Industry 4.0 and its Implementation: a Review

Zhang, Caiming; Chen, Yong; Hong, Chen; Chong, Dazhi

doi:10.1007/s10796-021-10153-5

Cited by 68 publications

(24 citation statements)

References 122 publications

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“…Data-driven decisions have been at the center of quality management for decades. Many recently revised criteria stress the significance of evidence-based decision making [ 24 , 104 ].…”

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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“…Data-driven decisions have been at the center of quality management for decades. Many recently revised criteria stress the significance of evidence-based decision making [ 24 , 104 ].…”

Section: Methodologiesmentioning

confidence: 99%

“…Smart manufacturing is fully integrated, and collaborative manufacturing frameworks can respond to evolving requirements and conditions in the factory, supply networks, and consumer needs in real-time [ 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

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 IIoT is still an emerging concept that needs to be developed. However, much research and many surveys are being conducted to comprehend the scope of this area and overcome the challenges it is facing [14][15][16][17]. To design efficient IoT-based architectures, the correct blending of the software and hardware modules of the system is necessary.…”

Section: Introductionmentioning

confidence: 99%

Smart Warehouse Management System: Architecture, Real-Time Implementation and Prototype Design

2022

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The world has witnessed the digital transformation and Industry 4.0 technologies in the past decade. Nevertheless, there is still a lack of automation and digitalization in certain areas of the manufacturing industry; in particular, warehouse automation often has challenges in design and successful deployment. The effective management of the warehouse and inventory plays a pivotal role in the supply chain and production. In the literature, different architectures of Warehouse Management Systems (WMSs) and automation techniques have been proposed, but most of those have focused only on particular sections of warehouses and have lacked successful deployment. To achieve the goal of process automation, we propose an Internet-of-Things (IoT)-based architecture for real-time warehouse management by dividing the warehouse into multiple domains. Architecture viewpoints were used to present models based on the context diagram, functional view, and operational view specifically catering to the needs of the stakeholders. In addition, we present a generic IoT-based prototype system that enables efficient data collection and transmission in the proposed architecture. Finally, the developed IoT-based solution was deployed in the warehouse of a textile factory for validation testing, and the results are discussed. A comparison of the key performance parameters such as system resilience, efficiency, and latency rate showed the effectiveness of our proposed IoT-based WMS architecture.

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“…The progress of Industry 4.0 has introduced a variety of novel technologies, such as artificial intelligence, cyber-physical systems, cloud computing, and the Internet of Things [1]. These technologies have enormous potential to revolutionize various fields such as manufacturing, transportation and agriculture [2]. Due to the growing demand for healthcare, cyber-physical systems (CPS) have received significant attention from researchers and industry experts [3].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Healthcare Cyber Physical Framework for Encephalitis Diagnosis Based on Information Fusion and Soft-Computing Techniques

Gupta

Singh

2022

New Gener. Comput.

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Viral encephalitis is a contagious disease that causes life insecurity and is considered one of the major health concerns worldwide. It causes inflammation of the brain and, if left untreated, can have persistent effects on the central nervous system. Conspicuously, this paper proposes an intelligent cyber-physical healthcare framework based on the IoT-fog-cloud collaborative network, employing soft-computing technology and information fusion. The proposed framework uses IoT-based sensors, electronic medical records, and user devices for data acquisition. The fog layer, composed of numerous nodes, processes the most specific encephalitis symptomrelated data to classify possible encephalitis cases in real time to issue an alarm when a significant health emergency occurs. Furthermore, the cloud layer involves a multi-step data processing scheme for in-depth data analysis. First, data obtained across multiple data generation sources are fused to obtain a more consistent, accurate, and reliable feature set. Data preprocessing and feature selection techniques are applied to the fused data for dimensionality reduction over the cloud computing platform. An adaptive neuro-fuzzy inference system is applied in the cloud to determine the risk of a disease and classify the results into one of four categories: no risk, probable risk, low risk, and acute risk. Moreover, the alerts are generated and sent to the stakeholders based on the risk factor. Finally, the computed results are stored in the cloud database for future use. For validation purposes, various experiments are performed using real-time datasets. The analysis results performed on the fog and cloud layers show higher performance than the existing models. Future research will focus on the resource allocation in the cloud layer while considering various security aspects to improve the utility of the proposed work.

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Industry 4.0 and its Implementation: a Review

Cited by 68 publications

References 122 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

Smart Warehouse Management System: Architecture, Real-Time Implementation and Prototype Design

An Intelligent Healthcare Cyber Physical Framework for Encephalitis Diagnosis Based on Information Fusion and Soft-Computing Techniques

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