International Journal of Production Research

Big Data Analytics in

Production and Distribution Management

Submission deadline: June 30th, 2021

Production and distribution are two key operational functions in a supply chain, which are interrelated as the latter can only start after the last task of a production process is completed. These two operational-related problems, which are solved separately or in an integrated way, have attracted considerable attention in the past five decades. However, existing studies usually assume that model parameters, be they certain or uncertain, are predefined.

We are now in the big data era. More and more companies and organizations are employing big data-related technologies, including information and communications technology (ICT), enterprise resources planning (ERP) systems, cloud computing, Internet of things, and social media, in their operations. All these sensor-based and computing systems store and manipulate massive amounts of data. The abundant available data together with big data analytics techniques offer unprecedented opportunities to enhance production and distribution management. How to apply big data analytics techniques to support production and distribution management is not only vital, but also challenging since data are often heterogeneous and diversified, and require huge storage and speedy processing.

This special issue seeks to provide a platform to facilitate interactions between researchers and practitioners in dig data analytics for production and distribution management. We welcome papers that make impactful contributions in terms of methodological advances or modelling innovativeness in addressing significant and well-motivated issues related to the theme. Papers can be theoretical, methodological, computational, or application-oriented. Potential topics include, but are not limited to, the following:

  • Identifying the limitations of the current big data analytics techniques and strategies for production and distribution management, and proposing improvements;

  • Conducting data analysis at all stages from production to distribution;

  • Developing new models or theories for dig data analytics for production and distribution management;

  • Comparing classical operational optimization-based and data-driven approaches for the models of production and distribution management;

  • Exploring new models for production and distribution management in different contexts (e.g., Industry 4.0, green manufacturing, green logistics, and last-mile delivery)


Lead Guest Editor

  • Yunqiang Yin, School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, 611731, China;


Guest Editors

  • Feng Chu, Laboratory IBISC, Univeristy of Evry Val d’Essonne, France;  

  • Alexandre Dolgui, Department of Automation, Production and Computer Sciences, IMT Atlantique, campus in Nantes, France; 

  • T.C.E. Cheng, Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong;

  • M.C. Zhou, Helen and John C. Hartmann Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA;