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Smart Service Systems, Operations Management, and Analytics

Proceedings of the 2019 INFORMS International Conference on Service Science

  • Conference proceedings
  • © 2020


  • Presents recent advances in using smart service systems, operations management, and/or analytics in service science research
  • Highlights emerging technology and state-of-the-art applications for service science
  • Includes service case studies written by scholars and practitioners worldwide

Part of the book series: Springer Proceedings in Business and Economics (SPBE)

Included in the following conference series:

Conference proceedings info: INFORMS-CSS 2019.

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About this book

This volume offers state-of-the-art research in service science and its related research, education and practice areas. It showcases recent developments in smart service systems, operations management and analytics and their impact in complex service systems. The papers included in this volume highlight emerging technology and applications in fields including healthcare, energy, finance, information technology, transportation, sports, logistics, and public services. Regardless of size and service, a service organization is a service system. Because of the socio-technical nature of a service system, a systems approach must be adopted to design, develop, and deliver services, aimed at meeting end users‘ both utilitarian and socio-psychological needs. Effective understanding of service and service systems often requires combining multiple methods to consider how interactions of people, technology, organizations, and information create value under various conditions. The papers in this volume present methods to approach such technical challenges in service science and are based on top papers from the 2019 INFORMS International Conference on Service Science.

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Table of contents (35 papers)

Other volumes

  1. Smart Service Systems, Operations Management, and Analytics

Editors and Affiliations

  • Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, USA

    Hui Yang

  • Division of Engineering and Information Science, Pennsylvania State University, Malvern, USA

    Robin Qiu

  • Department of Supply Chain Management, Rutgers, The State University of New Jersey, Piscataway, USA

    Weiwei Chen

About the editors

Hui Yang is an Associate Professor in the Harold and Inge Marcus Department of Industrial and Manufacturing Engineering at The Pennsylvania State University, University Park, PA. Dr. Yang's research interests focus on sensor-based modeling and analysis of complex systems for process monitoring, process control, system diagnostics, condition prognostics, quality improvement, and performance optimization.
Robin Qiu is a tenured full Professor of Information Science, teaches a variety of courses including Predictive Analytics, Management Science, Business Process Management, Decision Support Systems, Project Management, Enterprise Integration, Enterprise Service Computing, Software Engineering, Web-based Systems, Distributed Systems, Computer Architecture/SOA, Computer Security, Web Security, Operations Research, and System Engineering. Dr. Qiu’s research interests include Big Data, Data/Business Analytics, Smart Service Systems, Service Science, Service Operationsand Management, Information Systems, and Manufacturing and Supply Chain Management.
Weiwei Chen is an Associate Professor of Supply Chain Management in Rutgers Business School – Newark and New Brunswick at Rutgers University. Dr. Chen’s current research interest lies in operations and finance interface, as well as supply chain operations planning and scheduling. He also works on simulation and randomized global optimization methodologies. He has extensive experience working with businesses and public sectors to improve strategic decisions and operational efficiencies using data analytics. He has taught courses in optimization modeling, operations analysis, and lean six sigma.

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