Process owners and transformation teams that need observed operational behavior to influence architecture, requirements, testing, and cutover.
Whether process mining will remain a dashboard exercise or become traceable evidence for implementation and continuous improvement.
Decision context
Process mining reconstructs behavior from case and event data. For an SAP program, its value increases when variants and bottlenecks affect requirements, architecture, test selection, and the target operating model rather than living in a separate analysis surface.
Adranum ingests customer-authorized event logs, calculates observed transitions, cycle-time distributions, throughput, variants, bottlenecks, and anomaly signals, then stores those observations in the same context graph used by migration workflows.
Scenario models run immutable simulations over volume, concurrency, availability, error rate, queue pressure, manual work, and risk. Results are decision support, not forecasts guaranteed to match production.
What the workflow must cover
Observed variants
Build case paths and transition counts from event evidence, preserving source and case boundaries and identifying dominant and exceptional flows.
Performance baselines
Record p50 and p95 cycle time, throughput, bottlenecks, queue pressure, and configured process-health thresholds.
Scenario modeling
Run repeatable 500-run simulations with immutable inputs so alternatives can be compared without rewriting earlier assumptions.
Continuous improvement
Compare recurring observations, strengthen supported graph edges, detect anomalies, open incidents, and retain improvement-cycle history.
Implementation workflow
Start with a bounded customer scenario and explicit acceptance criteria. Preserve native SAP permissions and accountable review while the software creates a repeatable evidence chain.
- Define case, event, activity, and timestamp fields.
- Ingest an authorized event log or scheduled stream.
- Review variants, distributions, gaps, and bottlenecks.
- Relate observed behavior to requirements, tests, and target architecture.
- Repeat observations and compare improvement evidence.
Evidence to require
A transformation claim should resolve to observable artifacts, decisions, and execution receipts. Ask for the following evidence in a representative evaluation:
- Event source hash
- Case coverage
- Variant and transition counts
- Cycle-time distribution
- Bottleneck calculation
- Scenario input and result hash
- Anomaly rule
- Improvement-cycle history
Boundaries and non-claims
Adranum separates analysis, proposal, human review, package creation, customer-local validation, and production execution. A later state never rewrites the evidence that supported an earlier decision.
- Poor or incomplete event data produces bounded observations, not a complete process model.
- Simulation output is not a production guarantee.
- Adranum does not claim to replace specialist enterprise process-mining suites in every use case.
Buyer checklist
- What portion of cases and events was ingested?
- Are variants observed or inferred?
- Can process findings influence test selection?
- Are simulations reproducible from immutable inputs?
- How are improvements measured across cycles?