Maturity level determination and optimization model of D&A system based on support vector machine and multi-objective programming

Abstract

In today's era of fragmented information, good data management is essential for companies. Optimizing data integration and analysis (D&A) is crucial to unleashing the full potential of data value. An evaluation was conducted on Intercontinental Freight Company's (ICM) D&A system, with three key assessments identified: importance, connectivity, and availability nature. Via multi-level fuzzy evaluation metrics, key points that affect the system's development were determined. Multiple regression lines were used to analyze relationships between people, process, and technology. Potential performance was measured via average growth in business profits, and an index of 11 variables was created to measure the system's maturity. A growth phase distribution model was developed utilizing a support vector machine and tested on a sample of 50 US ports. The optimization model, based on multi-purpose functions, maximizes efficiency and can be seen by comparing factors before and after port optimization. This experiment proves the effectiveness of the D&A system development evaluation model.