A Big-Data-based platform of workers’ behavior: Observations from the field

Guo, Shengyu; Ding, Lieyun; Luo, Hanbin; Jiang, Xue

doi:10.1016/j.aap.2015.09.024

Cited by 88 publications

(28 citation statements)

References 44 publications

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“…It could be beneficial for future research to extend the application of this approach to more SC and operational management such as fraud detection (Miroslav et al, 2014) and behaviour-based safety analysis (Guo et al, 2016).…”

Section: Resultsmentioning

confidence: 99%

“…Product R&D Bae and Kim, 2011;Do, 2014;Lei and Moon, 2015;Opresnik and Taisch, 2015;Zhang et al, 2017 Production planning & control Chien et al, 2014;Krumeich et al, 2016;Zhang et al, 2017;Shu et al, 2016;Dai et al, 2012;Zhong et al, 2016;Helo and Hao, 2017 Quality management Krumeich et al, 2016;Zhang et al, 2017; Maintenance & diagnosis Shu et al, 2016;Guo et al, 2016;Kumar et al, 2016;Zhang et al, 2017;Wang et al, 2015 Warehousing Storage assignment Chuang et al, 2014;Li, Moghaddam, et al, 2016;Tsai and Huang, 2015;Chiang et al, 2011;Chiang et al, 2014 Order picking Ballestín et al, 2013;Chuang et al, 2014; Inventory control Alyahya et al, 2016;Hofmann, 2015;Huang and Van Mieghem, 2014;Lee et al, 2016;Stefanovic, 2015 Logistics and transportation Cui et al, 2016;Li et al, 2015;Shi and Abdel-Aty, 2015;StAubin et al, 2015;Toole et al, 2015;Xia et al, 2016;Yu and Abdel-Aty, 2014;Zangenehpour et al, 2015;…”

Section: Manufacturingmentioning

confidence: 99%

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Big data analytics in supply chain management: A state-of-the-art literature review

Nguyen

Zhou

Spiegler

et al. 2018

Computers & Operations Research

375

238

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Abstract:The rapidly growing interest from both academics and practitioners in the application of big data analytics (BDA) in supply chain management (SCM) has urged the need for review of up-to-date research development in order to develop a new agenda. This review responds to the call by proposing a novel classification framework that provides a full picture of current literature on where and how BDA has been applied within the SCM context. The classification framework is structurally based on the content analysis method of Mayring (2008) techniques are employed to develop these models? The discussion tackling these four questions reveals a number of research gaps, which leads to future research directions.

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Section: Resultsmentioning

confidence: 99%

Section: Manufacturingmentioning

confidence: 99%

Big data analytics in supply chain management: A state-of-the-art literature review

Nguyen

Zhou

Spiegler

et al. 2018

Computers & Operations Research

375

238

View full text Add to dashboard Cite

show abstract

“…Big Data accident modeling is a suite of techniques employing data‐mining to perform a deep dive into accident‐causing analyses to help people better predict future accidents, and also better perceive the present accident. For example, camera‐based behavior analysis technology , a branch of image recognition research, can assist with the discovery of abnormal behavior and extract featured images from video sequences. The opportunities of the “tools” aspect are analyzed as follows: Due to the limitation of data statistics and the analysis method, the traditional accident‐causing modeling may ignore or simplify some key factors, and Big Data will change the safety data collection, mining, and analysis methods, achieving analysis on the entire sample collection of safety data, thus revealing the accident‐causing mechanism more objectively. Traditional accident modeling focuses on causality analysis and explanations of an accident (explanatory type accident model), whereas the Big Data accident‐causing modeling pays more attention to the analysis of the relationship between a safety phenomenon and safety data. Traditional accident modeling always uses qualitative analysis, whereas Big Data accident modeling can uncover potential factors that contribute to the likelihood of accidents, such as frequency, relevance, locale, and timeliness.…”

Section: Opportunities Brought By the New Paradigmmentioning

confidence: 99%

A new paradigm for accident investigation and analysis in the era of big data

Huang

Wang

et al. 2017

Process Safety Progress

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The advent of the era of Big Data has spawned a new research paradigm and has transformed the outlook of numerous fields in science and engineering. Similarly, as one of the important fields of safety science, the area of accident investigation also has great opportunities for leveraging Big Data to its advantage. With this in mind, in this article, the influencing factors of accident investigation were analyzed. Then, the definition of Safety‐related Big Data (SRBD) was analyzed, and a four‐layer pyramidal structure consisting of SRBD, Safety Information, Safety Law, and Safety Knowledge was constructed. Based on this, the conceptual model of the accident investigation paradigm based on SRBD was proposed. Moreover, the opportunities offered by the proposed new paradigm were argued from three aspects, which are “tools,” “inputs,” and “constraints.” Last, the proposed paradigm was applied in a case study. Results show that the proposed paradigm can provide a novel method for accident investigation and analysis. The presented paper aims to explore the great expectations for accident investigation in the era of Big Data. © 2017 American Institute of Chemical Engineers Process Process Saf Prog 37:42–48, 2018

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“…A Big-Data-based platform was used to automatically obtain scenes reflecting the unsafe behaviors of workers from intelligent video surveillance (Guo et al 2016). Construction phases could be identified through the Gantt chart according to the time when workers' unsafe behavior manifested.…”

Section: Automatic Data Collection Mechanismmentioning

confidence: 99%

“…The use of intelligent video surveillance helped improve the productivity and safety on construction sites (e.g. Aguilar, Hewage 2013;Teizer et al 2013) as it can automatically obtain scenes reflecting workers' unsafe behaviors (Guo et al 2016). The development of data mining algorithms also supports the analysis and utilization of massive data.…”

Section: Introductionmentioning

confidence: 99%

A real-time control approach based on intelligent video surveillance for violations by construction workers

Guo

Xiong

Gong

2018

Journal of Civil Engineering and Management

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Abstract. The unsafe behavior of workers is the main object of construction safety management, in which violations require increased attention due to their pernicious consequences. However, existing studies have merely discussed violations separately from unsafe behaviors. To respond quickly to workers' violations on site, this study proposes a real-time control approach based on intelligent video surveillance. First, scenes reflecting unsafe behaviors are automatically acquired through camera-based behavior analysis technology. Meanwhile, the time corresponding to the construction phase is recorded. Second, the temporal association rule model of worker's unsafe behavior is constructed, and the rule "construction phase→unsafe behavior" is determined by the Apriori algorithm to identify target behaviors necessary for critical control in different construction phases. Finally, statistical process control is used to find the trends of violations with frequency and mass characteristics through the dynamic monitoring of target behavior. In addition, real-time alerts of these unsafe acts are produced simultaneously. A pilot study is conducted on the cross-river tunnel project in Wuhan city, Hubei, China, and the violations related to construction machineries is proven to be controllable. Thus, the proposed approach promotes behavioral safety management on construction since it effectively controls workers' violations by real-time monitoring and analysis.

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A Big-Data-based platform of workers’ behavior: Observations from the field

Cited by 88 publications

References 44 publications

Big data analytics in supply chain management: A state-of-the-art literature review

Big data analytics in supply chain management: A state-of-the-art literature review

A new paradigm for accident investigation and analysis in the era of big data

A real-time control approach based on intelligent video surveillance for violations by construction workers

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