Special Issue "Application of Big Data Analysis and Advanced Analytics in Sustainable Production Process"
Deadline for manuscript submissions: closed (30 June 2021).
Interests: data mining; industrial artificial intelligence; probabilistic OR
Interests: industrial artificial intelligence; machine learning; data and process mining; smart manufacturing
We are living in the big data era in which large amounts of information are continuously created, registering all kinds of events such as the ones generated in the design, planning, control, and execution of manufacturing, logistics, and supply chain processes. Furthermore, currently, a major concern for manufacturing organizations is the environmental responsibility that has become an integral aspect of the way their production processes are designed and executed.
Sustainable production processes involve the problems of traditional production processes but with the additional goal of reducing environmental impact and minimizing waste generation. Some of the approaches of sustainable production processes are energy reduction, emissions reduction, water use reduction, and waste generation reduction. The challenges of sustainable product design, process design, energy planning, and operational principles can be enhanced and optimized by utilizing big data analysis and the recently developed related information technologies, such as the Internet of Things (IoT), cloud, and cyber–physical space (CPS) technologies.
This Special Issue on “Application of Big Data Analysis and Advanced Analytics in Sustainable Production Process” aims to present a collection of state-of-the-art solutions to the different types of sustainable production processes using big data analysis as well as advanced analytics such as data mining, process mining, machine learning, and deep learning. Potential topics include, but are not limited to, the following:
- (Data-driven) manufacturing processes;
- (Data-driven) logistics and supply chain processes;
- (Data-driven) green manufacturing;
- (Data-driven) lean manufacturing processes;
- (Data-driven) reconfigurable manufacturing processes;
- (Data-driven) sustainable energy plant processes;
- (Data-driven) factory energy management systems (FEMS);
- Big data analytics and architectures for sustainable production processes;
- IoT, cloud, and CPS systems for sustainable production processes;
- Data warehouses for sustainable production processes;
- Data mining and process mining for sustainable production processes;
- Fault detection and system diagnostics for sustainable production processes;
- AI, machine learning, and deep learning for sustainable production processes;
- Theory and methods of big data analysis and advanced analytics;
- Data-driven applications to sustainable production processes;
- Case studies on sustainable production processes in industry.
Papers submitted to this Special Issue are expected to provide an original contribution, proposing new solutions/frameworks, improvements to existing solutions, and new applications with recent technologies. Contributions may take the form of (i) a research article, (ii) a review paper, or (iii) a case study or an industry paper.
Prof. Dr. Sun Hur
Prof. Dr. Jae-Yoon Jung
Dr. Josue Obregon
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- big data analysis
- advanced analytics
- green manufacturing
- production process
- data mining
- process mining
- machine learning
- deep learning