Special Issue "Digital Transformation in Manufacturing Industry Ⅱ"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: 28 February 2021.

Special Issue Editor

Dr. Radmehr P. Monfared
Website
Guest Editor
Senior Lecturer in Digital Manufacturing and Automation, Loughborough University, Loughborough, United Kingdom
Interests: industrial digitalisation; robotics and automation; simulation and predictive behaviour of systems; business analytics
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

During the last decade, the world has been transformed by new digital technologies. The current digital transformation has vast potential to change consumers’ lives and meet their new expectations, add further value to data-driven businesses, and create unique societal benefits.

The rate of technological innovation is exponential, to the point that change is becoming almost a new normal now, with many innovations having passed the proof-of-concept stage and entered the investment phase.

Although it has become apparent that industrial digitalization is potentially capable of improving the productivity and predictability of businesses, manufacturing industries are lagging behind and failing to keep pace with other sectors such as finance and media. This is probably due to the need for proven technological robustness and demonstratable benefits.

This Special Issue is aimed at disseminating advanced research in the theory and application of digitalization in the manufacturing industries (also known by some experts as Industry 4.0).

The scope of this Special Issue is focused on new digital technologies that can have a direct impact on various lifecycle stages of manufacturing industries. These could include marketing, design, production, quality control, resource management, supply chain, product and process tracking, and product recycling.

The potential themes include but are not limited to the application of the following technologies within manufacturing industries: cloud computing, big data, Internet of Things (IoT), blockchain in manufacturing, virtual engineering and digital twining (virtual reality and augmented reality), simulation, machine learning for analytics and predictions in industrial businesses, and industrial cybersecurity.

We also invite articles investigating the human role in digitalized manufacturing industries, the need for resource upskilling, and new business models for manufacturing industries.

 

Dr. Radmehr P. Monfared
Guest Editor

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. Applied Sciences is an international peer-reviewed open access semimonthly 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 1800 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.

Published Papers (1 paper)

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Research

Open AccessArticle
Customer-Oriented Quality of Service Management Method for the Future Intent-Based Networking
Appl. Sci. 2020, 10(22), 8223; https://doi.org/10.3390/app10228223 - 20 Nov 2020
Abstract
The rapid development and spread of communication technologies is now becoming a global information revolution. Customers have a need for communication services, which could be flexibly configured in accordance with their Quality of Experience (QoE) requirements. Realizing the close connection between customer experience [...] Read more.
The rapid development and spread of communication technologies is now becoming a global information revolution. Customers have a need for communication services, which could be flexibly configured in accordance with their Quality of Experience (QoE) requirements. Realizing the close connection between customer experience and profitability, the service provider has been placing more and more attention on customer experience and QoE. The traditional quality of service management method based on SLA (Service Level Agreement) is not sufficient as a means to provide QoE-related contracts between service providers and customers. The current SLA method is mostly limited and focused on technical aspects of QoS (Quality of Service). Furthermore, they do not follow on the network the principles and semantic approach to the QoS specification for a communication service using QoE parameters. In this paper, we propose a customer-oriented quality of service management method for future IBN (Intent-Based Networking). It is based on a new QoE metric on a scale from 1 to 5, which allows one to take into account the commercial value of e-services for customers. Based on this approach, the network configuration and functionality of network equipment automatically changes depending on customer requirements. To implement the new method of service quality management, an algorithm for routing data packets in the network was developed, taking into account the current load of the forecast path. The algorithm of billing system functioning in conditions of customer-oriented quality management in telecommunication networks has been created. To investigate the effectiveness of the proposed method of service quality management with the traditional SLA method, we developed a simulation network model with the implementation of two approaches. By conducting a simulation, it was determined that the proposed method gives an average gain of 2–5 times for the criterion of the number of customers who require high quality of experience of the service. Full article
(This article belongs to the Special Issue Digital Transformation in Manufacturing Industry Ⅱ)
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