Real-time data-driven monitoring in job-shop floor based on radio frequency identification

Cao, Wei; Jiang, Pingyu; Lü, Peng; Liu, Bin; Jiang, Kaiyong

doi:10.1007/s00170-017-0248-7

Cited by 21 publications

(11 citation statements)

References 43 publications

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“…Various authors use the Internet of Things to obtain information on the status of the production system and to make it available to employees, corresponding to the monitoring level (e.g. Alexopoulos et al 2018;Brintrup, Ranasinghe, and McFarlane 2010;Bonci, Pirani, and Longhi 2016;Cao et al 2017;Davies, Coole, and Smith 2017;Hwang et al 2017;Mayr et al 2018;Mousavi and Siervo 2017;Saez et al 2018;Zhong et al 2015;Zhuang, Liu, and Xiong 2018). Tao et al (2017) and Zhong et al (2015Zhong et al ( , 2016 suggest using cloud computing to make information related to the production system available.…”

Section: Foundationsmentioning

confidence: 99%

Impacts of Industry 4.0 technologies on Lean principles

Rosin

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Lamouri

et al. 2019

International Journal of Production Research

318

205

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Industry 4.0 is increasingly being promoted as the key to improving productivity, promoting economic growth and ensuring the sustainability of manufacturing companies. On the other hand, many companies have already partially or fully implemented principles and tools from the Lean management approach, which is also aimed at improving productivity. While the two approaches use very different strategies, they share some common principles. The objective of this article is to highlight the links between the principles and tools proposed by Industry 4.0 and those proposed by the Lean management approach, with a particular focus on how some of Industry 4.0's technologies are improving the implementation of Lean principles, depending on the technologies' capability levels. As such, this study aims to provide a characterisation of the impacts of Industry 4.0 technologies on Lean principles according to targeted capability levels. The results obtained show strong support for Industry 4.0 technologies for Just-in-time and Jidoka, but little or no support for waste reduction and People and Team work. There is, therefore, a clear need to pursue the deployment of Lean management while improving certain Lean principles using Industry 4.0 technologies.

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Section: Foundationsmentioning

confidence: 99%

Impacts of Industry 4.0 technologies on Lean principles

Rosin

Forget

Lamouri

et al. 2019

International Journal of Production Research

318

205

View full text Add to dashboard Cite

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“…Either the existing reviews aim to determine the benefits of implementing enabling technologies on the enterprise level, or they study the application of a selected set of enabling technologies on a detailed level. For example, using RFID for items tracing [12], cloud robotics for route planning [13] or Internet-of-Thing for warehousing [14].…”

Section: Related Work and Research Gapsmentioning

confidence: 99%

“…In their platform, data-driven models used a predictive planning tool to support decision-making. In some other studies, the term data-driven is used to describe the aim of the research but there is little effort to clarify the data-driven concept in the text, e.g., [12,28,29].…”

Section: Research Purpose Motivation and Article Structurementioning

confidence: 99%

“…Several studies reviewed these enabling technologies on a high level in connection with concepts such as smart manufacturing [9], smart logistics [10] and Industry 4.0 [11]. On the other hand, there are studies that looked in to this domain in more details by investigating the application of data-driven technologies in one specific area such as tracing [12], route planning [13] or warehousing [14]. However, the literature has a gap in addressing the application of data-driven enabling technologies in production logistics from a systematic point of view, covering all the activities in a PL system.…”

Section: Introductionmentioning

confidence: 99%

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A Systematic Review on Technologies for Data-Driven Production Logistics: Their Role from a Holistic and Value Creation Perspective

2021

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A data-driven approach in production logistics is adopted as a response to challenges such as low visibility and system rigidity. One important step for such a transition is to identify the enabling technologies from a value-creating perspective. The existing corpus of literature has discussed the benefits and applications of smart technologies in overall manufacturing or logistics. However, there is limited discussion specifically on a production logistics level, from a systematic perspective. This paper addresses two issues in this respect by conducting a systematic literature review and analyzing 142 articles. First, it covers the gap in literature concerning mapping the application of these smart technologies to specific production logistic activities. Ten groups of technologies were identified and production logistics activities divided into three major categories. A quantitative share assessment of the technologies in production logistics activities was carried out. Second, the ultimate goal of implementing these technologies is to create business value. This is addressed in this research by presenting the “production logistics data lifecycle” and the importance of having a balanced holistic perspective in technology development. The result of this paper is beneficial to build a ground to transit towards a data-driven state by knowing the applications and use cases described in the literature for the identified technologies.

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“…Without up-to-date information, it is impossible to make accurate shop-floor decisions. Therefore, real-time acquisition data technology has caused the attention of many researchers and some methods are provided, such as bar-coding based technology [9], Radio Frequency Identification (RFID) technology [10], automatic identification technology, etc.…”

Section: ) the Real-time Acquisition Data Technologymentioning

confidence: 99%

Dynamic Evaluation Method of Machining Process Planning Based on Digital Twin

et al. 2019

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Process evaluation is widely accepted as an effective strategy to improve product quality and shorten its development cycle. However, there has been very little research on how to evaluate the process plan with the dynamic change of the machining condition and uncertain available manufacturing resources. This paper proposes a novel process evaluation method based on digital twin technology. Three core technologies embodied in the proposed method are illustrated in details: 1) real-time mapping mechanism between the collected data in machining and the process design information; 2) construction of the digital twin-based machining process evaluation (DT-MPPE) framework; and 3) process evaluation driven by digital twin data. To elaborate on how to apply the proposed method to the reality, we present a detailed implementation process of the proposed DT-MPPE method for the key parts of the marine diesel engine. Meanwhile, the future work to completely fulfill digital twin-based smart process planning for complex products is discussed. INDEX TERMS Digital twin, process planning, process evaluation, mapping mechanism, digital twin data.

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Real-time data-driven monitoring in job-shop floor based on radio frequency identification

Cited by 21 publications

References 43 publications

Impacts of Industry 4.0 technologies on Lean principles

Impacts of Industry 4.0 technologies on Lean principles

A Systematic Review on Technologies for Data-Driven Production Logistics: Their Role from a Holistic and Value Creation Perspective

Dynamic Evaluation Method of Machining Process Planning Based on Digital Twin

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