From Data Chaos to a Clear Overview: Interactive Report Generation in Systems Engineering
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Challenge: Data Deluge and Lack of Integration
In development projects, requirements and result data from testing and simulation are often stored separately, segregated and evaluated differently. A consolidation of these evaluations usually occurs only sporadically.
In practice, this means:
- Traceability is not transparent and is difficult to follow
- Assessments are often manual, project-specific and not reproducible
- Reports primarily serve downstream documentation — not control
The presentation Target Evaluation and Interactive Reporting in a Post-Data Management Platform addresses this very challenge and presents solution approaches.
Centralized Data Management as the Basis for Consistent, Transparent Reporting
The presented solution is based on SCALE.sdm as the central post-data management platform that brings simulation, test and metadata together in a common structure. A central element is a Master Data Management (MDM), which, among other things, ensures the following aspects:
- Unique identification of KPIs, load cases and measured variables
- Consistent naming and versioning across projects
- Reusability of evaluation logics
This creates for the first time a stable, versioned data basis on which target evaluations and reports can be systematically built – an essential building block for methodical Systems Engineering.
Interactive Report Creation in Systems Engineering
The approach of SCALE.sdm uses the Report Designer for interactive report creation as an integral part of Systems Engineering. Reports are generated directly from SCALE.sdm and are directly linked to data, KPIs and evaluation logics.
- Content and visualizations can be assembled modularly
- Changes to data or evaluation rules automatically propagate into the reports
- Reporting remains consistent, up-to-date and reproducible
- Manual reworking is eliminated
AI-Assisted Analysis in Postprocessing
To efficiently analyze large reports from simulation and testing, AI methods augment SCALE.sdm: they help identify relevant changes and create targeted summaries.
- Reports can span hundreds of pages; manual review is time-consuming
- AI automatically creates concise summaries of the most important findings
- Focuses on relevant changes, e.g., in KPI evaluations
- Supports an efficient, targeted analysis of large datasets
