Theme: From Sensing to Decision-Making
Description:
The manufacturing industry, which is vital to all economies, is being challenged to improve its efficiency across the product life cycle and the value chain. The critical aspect of “smartness” is enabled by advanced data analytics for understanding, prediction, and control of manufacturing systems across the product life-cycle (design analytics, production analytics, and use and post-use analytics) and to the extended networked enterprises. Continuous improvements in sensor technologies, data acquisition systems, and data mining and big data analytics allow the manufacturing industry to effectively and efficiently collect large, rapid, and diverse volumes of data and get valuable insights from this data. Big data analytics is becoming a key competitive differentiator having great potential for converting raw data into information assets for smarter decision making during design, manufacturing, use and post use.
In addition, there is a need for open standards, communication protocols, and high performance distributed computing. This symposium intends to provide a platform for researchers and industry practitioners from manufacturing, information science, and data science disciplines to share their data mining and big-data-analytics-related research results, and practical design or development experiences in the manufacturing industry. It will foster collaboration among academia, industry, and governmental organizations to help improve efficiency, product quality, and sustainability in small and large manufacturing industries.
Research topics
Topics covered by the symposium include, but are not restricted to, the following:

• Infrastructure and algorithms
o Data systems architecture for a digital factory including sensors, security, and the Internet of Things (IoT)
o Collecting, storing, retrieving, and pre-processing big data
o New techniques for building predictive models from data
o Modeling and simulation of manufacturing systems and operations for data analytics
• Applications
o Application of data mining and big data analytics to improve efficiency and productivity of manufacturing processes, and product quality
o Prognostics and health management for manufacturing systems
o Forecasting and predictive maintenance based on customer data
o Case studies, surveys and reports on the impact of data mining and big data analytics in the manufacturing industry
• Standards and protocols
o Standards and protocols for deployment and exchange of analytics solutions: PMML, PFA, MTConnect, CRISP-DM, etc.
o Verification & validation issues related to application of DA in manufacturing
o Techniques for quantifying uncertainty in data and models, and variability in physical processes

This symposium will consist of four sessions:
1. Research Track: New research ideas on the development and application of data analytics techniques
2. Industry Track: Current use of data analytics applied to solve real problems in the industry
3. Keynote Speeches: Invited talks given by leading practitioners and domain experts in industry, government, and academia
4. Manufacturing Data Challenge: A challenge problem for applying DA in manufacturing will be provided, and the best solutions from participants will be selected for presentation.


Important dates

August 31, 2016: Results due for the manufacturing data challenge
Sept 20, 2016: Due date for full symposium papers submission
Oct 20, 2016: Notification of paper acceptance to authors
Nov 5, 2016: Camera-ready of accepted papers
Dec 5-8, 2016 (TBD): Symposium

Symposium Organizers
Dr. Sudarsan Rachuri

Advanced Manufacturing Office
Office of Energy Efficiency and Renewable Energy (EERE)
Department of Energy
E-mail: sudarsan@hq.doe.gov
Phone: +1-202-287-5943


Tina Lee
Systems Integration Division
National Institute of Standards and Technology
Gaithersburg, MD 20899
E-mail: yung-tsun.lee@nist.gov
Phone: +1-301 975 3550


Dr. Ronay Ak
Systems Integration Division
National Institute of Standards and Technology
Gaithersburg, MD 20899
E-mail: ronay.ak@nist.gov
Phone: +1-301 975 8655

Dr. Anantha Narayanan
Department of Mechanical Engineering
University of Maryland
College Park, MD, 20742
Email: anantha@umd.edu
Phone: +1-301 975 4322

Dr. Soundar Srinivasan
Robert Bosch, LLC
4005 Miranda Ave #200
Palo Alto, CA 94304
E-mail: Soundar.Srinivasan@us.bosch.com
Phone: +1-650 320 2980

Dr. Rumi Ghosh
Robert Bosch, LLC
4005 Miranda Ave #200
Palo Alto, CA 94304
E-mail: Rumi.Ghosh@us.bosch.com
Phone: +1-650 565 7459

Dr. Steve Eglash
Stanford Data Science Initiative
Stanford University
Stanford, CA 94305
E-mail: seglash@stanford.edu
Phone: +1-650 721 1637