scholarly journals Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 224
Author(s):  
Parkash Tambare ◽  
Chandrashekhar Meshram ◽  
Cheng-Chi Lee ◽  
Rakesh Jagdish Ramteke ◽  
Agbotiname Lucky Imoize
Keyword(s):  
Quality Management ◽  
Internet Of Things ◽  
Performance Measurement ◽  
Industry 4.0 ◽  
Manufacturing Industries ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Manufacturing Companies ◽  
Industrial Standards

The birth of mass production started in the early 1900s. The manufacturing industries were transformed from mechanization to digitalization with the help of Information and Communication Technology (ICT). Now, the advancement of ICT and the Internet of Things has enabled smart manufacturing or Industry 4.0. Industry 4.0 refers to the various technologies that are transforming the way we work in manufacturing industries such as Internet of Things, cloud, big data, AI, robotics, blockchain, autonomous vehicles, enterprise software, etc. Additionally, the Industry 4.0 concept refers to new production patterns involving new technologies, manufacturing factors, and workforce organization. It changes the production process and creates a highly efficient production system that reduces production costs and improves product quality. The concept of Industry 4.0 is relatively new; there is high uncertainty, lack of knowledge and limited publication about the performance measurement and quality management with respect to Industry 4.0. Conversely, manufacturing companies are still struggling to understand the variety of Industry 4.0 technologies. Industrial standards are used to measure performance and manage the quality of the product and services. In order to fill this gap, our study focuses on how the manufacturing industries use different industrial standards to measure performance and manage the quality of the product and services. This paper reviews the current methods, industrial standards, key performance indicators (KPIs) used for performance measurement systems in data-driven Industry 4.0, and the case studies to understand how smart manufacturing companies are taking advantage of Industry 4.0. Furthermore, this article discusses the digitalization of quality called Quality 4.0, research challenges and opportunities in data-driven Industry 4.0 are discussed.

scholarly journals Quality management in industry 4.0 era

2018 ◽  
Vol 8 (2) ◽  
pp. 182-191 ◽  
Author(s):  
Nur Hanifa Mohd Zaidin ◽  
Muhammad Nurazri Md Diah ◽  
Shahryar Sorooshian
Keyword(s):  
Quality Management ◽  
Internet Of Things ◽  
Industry 4.0 ◽  
Manufacturing Industries ◽  
Cyber Physical System ◽  
Academic Researcher ◽  
Technology Advancement ◽  
Key Characteristics ◽  
Manufactured Products ◽  
Manufacturing Sectors

In this era of technology advancement and industrial modernization, the concepts introduced by the German promised a great benefits and widely unexplored opportunities for application in industries. Industry 4.0 concept is one of the heavily discussed topics for academic researcher and practitioners. In this era of consumerism, manufacturing industries are required to manufactured products and services of the highest quality in order to retain competitiveness in the consumers market. The concepts of Smart Factory, Cyber-Physical System as well as Internet of Things and Services offers very capable opportunities and also downside challenges for quality management in manufacturing sectors. Thus, this paper present and discussed the opportunities and challenges in implementation of Industry 4.0 for quality management which triggered by a practical insight of an electronic production and service company in Austria. The findings of the recent study challenges are formed by the three key characteristics of Industry 4.0 which are horizontal, vertical and end-to-end manufacturing integration.

scholarly journals Sustainable Cyber-Physical Production Systems in Big Data-Driven Smart Urban Economy: A Systematic Literature Review

2021 ◽  
Vol 13 (2) ◽  
pp. 751
Author(s):  
Mihai Andronie ◽  
George Lăzăroiu ◽  
Mariana Iatagan ◽  
Iulian Hurloiu ◽  
Irina Dijmărescu
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Literature Review ◽  
Operations Management ◽  
Industry 4.0 ◽  
Production Systems ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Industrial Big Data ◽  
Cyber Physical Production Systems

In this article, we cumulate previous research findings indicating that cyber-physical production systems bring about operations shaping social sustainability performance technologically. We contribute to the literature on sustainable cyber-physical production systems by showing that the technological and operations management features of cyber-physical systems constitute the components of data-driven sustainable smart manufacturing. Throughout September 2020, we performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “sustainable industrial value creation”, “cyber-physical production systems”, “sustainable smart manufacturing”, “smart economy”, “industrial big data analytics”, “sustainable Internet of Things”, and “sustainable Industry 4.0”. As we inspected research published only in 2019 and 2020, only 323 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, we decided upon 119, generally empirical, sources. Future research should investigate whether Industry 4.0-based manufacturing technologies can ensure the sustainability of big data-driven production systems by use of Internet of Things sensing networks and deep learning-assisted smart process planning.

