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 big 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.
Topics of interest
- 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)
Principal Guest Editor
Yunqiang Yin (yinyq@uestc.edu.cn),
School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, China
Co-editors
Feng Chu (feng.chu@univ-evry.fr)
Alexandre Dolgui (alexandre.dolgui@imt-atlantique.fr)
T. C. E. Cheng (edwin.cheng@polyu.edu.hk)
M. C. Zhou (zhou@njit.edu)
生产和分销管理中的大数据分析
生产和分销是供应链中的两个关键运营职能，它们是相互关联的，因为后者只能在生产过程的最后一个任务完成后才开始。这两个与业务有关的问题，无论是单独解决还是综合解决，在过去的五十年里引起了相当大的关注。然而，现有的研究通常假设模型参数，无论是确定的还是不确定的，都是预先定义好的。
我们现在正处在大数据时代。越来越多的公司和组织在运营中采用大数据相关技术，包括信息和通信技术（ICT）、企业资源规划（ERP）系统、云计算、物联网和社交媒体。所有这些基于传感器和计算系统存储和操作大量数据。丰富的可用数据和大数据分析技术为加强生产和分销管理提供了前所未有的机会。如何应用大数据分析技术来支持生产和分销管理不仅至关重要，而且具有挑战性，因为数据往往是异构的和多样化的，并且需要大量的存储和快速的处理。
本期特刊旨在提供一个平台，以促进研究人员和从业者在生产和分销管理的大数据分析方面的互动。我们欢迎在解决与主题相关的重大且动机充分的问题方面，在方法论上的进步或建模创新方面作出重大贡献的论文。
感兴趣的主题：
论文可以是理论的，方法论的，计算的，或者是面向应用的。潜在主题包括但不限于以下内容：
- 识别当前大数据分析技术和策略在生产和分销管理方面的局限性，并提出改进建议；
- 从生产到分销的各个阶段进行数据分析；
- 开发用于生产和分销管理的大据分析的新模型或理论；
- 比较基于经典操作优化和数据驱动的生产和分销管理模型方法；
- 探索不同环境下生产和分销管理的新模式（如工业4.0、绿色制造、绿色物流和最后一公里交付）
首席特邀编辑
Yunqiang Yin (yinyq@uestc.edu.cn),
中国电子科技大学管理与经济学院，成都
联合编辑
Feng Chu (feng.chu@univ-evry.fr)
Alexandre Dolgui (alexandre.dolgui@imt-atlantique.fr)
T. C. E. Cheng (edwin.cheng@polyu.edu.hk)
M. C. Zhou (zhou@njit.edu)