发表机构
SAP（SAP）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出企业流程控制塔，集成观察、诊断与治理，用梯度提升模型在合成IDoc数据上实现宏F1 0.883的异常路由诊断，优于规则基线。
AI 中文摘要
在大型企业资源规划（ERP）系统中，企业集成流量通过电子数据交换（EDI）和中间文档（IDoc）进行传输，而围绕该流量的运营工具仍然分散。状态记录面向技术受众编写，异常处理逐文档进行，流程健康状况通过长列表手动读取。本工作提出了一种企业流程控制塔，使信息技术（IT）团队能够从单一运营层观察、诊断和改进业务流程。该设计将流程健康观察、技术状态文本的语义翻译、具有模型可解释性的机器学习异常诊断、分组恢复操作以及关键绩效指标整合在一个治理框架内。所提出的框架在包含84,000条记录、跨越十二个月的合成IDoc事件数据集上进行了评估，其中15,120条错误记录用于训练一个五类修复路由模型，该模型将每个异常映射到标准恢复操作。一个复现当前生产启发式规则的基于规则的路由器作为主要基线。所提出的梯度提升诊断引擎在保留测试分区上达到宏平均F1分数0.883，而基于规则的路由器为0.742，同时基于Shapley的归因将每个路由决策的驱动因素暴露给操作员。结果源自合成数据，并作为内部一致性演示而非真实世界验证呈现。其贡献是一种集成工具设计，将诊断和治理置于与观察同一层面。
英文摘要
Enterprise integration traffic in large Enterprise Resource Planning (ERP) systems move through Electronic Data Interchange (EDI) and the Intermediate Document (IDoc), and the operational tools that surround that traffic remain fragmented. Status records are written for a technical audience, exception handling is performed one document at a time, and process health is read manually from long lists. This work proposes an enterprise process control tower that lets an Information Technology (IT) team observe, diagnose, and improve business workflows from a single operational layer. The design links process health observation, semantic translation of technical status text, machine-learning diagnosis of exceptions with model explainability, grouped recovery actions, and key performance indicators inside one governance framework. The proposed framework is evaluated on a synthetic IDoc event dataset of 84,000 records spanning twelve months, of which 15,120 error records are used to train a five-class remediation-routing model that maps each exception to a standard recovery action. A rule-based router that reproduces current production heuristics serves as the primary baseline. The proposed gradient-boosted diagnosis engine reaches a macro-averaged F1 score of 0.883 on the held-out test partition, against 0.742 for the rule-based router, while Shapely-based attributions expose the drivers of each routing decision to operators. Results are derived from synthetic data and are presented as an internal-consistency demonstration rather than real-world validation. The contribution is an integrated tool design that places diagnosis and governance on the same plane as observation.
Comments10 pages
