OIM (Operations & Information Management) Research Group
The OIM research group aims to design innovative service/manufacturing systems and optimize their operations using engineering techniques as well as management methods. Its main research areas include financial engineering, service science, operations research & supply chain management, data mining, and business process & knowledge management. The related engineering and management methodologies are mathematical programming, graph theory, heuristics, simulation, Markov chain, queuing theory, reliability, time series analysis, regression, real analysis, numerical analysis, artificial intelligence, statistics, system analysis, value management, and expert systems.
Operations Research and Supply Chain Management
The area of operaproblems tions research and supply chain management includes modeling of new and developing algorithms using mathematical programming and theories of probability and statistics. In particular, research on global supply chain management is becoming more important than before because of globalization. Novel applications are also emphasized in the areas of energy system, eco system, health and medical system, and bioinformatics.
Service Science, Management, and Engineering
Services science seeks to use expertise in industrial engineering and its related fields such as technology, management, mathematics, and social science to improve the performance of service business. Our main research emphasis is placed on the engineering approach to new service development, service operation and management, service improvement and innovation, and customer value management. Special attention has been placed to knowledge-intensive service industries with high impact, including healthcare service, information and communication service, financial service, and logistics service.
Data Mining & Business Intelligence
Data mining seeks to develop new theories, algorithms, and applications for extracting meaningful knowledge from engineering and business data. This area is challenging when the data is large-scale, high dimensional and heterogeneous. The meaningful knowledge can be expressed by predicted target values, classification, clustering, ranking, and association rules. Statistical and mathematical methods as well as artificial intelligence and expert systems are dealt with. Major application areas include quality prediction of products, fraud detection, churn analysis, market segmentation, and financial volatility prediction. Business intelligence includes data mining as well as business process management and knowledge management.
Financial Engineering focuses on innovative development of financial markets through quantitative analysis. Its main goal is to create advanced research in developing parsimonious theories and scientific methods for investment and risk management in financial markets. The central areas of research in financial engineering can be grouped into three fields: 1) financial asset pricing, 2) financial risk management, and 3) financial investment management. Researchers in financial engineering strive to create and improve both financial techniques and financial markets of stocks, bonds, and financial derivatives, based on multidisciplinary studies including mathematics, computer science, and operations and management engineering. With a rapid development in financial derivative products and related markets, the application has immensely expanded. Currently fast growing, for instance, are the silver financial markets relating to the aging society and green financial markets for certified emission reduction. In this perspective, financial engineering is expected to become increasingly important.