Big Data Analytics in Smart Manufacturing Systems

截稿时间：2019/01/05

https://blog.hwr-berlin.de/mim2019/

· Prof. Dr. Jie Zhang, Donghua University, China

· Prof. Dr. Wenjun (Chris) Zhang, University of Saskatchewan, Canada

· Dr. Junliang Wang, Donghua University, China

Emerging technology breakthroughs in a number of fields, including robotics, artificial intelligence and quantum computing, etc., promote the development of different strategies (such as Industrial 4.0, Made in China 2025) and the fourth industrial revolution. The industrial big data (i.e. data of machine state, data of product quality and data of system bottleneck state, etc.) can be captured by using embedded ubiquitous sensors and multiple intelligent machines, which is featured by 3V-3M, i.e. volume, velocity, variety, multi-source, multi-noise, and multi-dimension. Enabled by the parallel computing, deep learning and other information science technologies, big data analytics has the potential to transform and advance manufacturing systems. It empowers a new paradigm for performance management and provides the predictive analytics to improve production quality, stability and efficiency. In recent years, the big data analytics has played a significant role in the production scheduling, equipment utilization enhancement, cycle time prediction and customer demand forecasting, as a new hotspot in the operation of manufacturing systems.

The session chairs invite researchers, scholars and decision-makers from academia, industry, and government to contribute theoretical and applied research papers in areas including but not limited to the following topics:

Predictive models for better forecasting, condition monitoring, manufacturing defects identification and remediation

Big data analytics for resilient engineering in manufacturing systems

Big data analytics for supply chain management in service or manufacturing sector

Big data analytics for smart logistics in service or manufacturing sector

Big data analytics for smart operations in service or manufacturing sector

Big data analytics for product design and development

Big data analytics for operations/service improvement using customer reviews

Open data analytics for consumer behaviour/preference elicitation and analysis

Big data analytics related to product tracking for efficiency improvements

How BDA can support SMEs to be competitive in local and/or global markets?

Big data analytics for product lifecycle management and innovation

For author guidelines, please refer to www.ifac-control.org. Manuscripts should be submitted electronically using Symposium Manuscript Management System (CMMS). All papers must be prepared in a two-column format in accordance with the IFAC manuscript style. Please use the official IFAC instructions and template to prepare your contribution as full-length draft paper and submit it online by December 15, 2018. Submission details are available on the symposium website. All submissions must be written in English. All papers that conform to submission guidelines will be peer-reviewed by IPC members. The corresponding author submits the paper online (pdf format) as an invited session paper. Submission as an invited paper requires the invited session code: sgmwi. Several international journals are associated with the MIM 2019 for publication of special issues.

STEP1  

点击如下链接，进入PaperPlaza Conference Manuscript Management System，选择MIM 2019，点击右侧“Submit a contribution to MIM 2019”进行论文提交。

https://ifac.papercept.net/conferences/scripts/start.pl#MIM16

STEP2

点击Invited Session Paper右侧的Submit 进入论文详细内容填写页面。

STEP3  

按照论文的相应信息依次填写并提交。请在Code 处填写本Session的邀请码：sgmwi