In today’s competitive global market, companies face increasing pressure to develop cost-efficient products without compromising quality or innovation. Cost Breakdown Analysis (CBA) is a method that supports informed decision-making by making cost structures transparent and identifying cost drivers early in the development process. This thesis investigates how IKEA of Sweden AB applies CBA within its new product development (NPD) process to support strategic and competitive business decisions. This case study also explores how cross-functional collaboration between roles, such as sourcing, engineering, and product development, enhances the effectiveness of cost analysis in guiding decisions. A single-case study design was adopted, applying a mixed-methods approach. Quantitative data was collected through a survey of 120 respondents from multiple departments, while qualitative insights were gathered through five semi-structured interviews with key stakeholders in IKEA’s range development process. Descriptive statistics, thematic coding, and qualitative content analysis were used to analyze the data. Findings show that detailed cost analysis supports strategic decision-making by enabling early evaluation of material choices, production methods, and supplier selection. Cost drivers such as materials and processing were identified as the most influential in shaping decisions. Cross-functional collaboration was found to be critical for interpreting cost data effectively, with early involvement and shared targets contributing to alignment and efficiency. The study also reveals challenges in tool standardization and departmental priorities that sometimes shape cost-related decisions. The conclusions highlight the strategic value of CBA in aligning design, sourcing, and business goals. While the study focuses on IKEA, the insights offer broader implications for companies aiming to enhance decision-making during product development. Future research may explore the integration of digital costing tools or compare CBA practices across different industries to assess generalizability.