Erp event logs enable uniform extraction and comparison of key business metrics
ERP Event Logs: A Canonical Form for KPI Time-Series Extraction and Characterization
Databases
Summary
Enterprise resource planning (ERP) systems store lots of business data across many tables, making it hard to calculate important metrics consistently. The authors show that converting this complex data into a simple, standard event log with cases, activities, and timestamps lets them calculate key performance indicators (KPIs) like volumes, durations, and rates using the same operations everywhere. They tested this on real ERP data from hundreds of companies, comparing processes like sales and procurement across industries. This common format helps spot differences and forecast patterns in business operations more easily.
What this means in practice
- •For erp analytics teams: Calculate operational KPIs uniformly across diverse ERP installations to benchmark and monitor business processes like sales and procurement.
- •For supply chain managers: Compare lead times and order flows across suppliers and customers by using a standardized event log representation of ERP transactions.
Authors
Sherri Hadian, Adrian Rebmann, Atacan Korkmaz, Gregor Berg, Ulf Brackmann
Abstract
Enterprise resource planning (ERP) systems record operations as transactions spread across hundreds of normalized relational tables. Extracting an operational key performance indicator (KPI) from this schema requires a separate, bespoke join for almost every metric. We show that an event log, a canonical (case, activity, timestamp) representation derived from the same tables, collapses this heterogeneity into one flat structure, from which KPI families reduce to reusable operations after a one-time mapping. Using standardized event logs built from hundreds of SAP S/4HANA customers under a shared activity ontology, we define three families of process-derived KPIs (volumes, durations, and rates) and extract them uniformly across customer systems for two common end-to-end organizational processes, order-to-cash and procure-to-pay. We normalize rates and screen series for minimum coverage to support comparison across customers of very different sizes. We then characterize heterogeneity across industries and tenants using descriptors derived directly from the canonical form, before analyzing the resulting KPI time series. The same process executes heterogeneously across customers and industries, yet this shared representation enables direct comparison of sales- and procurement-side signals, including lead times and order arrivals. Using this shared representation, we characterize which KPI families are forecastable and find a recurring ordering from volumes through durations to rates across both processes and a highly heterogeneous customer base; a pretrained time-series foundation model (Chronos) is competitive with classical baselines such as ARIMA and ETS on rate and duration series, while classical models retain an edge on volumes.