Next Generation Smart Manufacturing and Service Systems using Big Data Analytics

  in Special Issue   Posted on November 2, 2017

Information for the Special Issue

Submission Deadline: Sun 31 Dec 2017
Journal Impact Factor : 2.623
Journal Name : Computers and Industrial Engineering
Journal Publisher:
Website for the Special Issue: https://www.journals.elsevier.com/computers-and-industrial-engineering/call-for-papers/next-generation-smart-manufacturing-and-service-systems
Journal & Submission Website: https://www.journals.elsevier.com/computers-and-industrial-engineering

Special Issue Call for Papers:

Call for Papers: Next Generation Smart Manufacturing and Service Systems using Big Data Analytics

Introduction & Scope

Manufacturing and services industries are now dealing with increasingly massive amount of datasets in short time due to adoption of internet of things (IoT), sensors for asset monitoring, weblogs, social media feeds, product and parts tracking and others. Storing big datasets is not new for these industries but gathering actionable and manageable insights from the data is often lacking. This is also phrased by researchers as ‘data rich and information poor’. Big data analytics refers to the capability of organisations for systematic and computational analysis of big data sets, popularly characterised by 5Vs, i.e. volume, velocity, variety, veracity and value adding.

Big data analytics (BDA) has the potential to transform and advance manufacturing and service systems in future. It can help industries in making informed decisions such as better forecast for products, performance management across multiple manufacturing and service units, improving the quality of products and services, providing greater visibility into operations, understanding the customer preferences and buying patterns, real time manufacturing process and asset condition monitoring, product design, customer service and like these others. Furthermore, supply chain management in service and manufacturing sectors have also added to the complexity in presence of big data.

Inception of Web 2.0 together with increasing growth in social media and networks has empowered consumers to make better decisions. Furthermore, differences in product offerings by competing organisations are closed at an increasing pace to win battle for customers. BDA is also critical to the success of Industry 4.0, a German government initiative, which promotes the integration of IT, manufacturing and operational systems. The benefits of using BDA is enormous but its adoption in many organisations is still in nascent stage.

This special issue will focus on publishing original research papers dealing with big data analytics conceptualisation, theoretical knowledge expansion and its real world application in advancing manufacturing or service systems. This issue particularly welcomes research papers on the topic of analysing open data or publically available large datasets for improving manufacturing/service operations. Submissions involving real world case studies are encouraged. We are inviting original manuscripts in the following (but not limited to) topics:

  • Predictive models for better forecasting, condition monitoring, manufacturing defects identification and remediation
  • 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 visualisation framework for logistics, manufacturing and service operations
  • RFID-enabled real time decision making in manufacturing/service environments
  • Big data analytics for product lifecycle management and innovation
  • Big data analytics for supply chain resilience and risk management

Submission Guidelines

Manuscripts should be submitted via Elsevier Editorial System http://ees.elsevier.com/caie/. Please make sure you select “Special Issue” as Article Type and “SI: Big Data Analytics” as Section/Category. Manuscripts should not have been previously published nor be currently under consideration for publication elsewhere., To prepare your manuscript, please follow the format provided in EES webpage referred to above under “Guide for Authors”.Papers will be reviewed according to the rigorously review process followed by Computers and Industrial Engineering.

Important dates:

Manuscript submission deadline: 31st December 2017
Final manuscript submissions to publisher: 30th September 2018
Expected Publication: January 2019

Managing Guest Editor:

Dr Nagesh Shukla,
Senior Lecturer
School of Systems, Management and Leadership
Faculty of Engineering and Information Technology
University of Technology Sydney
Sydney, Australia 2007
Email: nagesh.shukla@uts.edu.au

Guest Editors:

Prof Manoj Kumar Tiwari
Professor
Department of Industrial and Systems Engineering
Indian Institute of Technology, Kharagpur
West Bengal, India 721302
Email: mkt09@hotmail.com

Prof Ghassan Beydoun
Professor (Deputy Head of School, Research)
School of Systems, Management and Leadership
Faculty of Engineering and Information Technology
University of Technology Sydney
Sydney, Australia 2007
Email: Ghassan.Beydoun@uts.edu.au

Closed Special Issues