Operations & Compliance

Process mining for operational bottlenecks

Reconstruct how processes actually run from system event logs, compare against the documented process, and quantify where time, cost, and rework are being lost.

Decision supportPattern-matchingCost reductionAccuracy

15–30%

reduction in process cycle time

Opportunity assessment

Business Impact
3

Moderate impact. Meaningful cost or revenue improvement, likely noticeable at function level but modest relative to total business scale.

Feasibility
3

Moderate effort. Requires configuration, prompt engineering, and testing. A capable team can get there but expect several months.

Data Readiness
2

Demanding data requirements. Relies on clean, integrated data across multiple systems. Significant data preparation work typically needed.

Risk Exposure
4

Low risk. Limited external exposure. A human reviews output before it reaches anyone outside the team.

Change Complexity
3

Moderate people impact. Part of someone's working day changes. Requires training and some adjustment time, but roles remain broadly the same.

Tooling required

Specialist AI tool

Things to consider

  • The gap between the documented process and the real one is almost always larger than management expects, and process mining shows it with evidence rather than opinion.

  • Event log quality is the gating factor. You need timestamps, case identifiers, and activity labels that are consistent across systems, and getting that is most of the project.

  • Rework loops are usually where the money is, not the headline cycle time. Look for the cases that go backwards through the process.

  • Experiment starter: Mine one end-to-end process with good log data — order to cash, or procure to pay. Quantify the cost of the top three deviations from the documented path and take those to the process owner.

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