The Silent Time Waster in Engineering
In industrial electrical engineering, manual data transfer from 2D CAD drawings to Excel lists is far more than just an administrative annoyance—it is a strategic risk to process reliability. Highly qualified electrical planners spend significant portions of their working time manually capturing, checking, and laboriously formatting equipment identification tags.
This practice not only wastes valuable engineering resources but also undermines overall data integrity. Particularly in complex installations, manual transfer errors inevitably lead to delays in downstream processes. As strategists for industrial digitalization, we must ask ourselves: Why do we still accept manual formatting tasks in the age of Industry 4.0 that specialized software can perform more precisely, faster, and reproducibly?
Takeaway 1: Moving Away from “Manual Layer-Picking”
Previously, the extraction of equipment tag data was based on an unsystematic process we call “manual layer-picking.” Planners had to manually search through various layers of a 2D CAD drawing, select symbols, and transfer the information to Excel lists. Often, unreliable isolated solutions such as rudimentary PowerShell scripts or uncoordinated CSV exports were used, which did not create a consistent database.
This fragmented approach is no longer sustainable with increasing project complexity, as it exponentially increases the error susceptibility of the entire project hierarchy.
“The preparation from the CAD layout by selecting the required symbols for export is error-prone and leads to delays in complex projects.”
Eliminating this error-prone intermediate step is the prerequisite for a significant reduction in time-to-market.
Takeaway 2: Python as the Secret Weapon of Electrical Engineering
To bridge the gap between classical engineering and modern IT, we rely on a modular Python tool. Unlike rigid standard software, this solution leverages the power of specialized libraries: Pandas handles high-performance structuring of complex datasets, while OpenPyXL provides a professional interface for Excel export.
The tool extracts raw data directly from DXF plant layouts and offers far more than just data export:
- Intelligent Classification: The software autonomously identifies inputs and outputs of the installation.
- Automated Data Enrichment: Missing information, such as connection numbers 0-7, is added by the script based on rules.
- No “Black Box”: Since the tool is modular and flexibly configurable, company-specific formats and individual naming conventions can be mapped at any time.
Takeaway 3: Intelligent Error Detection as an “Automated Quality Gate”
Automated conversion is only valuable if it simultaneously ensures quality. The equipment tag tool therefore functions as an integrated automated quality gate that detects discrepancies before they reach PLC programming or control cabinet construction.
The system automatically identifies and documents:
- Duplicates: Multiple assignments of identifications for different components.
- Layout Inconsistencies: Cable routes that are too long or missing connections to sub-distributors.
- Completeness Deficiencies: Incorrect entries or missing connection data.
A decisive process-technical advantage: All errors are logged in separate Excel sheets. This enables highly efficient error handling in the earliest planning stage and massively reduces the cost of quality.
Takeaway 4: The “Single Source of Truth” for PLC and E-CAD
Through automation, the DXF plant layout becomes the single authoritative data source (single source of truth). The tool eliminates manual redundancies by generating all required target formats from a single dataset:
- PLC Programming: Seamless creation of PLC tags and constants for the Siemens TIA Portal. Manual typos in the variable list are a thing of the past.
- E-CAD (WSCAD): Specially formatted exports, optionally with or without detailed reference information.
- Documentation: Automated creation of I/O overviews for project documentation.
This strategic approach ensures that all departments—from design through programming to maintenance—work with identical, validated data.
Conclusion: Efficiency Is Not a Coincidence, But a Decision
The automation of equipment identification is a prime example of how the digitalization of sub-processes optimizes the entire value chain. The results are measurable: significant time savings, drastically reduced error rates, and consistent data for all downstream processes.
Efficiency in electrical planning is not a random product but the result of the decision to replace unreliable isolated solutions with intelligent, script-based workflows. Those who invest in the automation of these interfaces today secure the decisive competitive advantage through shorter project durations.
How much time does your team currently still lose on tasks that a script should actually be handling?