Hybrid decision support in strategic product planning

Strategic product planning defines foresighted directions for product engineering by determining which ideas, technologies and concepts are pursued further. Decisions made in this phase strongly influence subsequent life cycle phases but are characterized by high uncertainty, fragmented information, and predominantly qualitative assessment practices. At the same time, increasing digitalization generates large, heterogeneous, and dynamic data from markets, engineering, usage, and sustainability domains. In this work, such information is understood as extreme data, as its heterogeneity, variety, and dynamics exceed the capabilities of traditional planning approaches. This paper investigates the potential of leveraging extreme data to strengthen hybrid decision support in strategic product planning. A structured literature review is conducted to identify key challenges. These challenges are analysed along the generic product life cycle to capture decision points, information flows, and information circularity. Relevant data sources are identified with respect to their relevance for strategic decisions. Based on this analysis, seventeen potentials for hybrid decision support are derived and clustered into five fields of action. The results indicate that extreme data can enhance strategic foresight, idea evaluation and portfolio decisions, lifecycle-spanning knowledge integration, engineering support for strategic decisions, and the adoption of AI-based methods.