Q-Monitor makes Product Data Quality (PDQ) visible throughout the entire process chain, providing results that reflect either the current status or data quality over time.

Statistical PDQ results are available in either graphical interpretations or data tables. The evaluation reports provide highly relevant feedback and highlights the most common errors that could be eliminated with specific training support. Combining this with Q-Checker`s batch mode data enables clients to evaluate the work of their suppliers in detail.

Establish a continuous improvement process

• Measure data quality in significant process points
• Statistics about most frequent errors and quality
• Evaluation of departments, partners and processes
• Targeted training

Provided Results

• Identifying of the most frequent errors, allowing users to avoid or eliminate them using targeted training with the designer.
• Evaluation of particular departments, projects or suppliers, enabling users to determine optimal requirements for targeted measures to ensure proper quality.
• Measurement of PDQ enhancement results over time, providing a measurable Return On
• Investment analysis for Q-Checker utilization, based on the saved re-working time.
• With each verification of a CATIA model, interactive or in batch mode results are written to the Q-Monitor database. A few mouse clicks are all that is required to create clear, graphical interpretations of the checked cross sections with the Q-Monitor administration tool.

Related Functionality from Q-Checker and Q-Monitor

The Q-Checker check seal for enhanced performance

• A check seal is a summary of a past checked model which can be internal or external
• Avoid a recheck if a valid check seal is compliant to save time
• A check seal includes a model check sum info, check profile used, checking date, etc.
• If check seal is invalid, options are to give a warning, recheck the model, or reject it.

Q-Monitor to establish a continuous improvement process

• Measure data quality in significant process points
• Statistics about most frequent errors and quality improvement
• Evaluation of departments, partners and processes
• Targeted training