Big Data in product lifecycle management

By Jingran Li, Fei Tao, Ying Cheng, Lian Zhao. The International Journal of Advanced Manufacturing Technology, 2015

481 citations recorded by the source corpus.

Subfields: Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/1c45bfad2900f553364b9f877ca31e1ab3a310a1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Industrial and Manufacturing Engineering

1. Qinglin Qi
2. Fei Tao
3. He Zhang
4. Hichem Snoussi
5. Yang Chen
6. Meina Qiao
7. Tian Wang
8. Jiangfeng Cheng

Citations in the corpus

• The adoption of digital technologies in supply chains: Drivers, process and impact (2021, background): “Some scholars investigated how to use IoT and big data for waste management (Gu et al., 2017) and provided the potential applications of digital technologies in sustainable product life cycle management (He et al., 2015; Qian et al., 2017; Li et al., 2015).”
• Big Data Analytics in Operations Management (2018, background): “It was predicted early that big data analytics would revolutionize supply chain design (Waller and Fawcett 2013) and may change product lifecycle management in the supply chain (Li et al. 2015).”
• Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain (2017, background): “Others point out how big data helps in efficiency of design, production, intelligence and service processes employed in product life cycle management (PLM) [42].”
• A fast and robust convolutional neural network-based defect detection model in product quality control (2017, background): “With the development of big data [13, 14] and the enhancement of computer computing ability, deep learning strategies have been successfully applied in various fields.”
• Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison (2018, background): “[16], which are also featured with 4Vs, i.”
• A Survey on Information and Communication Technologies for Industry 4.0: State-of-the-Art, Taxonomies, Perspectives, and Challenges (2019, methodology): “all the phases of product life cycle present their own categories of input data to be processed with Big Data tools and produce output data for decision making (we refer to [123]”
• Digital Twin Shop-Floor: A New Shop-Floor Paradigm Towards Smart Manufacturing (2017, background): “3) Construction and management of SDTD Technologies involve data cleaning, data integration, data fusion, two-way mirrors between physical and virtual data, shop-floor big data [40], etc.”
• The impact of big data on world-class sustainable manufacturing (2015, background): “Therefore, there is need for BDA adoption within WSSCM. Garetti and Taisch (2012), in their review of sustainable manufacturing, highlight the role of data and BDA, suggesting that there is need for methods that will be able to process large amounts of data related to environmental, social, and econ”
• Digital twin-driven product design, manufacturing and service with big data (2017, background): “At any stage of the product lifecycle, a large amount of data is collected, processed, and used, thus big data is formed [5].”
• Assessing relations between Circular Economy and Industry 4.0: a systematic literature review (2019, background): “…business models through integrative frameworks (Chiappetta Jabbour et al. 2017) or for particular types of companies (Soroka et al. 2017), and (iv) to gather or manage data on the lifecycle of products (J. Li et al. 2015) or implementing smart manufacturing practices (Kusiak 2018) (see Table 5).”