Despite increasing digitalization in factory planning, generating factory planning information remains a predominantly manual and time-consuming process. Meeting challenges like circular economy requires efficient factory planning, achievable by leveraging insights from large production datasets yet only accessible through inefficient manual processes. This paper presents a framework for automated data-driven generation of factory planning information utilizing data science. Insights extracted from production data using methods such as process mining are synchronized with planning objectives to align with upcoming factory requirements and are ultimately transformed to factory planning information. This bypasses limitations of manual processes and allows for higher efficiency in factory planning.