Data-driven smart manufacturing

Tao, Fei; Qi, Qinglin; Liu, Ang; Kusiak, Andrew

doi:10.1016/j.jmsy.2018.01.006

Cited by 1,225 publications

(539 citation statements)

References 55 publications

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“…The advances in the IoT and big data technologies have contributed to transform today's manufacturing paradigm to smart manufacturing (Tao, Qi, Liu, & Kusiak, 2018). With the ability of data collection from multiple stages in food supply chains, the IoT has made it possible for creating a more transparent, sustainable, and efficient food manufacturing (Astill et al, 2019).…”

Section: Food Supply Chain Managementmentioning

confidence: 99%

Utilization of text mining as a big data analysis tool for food science and nutrition

Tao

Yang

Feng

2020

Comp Rev Food Sci Food Safe

166

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Big data analysis has found applications in many industries due to its ability to turn huge amounts of data into insights for informed business and operational decisions. Advanced data mining techniques have been applied in many sectors of supply chains in the food industry. However, the previous work has mainly focused on the analysis of instrument‐generated data such as those from hyperspectral imaging, spectroscopy, and biometric receptors. The importance of digital text data in the food and nutrition has only recently gained attention due to advancements in big data analytics. The purpose of this review is to provide an overview of the data sources, computational methods, and applications of text data in the food industry. Text mining techniques such as word‐level analysis (e.g., frequency analysis), word association analysis (e.g., network analysis), and advanced techniques (e.g., text classification, text clustering, topic modeling, information retrieval, and sentiment analysis) will be discussed. Applications of text data analysis will be illustrated with respect to food safety and food fraud surveillance, dietary pattern characterization, consumer‐opinion mining, new‐product development, food knowledge discovery, food supply‐chain management, and online food services. The goal is to provide insights for intelligent decision‐making to improve food production, food safety, and human nutrition.

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Section: Food Supply Chain Managementmentioning

confidence: 99%

Utilization of text mining as a big data analysis tool for food science and nutrition

Tao

Yang

Feng

2020

Comp Rev Food Sci Food Safe

166

View full text Add to dashboard Cite

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“…2 shows the evolution of data in manufacturing systems, similar integration for other systems can be assumed. [41] Big data collected have to be processed and applied to improve system performance. This data consists of different type of parameters with different quality and form because several sensors and sources are applied.…”

Section: Big Data and Data Processingmentioning

confidence: 99%

Industry 4.0: Some Challenges and Opportunities for Reliability Engineering

Farsi

Zio

2019

Int J Reliabl Risk Safe Theory App

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According to the development of Industry 4.0 and increase the integration of digital, physical and human worlds, reliability engineering must evolve for addressing the existing and future challenges about that. In this paper, the principle of Industry 4.0 is presented and some of these challenges and opportunities for reliability engineering are discussed. New directions for research in system modeling, big data analysis, health management, cyber-physical system, human-machine interaction, uncertainty, jointly optimization, communication, and interfaces are proposed. Each topic can be investigated individually, but this paper summarizes them and prepared a vision about reliability engineering for consideration and discussion by the interested scientific community.

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“…The MIoT data is featured with large volume, heterogeneous types (i.e., structured, semi-structured, unstructured) and is generated in a real-time fashion. The analytics of MIoT data can bring many benefits, such as improving factory operation and production, reducing machine downtime, improving product quality, enhancing supply chain efficiency and improving customer experience [3]- [5]. However, there are also many challenges in data analytics in MIoT in the different phases of the whole life cycle of data analytics.…”

Section: Introductionmentioning

confidence: 99%

“…There are several surveys on data analytics in manufacturing industry. The work of [5] proposes a data-driven smart manufacturing framework and provides several application scenarios based on this conceptual framework. The necessities of big data analytics in smart manufacturing are summaried in [6].…”

Section: Introductionmentioning

confidence: 99%