demand with production capacity, ultimately promoting agility and improved financial outcomes for La Crème.]]>
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