A big data approach for logistics trajectory discovery from RFID-enabled production data

By Ray Y. Zhong, George Q. Huang, Shulin Lan, Q. Dai, Xu Chen, T. Zhang. 2015

435 citations recorded by the source corpus.

Subfields: Building and Construction, Food Science, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/4bb4bac39c052576ddd008f28a6d67d1e4a32b44

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In Building and Construction

In Industrial and Manufacturing Engineering

Citations in the corpus

Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives (2016, background): “Manufacturing sector is being flooded by a huge amount of data since various sensors, electronic devices, and digital machines are used in production lines, shop floors, and factories (Zhong et al., 2015).”
The Role and Impact of Industry 4.0 and the Internet of Things on the Business Strategy of the Value Chain—The Case of Hungary (2018, background): “0, tools that generate data and create big data are essential, including sensors, RFID chips, 3D scanners, cameras, and robots [56].”
Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities (2020, background): “In summary, big data analytics techniques can deal with a collection of large and complex datasets that are difficult to process and analyze using traditional techniques [46].”
Blockchain applications in supply chains, transport and logistics: a systematic review of the literature (2019, background): “…platforms (Tan et al. 2017) are at the heart of this transformation, the development and adoption of hardware technologies such as IoT (Ben-Daya, Hassini, and Bahroun 2017) and RFID (Ngai et al. 2010) feed the required data into these platforms (e.g., Zhong et al. 2015; Hopkins and Hawking 2018).”
A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0 (2020, background): “Zhong et al. (2015) pro-pose a big data approach to forecasting logistics trajectories using RFID-enabled production data.”
Big Data Analytics for Physical Internet-based intelligent manufacturing shop floors (2017, methodology): “However, the characteristics of RFID-enabled logistics data bring several challenges to mining the implicit knowledge (Zhong et al. 2015).”
System architecture for blockchain based transparency of supply chain social sustainability (2020, methodology): “For example, a big data approach can be used to discover the meaningful and useful patterns based on IoT-captured data, such as logistics trajectories [57] and standard operation times [60].”
A Digital Twin-Based Approach for Designing and Multi-Objective Optimization of Hollow Glass Production Line (2017, background): “use of sensors and on-line measuring equipment [11] are making it possible to reuse simulation models during both pre-production and production phases, now with real context data rather than estimated or historical data as input [12], [13].”
Big data analytics in supply chain management: A state-of-the-art literature review (2017, methodology): “Notably, the visualization model is rarely considered as the main focus of a study (Zhong et al., 2016; Zhong et al., 2015), but is commonly used as a complement to other advanced data-mining models (Shan and Zhu, 2015; Zhang et al.”
Smart logistics based on the internet of things technology: an overview (2020, background): “The perceived and interactive logistics data exhibits these characteristics: an increase of the amount of data, coexistence of structural data and unstructured data, incomplete, inaccurate or redundant noisy data, and scattered data with valuable information (Zhong et al. 2015).”
Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system (2019, methodology): “Using a real-time digital copy of physical system (Grieves 2014) can predict performance of multiple designs earlier, reduce reliance on expensive and multiple physical testing, optimise for maximum performance and cut-down design time (Zhong et al. 2015).”