Enterprise system tool improves IT workflow error handling accuracy
Enterprise Process Control Tower for IT-Led Improvement
Computational Engineering, Finance, and Science
Summary
Large business computer systems often have many small messages about errors, but these messages are hard to read and fix quickly because they are technical and scattered. The authors created a tool that shows the overall health of these business workflows in one place, explains the errors in easy-to-understand terms, and suggests fixes automatically using machine learning. This tool was tested using fake data and it correctly matched errors to fix actions much better than the old rule-based system. The work combines watching, diagnosing, and governing business processes together to help IT teams improve efficiency.
What this means in practice
- •For it operations teams: Automatically diagnose and route business workflow exceptions to proper recovery actions in large enterprise systems to reduce manual handling.
- •For enterprise software vendors: Integrate explainable machine learning models into ERP monitoring tools to improve error handling accuracy and visibility of decision drivers.$Commercial implications: Enables ERP vendors to offer smarter diagnostic add-ons that improve operational efficiency and transparency for enterprise customers.
Tested on simulated data.
Authors
Shunmukha Sagar Puppala
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.