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advanced·Finance & FP&A·3 hours

Process Mining Readiness: Event Log Specification

A complete event-log specification with case ID rationale, per-activity extraction logic, data-quality trap list, and the first five analyses to run.

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Recommended modelClaude Sonnet 4.6

Long context for multi-document FP&A analysis

What you need to fill in

[process, e.g., purchase-to-pay][tool, e.g., Celonis/open-source PM4Py][describe what you know]

Tools used

ClaudeCelonisPython

The prompt

"We want to run process mining on our [process, e.g., purchase-to-pay] process in [tool, e.g., Celonis/open-source PM4Py]. Systems involved and their tables/logs: [describe what you know] Please: 1. Define the event log spec: case ID choice (and why), mandatory activities with their timestamp sources, and attributes worth extracting (amount, vendor, plant, user) 2. Write the extraction logic per activity (which table/field marks 'PO created', 'goods receipt', 'invoice posted', 'payment cleared') 3. List data quality traps: missing timestamps, batch-job users, timezone mixing, deleted documents 4. Propose the first 5 analyses to run and the concrete question each answers"
Open in Claude

Sample output

**Case ID:** PO line item (EBELN+EBELP) — invoice-level cases fragment multi-PO invoices; header-level hides line splits. **Activities** | Activity | Source | Timestamp | |---|---|---| | PO created | EKKO | AEDAT + creation time from CDHDR | | Goods receipt | MSEG (BWART 101) | CPUDT/CPUTM | | Invoice posted | RBKP | CPUDT | | Payment cleared | BSAK | AUGDT ⚠ date only — day precision | **Traps:** AUGDT has no time (order same-day events explicitly); batch user 'RFC_BATCH' masks real actor — join CDHDR. **First analyses:** maverick buying rate; touchless invoice %; approval bottleneck by plant; payment-term leakage; rework loops (PO changes after GR).

Try with these tools

claudeclaude codechatgpt
financeprocess-miningprocure-to-paydata-engineering

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