Traditional document-based design has been the dominant engineering practice for decades. In this paradigm, system design information — requirements, interface control documents, performance specifications, and design rationales — is recorded in large volumes of natural-language text and passed between engineers, departments, and contractors. As explicitly documented in the Beijing Institute of Aerospace Systems Engineering patent (2019), "in traditional complex product development processes, top-level system design schemes exist in the form of a large number of documents, making it difficult to assess the completeness and consistency of information, difficult to describe various activities and their interactions, and difficult to perform verification, optimization, and evaluation."

The PatSnap platform tracks this transition across sectors. The National University of Defense Technology (2021) makes the paradigm contrast explicit: "currently, when designing new models, most people adopt text-based systems engineering (TSE)... as systems become increasingly complex, TSE gradually fails to meet the challenges, generating various problems, such as easily producing inconsistency in understanding." MBSE was introduced specifically to address these failure modes by replacing documents with formal, machine-readable models.

The formal INCOSE definition, cited in the Nanjing University of Aeronautics and Astronautics patent (2021), describes MBSE as "a standardized modeling tool that continuously runs through the entire lifecycle during the entire system development process, supporting system requirements analysis, system analysis and design, and subsequent simulation and verification through formal modeling." The INCOSE 2025 Systems Engineering Vision identifies MBSE as a primary direction for systems engineering propagation into all industrial design domains.

According to Defense Acquisition University, MBSE is now a core competency requirement for major US defense acquisition programs. The PatSnap Analytics platform enables teams to map exactly where competitors are filing in this space.

Consistency is the primary differentiator. Document-based design cannot enforce semantic consistency across hundreds of interconnected specification documents; MBSE's formal model structure enforces it by design, as demonstrated by the China Academy of Launch Vehicle Technology (2025).

Change propagation is computationally intractable in document-based design. Raytheon's Computational Accelerator Architecture for Change Control (2020, US Air Force-funded) provides a patent-protected solution to this problem that has no analog in document-centric environments. According to NASA's systems engineering handbook, change propagation failures are among the leading causes of late-stage program cost growth.

Interface failures at system integration are structurally endemic to DBSE. The China Ship Research and Design Center (2026) documents that separately developed subsystems lead to "interface mismatches and performance shortfalls at late-stage integration" — a failure mode eliminated by MBSE's unified model.

US military programs (B-21) are already deploying MBSE at scale, establishing a competitive benchmark that Chinese defense programs are explicitly referencing, as noted by CETC Research Institute No. 10 (2024). The US Department of Defense has mandated MBSE adoption across major acquisition programs.

Domain-specific MBSE process definition is an emerging bottleneck. Generic MBSE frameworks do not map naturally to the organizational structures and long-cycle workflows of defense programs. AVIC Shenyang Engine Research Institute (2025) identifies role assignment, modeling responsibility clarity, and model handoff path definition as the critical unsolved problems in enterprise-scale MBSE adoption. The PatSnap open API enables integration of patent signals into engineering workflows.

Multi-source linkage determination across disparate MBSE works remains an open research frontier. Raytheon (2019) demonstrates that even within MBSE regimes, automated semantic linkage discovery between independently developed work products requires dedicated computational infrastructure.