Enterprise Process Control Tower for IT-Led Improvement

Abstract

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.