Affiliation:
1. Universiti Sains Malaysia, Malaysia
2. University of Oulu, Finland
3. Ideal Vision Integration Sdn. Bhd., Malaysia
Abstract
This chapter explores the integration of inventory management and machine learning, offering a comprehensive guide to harnessing analytical dashboards for improved decision-making. At the core of modern inventory management lies the challenge of balancing stock levels to meet demand without incurring excess or shortfall. Using classification algorithms, this chapter explores how machine learning techniques can revolutionize inventory control, making predictions more accurate and operations more efficient. It provides a detailed walkthrough of implementing these machine learning models, emphasizing their practical benefits in forecasting and classification tasks within inventory management. Furthermore, it demonstrates how Power BI can be leveraged to visualize inventory data, enabling stakeholders to gain insights into stock trends, performance metrics, and the overall health of the supply chain. By integrating machine learning outputs into Power BI dashboards, businesses can achieve a holistic view of their inventory dynamics, facilitating informed decision-making processes.