In the 21st century, the service industry is at the center of the industrial structure. The service industry, however, has not had an academic discipline that corresponds to engineering in the manufacturing industry. In 2003, IBM proposed to create a new discipline called services sciences, management and engineering (SSME). This is a multidisciplinary area in which information sciences, operations research (OR), management engineering, social sciences, and other areas overlap. TRL is leading this new, exciting field as a pioneer in services sciences in Japan.
Competency fields
Social Network Analysis
To analyze service systems, it is important to understand the relationships among the people in the organizations. We are utilizing Social Network Analysis (SNA) to quantitatively investigate the complex human relationships with scientific methodologies. In particular, we provide Collaborative Organization Analysis (COA) services with IBM Business Consulting Services. COA evaluates the organizational structure and extracts its characteristics by analyzing an aggregated communication network constructed from huge volumes of email transaction data among hierarchical departments within the enterprise using graph and network theory.
Service Software Engineering (SSE)
Service Software Engineering (SSE) promotes the use of innovative software engineering practices for complex service delivery projects such as systems integration. TRL and the Service Quality Assurance Division visualize the symptoms of problems and their defect patterns in service delivery projects via data analysis and mining. In addition, TRL is currently working on a system that recommends effective countermeasures to be taken to avoid problems.
Services Information
In order to analyze a service business scientifically and make it more efficient, it is important to systematically collect and store information regarding the business, and to take appropriate actions based on analysis of the stored information. TRL is focusing on the analysis parts, and aims at creating value by applying machine learning technologies.
Customer Insight
Innovations in data collection sometimes lead to advanced analyses and deep insights. We are working on experimental activities in marketing, including integrated data analysis of POS data and the tracks of customer movements in stores collected by using RFID tags attached to shopping carts
