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Complex & Intelligent Systems - Special Issue on

Data-driven Operations Management


Aims and Scope:
We are now in the digital era. More and more companies and organizations are employing digital technologies, including information and communications technology, enterprise resources planning  systems, cloud computing, Internet of things, and social media, in their operations. Operations management is commonly known as the discipline that applies scientifically sound analytical methods to improve the efficiency and effectiveness of operations, which is inherently related to the use of data. 
Against a background of big data, management activities are characterized by high frequency real-time data integration across organizational boundaries, multi-agent decision-making, and so on. Such management attributes in the operations context leads to data-driven operations management, which presents an unprecedented challenge as well as an opportunity for operations management research and practice. 
This special issue aims to promote mutual stimulation and improvement between scholars and entrepreneurs across the fields of big data and operations management. We invite and welcome papers that make impactful contributions in terms of methodological advances and/or modelling innovativeness in addressing significant and well-motivated operations management issues related to the theme. Papers can be theoretical, methodological, computational, or application-oriented. Potential topics of interest include, but are not limited to: 


  • Identifying data sources and constructing data models for operations management;
  • Ascertaining the limitations of current big data analytics techniques and strategies for operations management, and proposing improvements;
  • Developing new theories or models for data-driven operations management;
  • Integrating big data technologies such as data mining and machine learning with classical OR techniques for operations management;
  • Creating new architectures for data-driven operations management;
  • Comparing classical optimization-based and data-driven approaches for operations management modelling and solution; 
  • Exploring new models for data-driven operations management in different contexts (e.g., revenue management, marketing, supply chain management, intelligent manufacturing, healthcare management etc)

Important dates (approx.):
Submission Deadline: June 30，2021
Author notification: August 31，2021
Revised paper submission: October 29, 2021
Final acceptance: December 31, 2021

Lead Guest Editor
Dujuan Wang, Business School, Sichuan University, Chengdu 610064, China; djwang@scu.edu.cn

Guest Editors
Yugang Yu, School of Management, University of Science and Technology of China, Hefei 230026, China; ygyu@ustc.edu.cn
  
T.C.E. Cheng, Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong; edwin.cheng@polyu.edu.hk

Yunqiang Yin, School of Management and Economics, University of Electronic Science and Technology of China, Chengdu 611731, China; yinyq@uestc.edu.cn 

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