Domain
Business processes function, but their actual execution often diverges from documented designs. SAP processes rarely follow planned flows in practice. Instead, loops, waiting times, and bottlenecks emerge whose causes remain unclear. Process Mining uses event-based data to uncover what actually happens in processes rather than analyzing only documentation.
Planned vs. actual
Illustrative example of an order-to-cash process: switch between the documented and the actual flow.
Illustrative process map of an order-to-cash process with the steps order, check, approval, delivery and invoice. As documented, the process is linear. In reality it shows a loop back to the order, a variant that bypasses approval, a dead end after delivery and a bottleneck with waiting time at approval.
Processes are often understood as linear sequences, but event-based log data reveals a more complex reality: numerous variants, processing loops, and dead-end paths. This data shows actual process execution, not idealized scenarios.
Every process has points where delays emerge. Often the core problem isn't pure processing time, but wait times at critical approval gates. In processes like order-to-cash and purchase-to-pay, these delays become visible and can be quantified to determine improvement potential.
A single process analysis provides only a snapshot. Processes are dynamic and change continuously. Therefore, ongoing monitoring is required: KPI dashboards provide visibility into improvements and emerging challenges. Real-time alerts are more meaningful than monthly static reports.
Log data from SAP and Salesforce requires extensive preparation work. Data types are often inconsistent, timestamps follow different formats, and identical activities may have different names across system runs. This harmonization work is detailed and time-consuming, but necessary for reliable process analysis.
A small number of process variants are typically responsible for most delays. Identifying these variants with data rather than intuition enables targeted optimization. Data analysis lets you determine with certainty where optimization efforts will be effective rather than relying on assumptions.
Process analysis results are valuable only if they lead to concrete improvements. Visualizations and insights from Process Mining have little value if teams don't adapt their methods. We support organizations in implementing real change projects based on analysis findings that achieve sustainable improvements.
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