In today's changing business climate, strategic business process optimization is critical for firms to promote innovation, improve operational efficiency, and keep a competitive advantage.
Challenges like complexity of interconnected processes, resistance to change from employees and stakeholders, inadequate data and insights for decision-making. In this manuscript, Robust Variation Physics-Informed Neural Network based prediction of Strategic Business Process (SBP-RVPNN-EWO) is proposed. This study utilizes data from the Reimbursement Process Dataset and employs preprocessing technique. The Prediction task focuses on to recover productivity, decrease budgets and increase buyer gratification using Robust Variation Physics-Informed Neural Networks (RVPNN). The Enhanced Walrus Optimization (EWO) is introduced for optimizing RVPNN for accurate business process. The proposed method is implemented and analyzed performance metrics likes accuracy, precision, recall. The proposed method attains 20.78%, 17.98% and 25.67% high accuracy more than existing methods like Strategic Business Process with Particle Swarm Optimization (SBP-PSO), Strategic Business Process with Artificial Neural Network and Strategic Business Process with Convolutional Neural Network respectively.
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