As remanufacturing becomes an increasingly important strategy for supporting circular business models, manufacturing companies face growing complexity in deciding which components to remanufacture and how remanufacturing should be designed and operated. These decisions involve not only technical feasibility, but also considerations related to technological readiness, production constraints, and commercial viability. The purpose of this study is to examine decision-making across engineering, sales, and production functions, and the data needed to support remanufacturing. The study follows a case study method with data collected through interviews and workshops at an Original Equipment Manufacturer (OEM) planning to expand its remanufacturing portfolio. The results show a clear distinction between decisions made before and after implementation. Before implementation, decisions are mainly strategic and focus on customer value, business models, component selection, site strategy, and compliance, supported by descriptive and comparative data such as market insights, cost-benefit analyses, and historical performance data. After implementation, decision-making becomes more operational and optimization-oriented, relying on real-time and operational data, including sensor data, traceability information, inspection results, and process performance data. The results further show strong interdependencies between engineering, sales, and production. Decisions made in one function directly affect the others, and lack of coordination can result in remanufacturing solutions that are technically feasible but commercially or operationally challenging. To address this, the study identifies key decision areas and associated data needs across functions, highlighting the importance of integrated and data-driven decision-making for effective and scalable remanufacturing.