Service Engineering: Data Driven Service Design and Optimization
细分领域
Service design: Data and its processing facilitate modern business models such as sharing or platform operations. How can data improve resource usage and welfare in these businesses?
Service Optimization: Using data to set optimal capacity or design systems that maximize efficiency, reduce wait times, and improve resource allocation across various service industries. How can data be used to set optimal service capacity and design systems that maximize efficiency, reduce wait times, and improve resource allocation across various touchpoints?
Enhanced customer experience: Analytics can help service businesses to better understand their customers' needs and preferences, leading to more personalized and effective ongoing and new services and improved tailored service recommendations.
Information Technology in Support of Service: Data can support the automatic creation, monitoring, and management of business processes, e.g., via highlighting automation opportunities or enhancing the usage of digital twins.
Adoption of Advanced Information Technology in Services: Modern information technology and algorithms can support services, e.g., in high-frequency trading, and AI in support of diagnostics in healthcare, but their adoption often faces difficulties. How can these difficulties be overcome?
Usage of on-line data: Access to real-time data provides businesses with insights into their performance, allowing them to make informed decisions on their operational, tactical, and strategic initiatives.
Applications of AI&ML: Recent algorithms, such as LLM, can overhaul business management, e.g., by improving customer support. How could service business use AI&ML to improve productivity?
Improved risk management in services: By analyzing historical data and identifying potential risks, service businesses can make informed decisions about lending, investing, credit rating and other financial activities.
