Workshop 3:
Artificial Intelligence Techniques for Industry Data Science
Digital manufacturing strategies are gaining ground as manufacturers adopt big data and analytics to improve operational effectiveness, time-to-market, new product development and increase product quality and reliability. Traditional, large-volume manufacturers often have one goal in mind—reduce costs associated with making high volumes of a single item by producing as many parts as possible in the shortest amount of time. That business model works for high-volume products, particularly if automation can be deployed, but it ignores innovation and the competitive edge created by digital manufacturing. The convergence of software and flexible manufacturing makes quick-turn prototyping and low-volume, on-demand production of custom parts fast and economical. In addition, digital manufacturers capture massive amounts of data across their business from the first customer engagement, through the sales and quoting process, and throughout the manufacturing and fulfillment processes. To take full advantage of all the information, understand the complexity of a high product mix and optimize business, companies need sophisticated analytics that cross business domains.
Data science, a vast discipline encompassing expertise in sophisticated math, engineering, and computer science techniques, typically involves using advanced analytics to mine insights out of data—data that informs business decisions, accelerates growth and improves customer experiences. Much of this data originates from the digital thread that starts with a customer’s first interaction with a digital manufacturer’s website and connects front-end software to automated fulfillment. The applications of data science to digital manufacturing are limitless. Real-time understanding of operational processes enables higher throughput and rapid pivots. Analyzing shop-floor sensor data lets manufacturers make compensating tweaks when quality veers from tolerance. Customer sentiment can be graphed to customized design and production. Supply-chain forecasts and delivery decisions can be optimized down to the individual stockkeeping unit and location level. This makes digital manufacturing one of the drivers of Industry 4.0, the union of physical and digital systems through complex software algorithms, artificial intelligence, machine learning and the network of smart industrial devices that comprise the industrial internet of things (IIoT).
So, how will digital manufacturers use data science in the future? Machine learning will be critical as companies race to find even better ways to serve customers. Analytics and machine learning will help digital manufacturers identify what capabilities customers will value including price, lead times, quality offerings and other options. Manufacturers that use analytics will enjoy a critical edge that will lead to growing their business.

Artificial Intelligence, Data Science, Digital Manufacturing, Industry Data Science, Internet of Things, Machine Learning.

Prof. Kannimuthu Subramaniyam, Karpagam College of Engineering, India 

Kannimuthu Subramaniyam is currently working as Professor in the Department of Computer Science and Engineering at Karpagam College of Engineering, Coimbatore, Tamil Nadu, India. He is also an In-Charge for the Center of Excellence in Algorithms. He is an IBM Certified Cybersecurity Analyst. He did PhD in Computer Science and Engineering at Anna University, Chennai. He did his M.E (CSE) and B.Tech (IT) at Anna University, Chennai. He has more than 15 years of teaching and industrial experience. He is the recognized supervisor of Anna University, Chennai. Two PhD candidate is completed their research under his guidance. He is now guiding 7 PhD Research Scholars. He has published 57 research articles in various International Journals. He published 2 books ("Artificial Intelligence" & “LinkedList Demystified-A Placement Perspective” and 3 Book Chapters (WOS / Scopus Indexed). He is acting as mentor / consultantfor DeepLearning.AI, Hubino, MaxByte Technologies and Dhanvi Info Tech, Coimbatore. He is the expert member for AICTE Student learning Assessment Project (ASLAP). He has presented a number of papers in various National and International conferences. He has visited more than 100 Engineering colleges and delivered more than 131 Guest Lectures on various topics. He is the reviewer for 50 Journals and 3 Books. He has successfully completed the consultancy project through Industry-Institute Interaction for ZF Wind Power Antwerpen Ltd., Belgium. He has received funds from CSIR, DRDO and ISRO to conduct workshops and seminars. He has completed more than 610 Certifications (41 Specializations and 4 Professional Certifications) in Coursera, Hackerrank and NPTEL on various domains. He has guided a number of research-oriented as well as application-oriented projects organized by well-known companies like IBM. He is actively involving in setting up lab for Cloud Computing, Big Data Analytics, Open-Source Software, Internet Technologies etc., His research interests include Artificial Intelligence, Data Structures and Algorithms, Machine Learning, Big Data Analytics, Virtual Reality & Blockchain. One of his research works is incorporated SPMF Open-Source Data Mining Tool. He Conferred   Second Best Team in NLP Challenge as part of FIRE 2019 conference. He secured first Position in NLP Challenge as part of FIRE 2018 Conference.