Discover, supervise and improve your Business processes

Process Mining aims to discover, monitor and improve real processes by extracting knowledge available in the event logs of an organization's current information systems.

Process Mining Detects or diagnoses problems based on fact and not guesswork or intuition. 

The application of Process Mining in an organization offers the following capabilities:

  • Automated discovery of process models, exceptions, and process instances (cases) along with basic frequencies and statistics.
  • Automated discovery and analysis of customer interactions, as well as alignment with internal processes.
  • Understanding different perspectives on operations, not just a process perspective.
  • Monitoring of key performance indicators using real-time dashboards
  • Compliance Verification and Gap Analysis Capabilities
  • Predictive analysis, prescriptive analysis, scenario testing and simulation with contextual data.
  • Improvement of existing or previous process models using additional data from recorded records.
  • Data preparation and data cleaning support.
  • Combination of different process models that interact with each other in a single process mining panel
  • Support for visualizing how processes contribute to business value (such as business operating models) - process contextualization.
  • Effective cooperation between Business and IT.
  • Standardization of business processes
  • Improving operational excellence by optimizing processes

With the information available in the event log, three types of analysis are performed:

Discovery which takes an event log and produces a process model without using any a priori information, only with the help of Process Mining algorithms.

Conformance where the event logs (real processes) and the corresponding process models (ideal and predefined processes in BPMN) are compared, and the resulting coincidences or differences are identified, in order to diagnose deviations or inefficiencies between the process process model. derivative business and ideal processes.

Extension/Enhancement where the process models are adapted and improved based on the actual process data.

Example:

Process "Policy Issue", Insurance item.
Stage: Discovery

Expectation:

  • 1 process.
  • 24 stages.
  • 1 case
  • Execution time: 10 days

Reality:

  • 140 thousand records captured.
  • 1 process.
  • more than a thousand possible cases
  • Average Execution Time: 14 days

After debugging the data and modeling the information we can discover the following inefficiencies in the process:

Critical route

Statistics

People and activities interaction graph

Bottleneck detection

Reprocesses and Loops