Value chain collaboration for complicated product innovation and manufacturing excellence by industrial data-driven modeling and optimization

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Aims

Industrial Data-driven value chain digital ecosystem extract insight from manufacturing data systems to enabling intelligent clustering. Data-driven methods such as artificial intelligence, big data analytic and machine learning that learn from data to drive better decisions. According to various data, manufacturing enterprises can uncover patterns and relationships behind the data, which allow decision makers can anticipate outcomes based upon more concrete information than an assumption.

Thus, value chain digital ecosystem illustrates manufacturing enterprises behavior, rather than relying on domain knowledge, decision-makers experience, or subjective intuition alone. Moreover, the interpretable models and results are also important to enhance the efficiency and effectiveness of enterprises collaborations to empower operations excellence and smart manufacturing. Well established machine learning, and mathematical models form decision support systems for production/process engineering must be integrated with data-driven methods for cross-domain knowledge generation and representation. In particular, artificial intelligence and other model-driven methods are main methodologies for considering in problem-oriented industrial management and decision support to empower intelligence excellence.

Nowadays, data-driven methods is rapidly reshaping the strategic framework of manufacturing enterprises in all industries and leading a paradigm shift towards an innovation-based economy based on knowledge, data, and the Internet of Things. This latest industrial revolution provides firms with ample opportunities for sustainability, but it also brings challenges. Industrial companies are facing the challenge of transferring the concept of the sustainable value chain digital ecosystem, Internet of Things, cyber-physical system into real applications, threatening established business models, changing processes of value creation, creating new security risks, and intensifying innovation competition.

Modern industrial production and operation processes are recorded by huge of heterogeneous data including sensor-based data, image-based data, and other numerical and categorical data related design and manufacturing process domain. These data are needed to analyze and extract the key information behind the data and then provide useful information and knowledge for decision making such as yield enhancement, cycle time control, defect inspection, demand forecast, fault detection, product design and predictive maintenance. Additionally, digital twin, which is like a virtual model of a product, process, and service, provide the analysis and monitor information to physical system for understanding the variation. The value chain digital ecosystem of manufacturing enterprises focus on the data management issue for data-driven methods.

Scope

This special issue of the Industrial Management & Data Systems aims to address emergent research issues driven by the needs of intelligent applications in various industries, such as smart manufacturing and service. Empirical studies with technical and/or methodological advances to address realistic issues are encouraged. The scope of this special issue cover the aspects of areas that explore the data science for value chain digital ecosystem of manufacturing enterprises in the context of dual carbon. Submissions of scientific results from experts in academia and industry worldwide are strongly encouraged. The topics include but not limited to, are listed below:

• The data integration, information fusion, and business collaboration for Physical-space, social-space, and information-space
• Scaled Heterogeneous swarm intelligence for cooperation and decision making
• Data driven industrial value chain, especially across value chain coupling and modeling
• Data driven for multi-agent collaboration and value discovery
• Industrial internet-based product integration and innovation
• Industrial internet-based production management and quality enhancement
• Industrial data governance, trusted security exchange, data quality assurance
• Spatial-Temporal correlation extraction and evolution for Industrial Internet
• Deep Sensing and cooperative forecast model with substance Flow and energy transfer
• Data driven-based optimization and decision framework for production and industrial chains

Submission guidelines

All papers must be original, high quality and have not been published, submitted and/or be currently under review elsewhere.

Manuscripts submissions to Industrial Management & Data Systems are made using ScholarOne.

Please make sure you select “Special Issue” as article type and “MIDT” as Section/Category.

Please follow the instructions described in the “Author Guidelines” on the journal webpage.

Submissions will be reviewed according to rigorous standards and procedures through double-blind peer review by at least two qualified reviewers. Accepted papers become the property of the publisher Emerald.

Publication schedule

Deadline for manuscript submissions: 30 November 2023.

Guest Editors

Prof. Runliang Dou (managing guest editor), College of Management and Economics, Tianjin University, China [email protected] 

Prof. Kuo-Yi Lin, School of Business, Guilin University of Electronic Technology, Guilin, China, [email protected]

Prof. Chia-Yu Hsu, Department of Industrial Management, National Taiwan University of Science and Technology, Taiwan, [email protected]

Prof. Mohammad T. Khasawneh, Department of Systems Science and Industrial Engineering, State University of New York at Binghamton, USA, [email protected]