Improving Fast Project Delivery using Decision Support Vector Machine with ACO approaches in SAP

One of the most critical issues concerning enterprise resource planning systems like SAP is fast project delivery, where the correct risk forecasting, optimal scheduling, and resource allocation support the timely execution. The earlier methods are generally not effective in selecting features, and in prediction and judgment making are unscalable, so pathetic. This research proposes a hybrid model combining the Decision Support Vector Machine (DSVM) with Ant Colony Optimization (ACO)-based Feature Selection (ACO-FS) to improve the quality of project delivery. The workflow starts with gathering project data from SAP modules, including resource consumption, budget variances, vendor lead times, and risk logs. The resulting data undergoes Z-Score Normalizationto achieve a standard scale across all attributes. Afterward, ACO-FS is used to trim down the most valuable and non-redundant features, effectively downsizing the dimensions without losing information important to project delivery outcomes. The refined feature set is then categorized using DSVM, which classifies the project’s risk categories. Correspondingly, the ACO algorithm is conducive to optimal resource allocation and task scheduling to reduce bottlenecks. The usefulness of the suggested framework is evaluated based on performance measures such as accuracy, precision, recall, F1-score, and computational efficiency. Generally, the integration of DSVM and ACO-FS provides a smart and reasonable decision support system capable of achieving significant improvements in the speed of project execution and asset use, and in business agility in dynamic business structures.