A Proposed Business Improvement Model Utilizing Machine Learning: Enhancing Decision-Making and Performance
Keywords:
Business improvement model, Machine learning, Support Vector Machine (SVM), Decision-making, PerformanceAbstract
This paper presents a proposed business improvement model that leverages Support Vector Machine (SVM) algorithms to enhance decision-making and performance in organizations. The integration of machine learning techniques into business processes has gained significant attention due to its potential for optimizing operations and achieving better outcomes. In this study, we focus on SVM, a powerful supervised learning method known for its ability to handle complex classification and regression tasks. The proposed business improvement model adopts a systematic methodology, beginning with the collection and preparation of data to guarantee the accuracy and usefulness of input data. In order to analyse the data and create predictive models that support decision-making in various commercial situations, SVM is then used. The model can be modified to solve various problems, including resource allocation, demand forecasting, risk assessment, and consumer segmentation. Using real-world company data from various industries, we ran experiments to verify the usefulness of the suggested approach. The outcomes show how SVM-based decision-making has a major impact on organisational performance. Businesses can make wise decisions that result in cost reductions, efficiency gains, and increased customer satisfaction by utilising the patterns and insights discovered by SVM. We also discuss the difficulties and factors involved in applying machine learning models in commercial contexts, such as data protection, interpretability, and model maintenance. By conducting this study, we hope to add to the growing body of knowledge on the application of machine learning to business improvement strategies and offer useful advice for businesses looking to use these methods to improve their decision-making and overall performance.
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