process.science
بواسطة process.science GmbH & Co. KG
Good value for money compared to enterprise competitors: Reviewers often compare Process.Science favorably to larger tools like Celonis, citing lower cost and reduced implementation complexity
Strong visualization of bottlenecks and deviations: Frequently praised for making hidden inefficiencies, rework loops, and process variants visually obvious and easy to explain to stakeholders
Easy and fast implementation (“plug‑and‑play”): Many reviewers highlight minimal setup effort, quick time‑to‑value, and first insights within days or weeks rather than months
Seamless integration with Power BI and Qlik Sense: Users consistently praise the ability to run process mining inside existing BI tools, avoiding the need for a standalone platform or duplicated data models
Requires additional components to get started: Some reviews mention the need to install extra packages (e.g., event log transformers, database connectors), adding upfront technical steps
Initial data preparation can be technically demanding: Creating clean event logs (timestamps, case IDs, activities) often requires SQL skills or additional data transformation work
Limited integrations beyond BI and core data sources: Users note fewer out‑of‑the‑box integrations compared to enterprise process mining platforms, sometimes requiring manual data preparation or exports
Strong dependency on Power BI or Qlik Sense: A recurring criticism is that organizations must already be invested in Power BI or Qlik; limitations or performance issues in those platforms also affect Process.Science
