A Pilot Study of Industry 4.0 Asset Interoperability Challenges in an Industry 4.0 Laboratory

Jepsen, Sune Chung; Mørk, Thomas Ingemann; Hviid, Jakob; Worm, Torben

doi:10.1109/ieem45057.2020.9309952

Cited by 25 publications

(11 citation statements)

References 9 publications

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“…and Operation Technologies (Sensors, Actuates, Microcontrollers, SCADA, PLCs, etc.) interact [ 55 , 76 , 77 , 78 , 79 ] and the International Organization for Standardization ISO 22400 is a standard describing KPIs in manufacturing [ 19 , 28 , 29 , 80 ]. It focuses on performance measures that serve as the foundation for achieving continuous operational performance improvement in manufacturing through key performance indicators (KPIs) based on various measurements derived from the context of the operation [ 81 , 82 , 83 ].…”

Section: Methodologiesmentioning

confidence: 99%

“…The American National Standard ANSI/ISA-95 [ 30 , 62 , 79 ] contains standards defining various production and automation components. The bar is entitled “Integration of the Enterprise-Control System” [ 30 , 62 ], and the title reads as how to incorporate enterprise/business systems with production and control systems [ 65 ].…”

Section: Methodologiesmentioning

confidence: 99%

“…In this section, we will discuss a few critical challenges that need to be addressed. In the manufacturing sector, the latest wave of internet technology such as cloud, IoT, big data, robotics, and cyber–physical systems has allowed the manufacturing industry to generate a vast array of business data that will bring new challenges, particularly cybersecurity [ 79 , 119 , 120 , 121 ]. These challenges are discussed in Table 5 [ 49 , 122 , 123 ].…”

Section: Research Challenges Opportunities Scope Of Future Work and Implication For Practitionersmentioning

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%

Section: Methodologiesmentioning

confidence: 99%

Section: Research Challenges Opportunities Scope Of Future Work and Implication For Practitionersmentioning

confidence: 99%

See 1 more Smart Citation

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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“…If properly provided and managed, these many heterogeneous vertical and horizontal integrations can enable CPSs to leverage the many advances in industrial systems, big data, AI/ML and cloud computing systems. This way, the seamless integration needs advocated by leading technology providers, vendors, and end-users [17] can be fulfilled.…”

Section: Application Integration Dealing With the Coordination And Cooperationmentioning

confidence: 96%

“…CPSs are typically embedded into a more complex system via interfaces, so modularity (plug and play) and autonomy are important enablers to adapt the upgrades and reconfigurations effectively, in accordance with rapidly changing customer needs [24]. Trustworthy interoperability both at the vertical and horizontal level is critical for setting up Industry 4.0 operations [17].…”

Section: Application Integration Dealing With the Coordination And Cooperationmentioning

confidence: 99%

The Interoperability Challenge: Building a Model-Driven Digital Thread Platform for CPS

Margaria

Chaudhary

Guevara

et al. 2021

Lecture Notes in Computer Science

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With the heterogeneity of the industry 4.0 world, and more generally of the Cyberphysical Systems realm, the quest towards a platform approach to solve the interoperability problem is front and centre to any system and system-of-systems project. Traditional approaches cover individual aspects, like data exchange formats and published interfaces. They may adhere to some standard, however they hardly cover the production of the integration layer, which is implemented as bespoke glue code that is hard to produce and even harder to maintain. Therefore, the traditional integration approach often leads to poor code quality, further increasing the time and cost and reducing the agility, and a high reliance on the individual development skills. We are instead tackling the interoperability challenge by building a model driven/low-code Digital Thread platform that 1) systematizes the integration methodology, 2) provides methods and techniques for the individual integrations based on a layered Domain Specific Languages (DSL) approach, 3) through the DSLs it covers the integration space domain by domain, technology by technology, and is thus highly generalizable and reusable, 4) showcases a first collection of examples from the domains of robotics, IoT, data analytics, AI/ML and web applications, 5) brings cohesiveness to the aforementioned heterogeneous platform, and 6) is easier to understand and maintain, even by not specialized programmers. We showcase the power, versatility and the potential of the Digital Thread platform on four interoperability case studies: the generic extension to REST services, to robotics through the UR family of robots, to the integration of various external databases (for data integration) and to the provision of data analytics capabilities in R.

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Designing and Evaluating Interoperable Industry 4.0 Middleware Software Architecture: Reconfiguration of Robotic System

Jepsen,

Worm

2023

Lecture Notes in Computer Science

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A Pilot Study of Industry 4.0 Asset Interoperability Challenges in an Industry 4.0 Laboratory

Cited by 25 publications

References 9 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

The Interoperability Challenge: Building a Model-Driven Digital Thread Platform for CPS

Designing and Evaluating Interoperable Industry 4.0 Middleware Software Architecture: Reconfiguration of Robotic System

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