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Using data-driven safety decision-making to realize smart safety management in the era of big data: A theoretical perspective on basic questions and their answers

Author:
Wang, Bing, Wu, Chao, Huang, Lang, Kang, Liangguo
Source:
Journal of cleaner production 2019 v.210 pp. 1595-1604
ISSN:
0959-6526
Subject:
assets, data analysis, decision making, safety assessment, theoretical models
Abstract:
How to make an effective safety decision is always a topic of intense interest in the safety management field. Safety-Related Data (SRD) are the most valuable assets for organizations’ Safety Decision-Making (SDM), especially in the era of big data. This paper focuses on the potentially important value of SRD in SDM, and aims to systematically answer some fundamental questions concerning a new paradigm for SDM, known as data-driven SDM, from a theoretical perspective. These questions examine (1) what it is, (2) what its benefits are, (3) what its theoretical foundations are, (4) what its fundamental elements consist of, (5) what factors influencing it are, and (6) how the organization should implement it and realize smart safety management by using it. Other theoretical and practical contributions include a discussion of the problems of traditional SDM approaches and how to solve them, a rationale for creating and studying data-driven SDM, and suggestions for future research. This paper is the first to study the basic questions of data-driven SDM specifically, thus its results hold important implications for future research and practice on data-driven SDM and smart safety management.
Agid:
6233515