scholarly journals Sustainable, Smart, and Sensing Technologies for Cyber-Physical Manufacturing Systems: A Systematic Literature Review

2021 ◽  
Vol 13 (10) ◽  
pp. 5495
Author(s):  
Mihai Andronie ◽  
George Lăzăroiu ◽  
Roxana Ștefănescu ◽  
Cristian Uță ◽  
Irina Dijmărescu
Keyword(s):  
Decision Making ◽  
Internet Of Things ◽  
Literature Review ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Production Networks ◽  
Production Logistics ◽  
Time Production

With growing evidence of the operational performance of cyber-physical manufacturing systems, there is a pivotal need for comprehending sustainable, smart, and sensing technologies underpinning data-driven decision-making processes. In this research, previous findings were cumulated showing that cyber-physical production networks operate automatically and smoothly with artificial intelligence-based decision-making algorithms in a sustainable manner and contribute to the literature by indicating that sustainable Internet of Things-based manufacturing systems function in an automated, robust, and flexible manner. Throughout October 2020 and April 2021, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “Internet of Things-based real-time production logistics”, “sustainable smart manufacturing”, “cyber-physical production system”, “industrial big data”, “sustainable organizational performance”, “cyber-physical smart manufacturing system”, and “sustainable Internet of Things-based manufacturing system”. As research published between 2018 and 2021 was inspected, and only 426 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 174 mainly empirical sources. Further developments should entail how cyber-physical production networks and Internet of Things-based real-time production logistics, by use of cognitive decision-making algorithms, enable the advancement of data-driven sustainable smart manufacturing.

Industrial 4.0 of Service and Manufacturing in Java-Indonesia: Level of Implementation

2021 ◽  
Vol 13 (1) ◽  
pp. 36
Author(s):  
Dewi Nusraningrum ◽  
Salmi Mohd. Isa ◽  
Dipa Mulia
Keyword(s):  
Operating System ◽  
Operating Systems ◽  
Industry 4.0 ◽  
Sampling Method ◽  
Developed Countries ◽  
Manufacturing Industries ◽  
Manufacturing Companies ◽  
Purposive Sampling ◽  
Service Companies ◽  
The World

The application of industry 4.0 has been doing in many countries in the world today even some developed countries have headed to industry 5.0, nevertheless in Indonesia there are still many companies that have not implemented industry 4.0. This research aims to find out the extent of the implementation of industry 4.0 in Indonesia, especially the industry located on the island of Java.The industry 4.0 aspects as a benchmark of differentiator from previous industrial developments is worth scrutinized to know its application levels in the service and manufacturing industries. Although many industries still combine their operating system between the 4.0 industry and conventional operating systems. The populations are the services and manufacturing companies. The data was obtained by using a questionnaire distributed online to respondents with a purposive sampling method. The data was grouped according to The level of implementation and is centered. The calculation and percentage results demonstrate that the level of implementation of the 4.0 industry with a technology base in service companies and manufacturing companies are at a managed level. It illustrates that the companies being researched have not been fully on the demands of the 4.0 industry.

scholarly journals A Lightweight Slice-Based Quality of Service Manager for IoT

IoT ◽  
2020 ◽  
Vol 1 (1) ◽  
pp. 49-75
Author(s):  
Antonio Oliveira-Jr ◽  
Kleber Cardoso ◽  
Filipe Sousa ◽  
Waldir Moreira
Keyword(s):  
Quality Of Service ◽  
Industry 4.0 ◽  
Farming Systems ◽  
Smart Manufacturing ◽  
Network Slicing ◽  
Wide Range ◽  
The Internet Of Things ◽  
Service Manager ◽  
Digital Farming

Industry 4.0 and digital farming rely on modern communication and computation technologies such as the Internet of Things (IoT) to provide smart manufacturing and farming systems. Having in mind a scenario with a high number of heterogeneous connected devices, with varying technologies and characteristics, the deployment of Industry 4.0 and digital farming solutions faces innovative challenges in different domains (e.g., communications, security, quality of service). Concepts such as network slicing and Software-Defined Networking (SDN) provide the means for faster, simpler, scalable and flexible solutions in order to serve a wide range of applications with different Quality-of-Service (QoS) requirements. Hence, this paper proposes a lightweight slice-based QoS manager for non-3GPP IoT focusing on different use cases and their varying requirements and characteristics. Our focus in this work is on non-3GPP IoT unlicensed wireless technologies and not specifically the end-to-end network slice perspective as described in 5G standards. We implemented and evaluated different QoS models in distinct scenarios in a real experimental environment in order to illustrate the potential of the proposed solution.

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