ERP事件日志：用于KPI时间序列提取与表征的规范形式
ERP Event Logs: A Canonical Form for KPI Time-Series Extraction and Characterization
- SAP SE（SAP公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出将ERP事务转化为规范事件日志，以统一提取数量、时长和比率三类KPI，实现跨客户比较，并发现可预测性排序，预训练模型在比率和时长上媲美经典基线。
AI中文摘要：
企业资源规划（ERP）系统将操作记录为分散在数百个规范化关系表中的事务。从该模式中提取运营关键绩效指标（KPI）需要对几乎每个指标进行单独的、定制的连接操作。我们证明，事件日志——一种从相同表派生的规范（案例、活动、时间戳）表示——将这种异构性压缩为一个扁平结构，从中KPI族在一次映射后即可简化为可重用操作。利用在共享活动本体下从数百个SAP S/4HANA客户构建的标准化事件日志，我们定义了三个流程派生KPI族（数量、时长和比率），并针对两个常见的端到端组织流程（订单到现金和采购到付款）在客户系统间统一提取它们。我们对比率进行归一化，并筛选序列的最小覆盖率，以支持不同规模客户之间的比较。然后，我们使用直接从规范形式派生的描述符表征跨行业和租户的异构性，再分析所得的KPI时间序列。同一流程在客户和行业间执行方式异构，但这种共享表示使得销售侧和采购侧信号（包括提前期和订单到达）能够直接比较。利用这种共享表示，我们表征了哪些KPI族是可预测的，并发现跨两个流程和高度异构的客户群中，从数量到时长再到比率的重复排序；预训练时间序列基础模型（Chronos）在比率和时长序列上与经典基线（如ARIMA和ETS）具有竞争力，而经典模型在数量上仍保持优势。
英文摘要：
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.
