A review of quality 4.0: definitions, features, technologies, applications, and challenges

Sader, Sami; Husti, István; Daróczi, Miklós

doi:10.1080/14783363.2021.1944082

Cited by 100 publications

(73 citation statements)

References 20 publications

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“…Manufacturers must transition to the “Quality 4.0” concept to integrate new technologies to analyze the data and assess quality [ 96 , 97 ]. Quality 4.0 is a term that refers to the increasing digitization of industry, which employs advanced technologies to improve the quality of manufacturing and services [ 25 , 40 , 98 , 99 , 100 ].…”

Section: Methodologiesmentioning

confidence: 99%

“…Quality 4.0 is a reference point for Industry 4.0 [ 50 , 99 ]. Quality 4.0 requires the digitalization of the management of quality.…”

Section: Methodologiesmentioning

confidence: 99%

“…Industry 4.0 allows the company to gain real-time visibility of quality indicators such as production efficiency, supplier performance, engineering manufacturing, and customer support with the aid of ICT developments in Industry 4.0–such as advanced analytics, AI, ML, and IoT [ 4 , 72 ]. A core element of Quality 4.0 is the rapid and efficient data collection from multiple sources to empower informed and agile decision making [ 70 , 92 , 99 , 101 , 105 ].…”

Section: Methodologiesmentioning

confidence: 99%

“…Analytics: Industry 4.0’s advanced technologies enable us to gather massive data from the production plant and apply the analytics tools to measure the quality matrices. ML and AI insights allow prescriptive analytics to forecast loss and clarify what steps to boost the results [ 70 , 92 , 99 , 101 , 105 ].…”

Section: Methodologiesmentioning

confidence: 99%

“…In contrast, OT refers to technology such as smart devices, sensors, edge devices used in manufacturing plants. Leveraging contact can make it possible to obtain feedback in real-time or near real-time [ 70 , 92 , 99 , 101 , 105 ].…”

Section: Methodologiesmentioning

confidence: 99%

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

Tambare¹,

Meshram²,

Lee

et al. 2021

Sensors

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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.

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Section: Methodologiesmentioning

confidence: 99%

“…Quality 4.0 is a reference point for Industry 4.0 [ 50 , 99 ]. Quality 4.0 requires the digitalization of the management of quality.…”

Section: Methodologiesmentioning

confidence: 99%

Section: Methodologiesmentioning

confidence: 99%

Section: Methodologiesmentioning

confidence: 99%

Section: Methodologiesmentioning

confidence: 99%

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

Tambare¹,

Meshram²,

Lee

et al. 2021

Sensors

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Is designed data collection still relevant in the big data era?

Anderson‐Cook

2023

Quality & Reliability Eng

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The excitement and buzz around big data (BD) has been so prevalent that it seems BD has become thought of as a panacea for solving all challenges. But there are in fact limitations to what BD can do, which has led to missed opportunities and potentially suboptimal problem-solving and decision-making. Hence the answer to the question posed in the title is an emphatic 'Yes!' The intentional strategic use of designed data collection (DDC) represents an opportunity to elevate how studies involving BD are planned, executed, and interpreted to deliver more satisfying solutions. In this paper, we outline eleven opportunities for integrating DDC strategies, including statistical design of experiments (DoE) and sampling techniques, with BD at all stages of the study. There are opportunities for building background understanding of the problem and improving the quality of the data via strategic planning before acquiring BD. In addition, incorporating DDC into the collection and processing of the actual BD, and strengthening conclusions or augmenting with supplementary designed data after BD also represent key opportunities to increase the impact of BD in the era of Industry 4.0 and Quality 4.0.

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Supporting the Selection of Quality Tools Using Neural Networks

Starzyńska,

Rojek

2023

Lecture Notes in Networks and Systems

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A review of quality 4.0: definitions, features, technologies, applications, and challenges

Cited by 100 publications

References 20 publications

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

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

Is designed data collection still relevant in the big data era?

Supporting the Selection of Quality Tools Using Neural Networks

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