Safety informatics as a new, promising and sustainable area of safety science in the information age

Wang, Bing; Wu, Chao

doi:10.1016/j.jclepro.2019.119852

Cited by 46 publications

(21 citation statements)

References 77 publications

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“…Some researchers refer to these two ways of thinking as “Safety I" and “Safety II.” [ 9 , 28 ] One such data repository, for example, is the data accumulated by smart devices and machines: the so-called Internet of Things [ 29 ]. The emerging field of safety informatics and “smart safety decision-making” are revolutionizing the role of big data (or “big safety data”) in safety thinking [ 3 , 5 , 30 ]. Another source of information, for example, is the “open web.” [ 31 ] Dasgupta claims that, “curating and managing health and safety related incidents from various sources is an important part of an organization.” (p. 434) Still another source is the paradigm shift occurring in OHS and safety known as Industry 4.0 [ 32 ].…”

Section: Background For Shifts In Safety Thinkingmentioning

confidence: 99%

“…But this backward-looking has not allowed them to predict incidents or identify safety behaviors with high degrees of certainty [ 2 ]. The goal of OHS predictive data analytics, also known as safety informatics or safety intelligence, is not to quantify patterns of errors and system failures, but to sense out hard-to-find archetypes of safety performance [ [3] , [4] , [5] , [6] ]. To reach new levels of effectiveness, OHS professionals need to start “finding Plutos.” This kind of predictive analytical thinking, or the tracing of “weak and potential information” opens opportunities to mine the knowledge hidden in big data systems —a catch-all phrase here for “known systems”—such as we will explore later in this paper [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

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Finding Pluto: An Analytics-Based Approach to Safety Data Ecosystems

Barker

2021

Safety and Health at Work

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This review article addresses the role of safety professionals in the diffusion strategies for predictive analytics for safety performance. The article explores the models, definitions, roles, and relationships of safety professionals in knowledge application, access, management, and leadership in safety analytics. The article addresses challenges safety professionals face when integrating safety analytics in organizational settings in four operations areas: application, technology, management, and strategy. A review of existing conventional safety data sources (safety data, internal data, external data, and context data) is briefly summarized as a baseline. For each of these data sources, the article points out how emerging analytic data sources (such as Industry 4.0 and the Internet of Things) broaden and challenge the scope of work and operational roles throughout an organization. In doing so, the article defines four perspectives on the integration of predictive analytics into organizational safety practice: the programmatic perspective, the technological perspective, the sociocultural perspective, and knowledge-organization perspective. The article posits a four-level, organizational knowledge-skills-abilities matrix for analytics integration, indicating key organizational capacities needed for each area. The work shows the benefits of organizational alignment, clear stakeholder categorization, and the ability to predict future safety performance.

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Section: Background For Shifts In Safety Thinkingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Finding Pluto: An Analytics-Based Approach to Safety Data Ecosystems

Barker

2021

Safety and Health at Work

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“…Mao et al (2019) investigate a case study of an institutional approach to achieve proper control of coastal water pollution about how China realizes SDG 14 by 2030. Wang and Wu (2020) presented a safety informatics study, as a new, promising, and sustainable area of safety science in the information age for students. Athar et al (2019) investigated an inherent safety study for the sustainable process design of process piping at the preliminary design stage.…”

Section: Literature Reviewmentioning

confidence: 99%

Sustainable Development Importance in Higher Education for Occupational Health and Safety Using Egypt Vision 2030 under COVID-19 Pandemic

Mouneer¹

2021

GEP

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Insertion of Sustainable Development Goals (SDGs) into higher education is an important concern. This article aims to address the influences of SDGs in higher and engineering educational system in Egypt. The present study focuses on SDGs 3, 4, 6, 7, 9, and 11. SDG 3 is related to occupational health and SDG 4 is related to higher education development. However, SDGs 6, 7, 9, and 11 are related to both mechanical and environmental engineering, and occupational safety. The study approach involves case risk analysis, investigations, and review for design concepts of occupational health and safety before and after the Coronavirus disease . A number of cross-sectional studies that constitute 125 multidisciplinary research plans (MDRPs) are grouped to monitor and evaluate two main factors which are an influence of involving SDGs into engineering education and selected curriculum of occupational health and safety. Then the results of these research-based studies, which performed over four academic years started from 2017 up to date 2021, are depicted and discussed. These results reveal the importance of interlink between engineering education and the intended SDGs. The latest 25 MDRPs demonstrate that Egypt response to alleviate the COVID-19 is rapidly pronounced specially on SDGs 3 and 4 simultaneously. However, those MDRPs show that neither record impacts nor delays on SDGs 6, 7, 9 and 11. The conclusions prioritize both the advanced educational mechanisms and verify the engineering curriculums to raise awareness of SDGs among both staff members and students in Egyptian universities. Finally, future work is recommended at the end of this article to focus on each specific goal to gain deep insight to state of the art for each SDG around globe using education development.

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“…The application of big data is almost indispensable [2]. Human resources issues in urban smart production [3], issues of reducing environmental pollution through smart manufacturing [4], intelligent information security management fields of various industries involved in safety information technology [5], etc., both internally and externally As so many topics are related to data, it is one of the necessary tools to reach the smart factory.…”

Section: Literature Reviewmentioning

confidence: 99%

The Investigation of Motor Production Test Indicators in Qualitative Research Methods

Lin¹,

Yeh²,

Yang³

et al. 2020

IJMMM

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This research is based on the concept of smart manufacturing, with the case of a motor manufacturing line. Explore the relationship between the project content in the finished motor data and all processes of pre-engineering, coils, and assembly. The objective of this study is to improve the efficiency of motor quality control inspection. The method is mainly to use expert interviews and secondary data to collect case company data. This study finds that: 1. This research is based on the initial 88 testing items, of which 14 items can be referenced in the motor manufacturing process. 2. This study proposes the relevant parts and production steps corresponding to these 14 items, which can evaluate the error points from the values of the motor finished product inspection items and simplify the time spent in quality control inspection. Finally, this study proposes practical implications and future research recommendations.

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Safety informatics as a new, promising and sustainable area of safety science in the information age

Cited by 46 publications

References 77 publications

Finding Pluto: An Analytics-Based Approach to Safety Data Ecosystems

Finding Pluto: An Analytics-Based Approach to Safety Data Ecosystems

Sustainable Development Importance in Higher Education for Occupational Health and Safety Using Egypt Vision 2030 under COVID-19 Pandemic

The Investigation of Motor Production Test Indicators in Qualitative Research Methods

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