Apromore supports the full spectrum of process mining functionality, from automated discovery of process models for tactical analysis, to real-time predictive monitoring for operational intervention. These features are complemented by an authoring environment for business process models, underpinned by an enterprise process repository.
Shared workspace of models and logs
Organize and seamlessly share your process models and event logs across the enterprise via a collaborative repository workspace.
Discovery of process maps and BPMN models
Automatically discover a process map or your “as-is” BPMN model from an event log, and dynamically switch between the two views. Change process perspective to focus on resources, roles, business object states and more. Apply abstraction mechanisms to create simplified views of your model.
Focus on what matters more. Reduce the complexity of your process execution data by slicing and dicing your event logs by case variant, timeframe, a wide range of performance measures, specific execution paths, degree of rework, any attribute-value in the log and more.
Analyze frequencies and durations of activities and handovers, using a range of statistic measures, overlaid on a process map or BPMN model.
Show detailed statistics at different levels of abstraction using a wide range of dashboard charts. Focus on cases, activities, resources, or any attribute in your log. Inspect your process data case-by-case or by case variant, to find out who did what, when and how often.
Flow comparison and multi-log animation
Visually compare two or more process variants to identify structural differences. Animate variants simultaneously to understand differences in temporal dynamics.
Complete authoring environment
Create and edit BPMN process models, share your models, check their similarity, merge them and use them as input for process mining.
Rule-based and model-based conformance
Compare “expected processes”, as represented by your to-be BPMN process models or a set of business rules, against ”actual processes” as recorded in your event logs, to spot discrepancies and assess their impact.
Predictive process monitoring
Train machine learning models to predict different process characteristics, and watch predictions refresh in real time, as your process cases unfold. Train your models for a variety of prediction problems, including case outcome, remaining time, next activity and case continuation.
Change detection and explanation
Detect and explain undocumented changes (“drifts”) in your process behavior from your event logs. Segregate different process variants temporally (e.g. before, during and after a crisis) and compare the variants based on their flows, bottlenecks, frequency or rework, to identify sources of agility.
Discover logical stages of your processes and measure performance at each stage of your business process. Compare stages based on a range of performance statistics such as arrival rate, departure rate and exit rate, and assess their efficiency and knock-out effects.
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