Safety and Health at Work

  • 제12권1호
  • /
  • Pages.1-9
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  • 2021
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  • 2093-7911(pISSN)
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  • 2093-7997(eISSN)
산업안전보건연구원 (Occupational Safety and Health Research Institute, Korea Occupational Safety and Health Agency)
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Finding Pluto: An Analytics-Based Approach to Safety Data Ecosystems

  • Barker, Thomas T. (The University of Alberta, Faculty of Extension)
  • 투고 : 2020.07.02
  • 심사 : 2020.09.21
  • 발행 : 2021.03.30
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초록

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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참고문헌

  1. History.com Editors. Pluto discovered - HISTORY. In: HISTORY [Internet]. 3 Mar 2010 [cited 11 Feb 2019]. Available: https://www.history.com/this-day-in-history/pluto-discovered.
  2. Schultz G. Using advanced analytics to predict and prevent workplace injuries - occupational health & safety. [cited 10 Sep 2020]. Available: https://ohsonline.com/Articles/2012/07/01/Using-Advanced-Analytics-to-Predictand-Prevent-Workplace-Injuries.aspx.
  3. Huang L, Wu C, Wang B, Ouyang Q. A new paradigm for accident investigation and analysis in the era of big data. Process Saf Prog 2018;37:42-8. https://doi.org/10.1002/prs.11898
  4. Wang B, Wu C. Demystifying safety-related intelligence in safety management: some key questions answered from a theoretical perspective. Saf Sci 2019;120:932-40. https://doi.org/10.1016/j.ssci.2019.08.030
  5. Wang B, Wu C. Safety informatics as a new, promising and sustainable area of safety science in the information age. J Clean Prod 2020;252:119852. https://doi.org/10.1016/j.jclepro.2019.119852
  6. Ouyang Q, Wu C, Huang L. Methodologies, principles and prospects of applying big data in safety science research. Saf Sci 2018;101:60-71. https://doi.org/10.1016/j.ssci.2017.08.012
  7. Tripanagnostopoulos Y, Nousia T, Souliotis M, Yianoulis P. Hybrid photovoltaic/thermal solar systems. Solar Energy 2002;72:217-34. https://doi.org/10.1016/S0038-092X(01)00096-2
  8. Dekker S. Safety differently: human factors for a new era. CRC Press; 2014.
  9. Hollnagel E. Safety managementelooking back or looking forward. Resilience Eng Perspect 2016;1. Available: https://www.taylorfrancis.com/books/e/9781351903929/chapters/10.4324%2F9781315244396-11.
  10. Wick C. Knowledge management: communication of technical information, vol. 47. Technical Communication; 2000.
  11. Balfe N, Leva MC, Ciarapica-Alunni C, O'Mahoney S. Total project planning: integration of task analysis, safety analysis and optimisation techniques. Saf Sci 2017;100:216-24. https://doi.org/10.1016/j.ssci.2016.10.014
  12. Pabico C. Positive healthcare practice environments: implications for patient safety and outcomes. Health Care Curr Rev 2016;4. https://doi.org/10.4172/2375-4273.C1.017.
  13. Li J, Hale A. Identification of, and knowledge communication among core safety science journals. Saf Sci 2015;74:70-8. https://doi.org/10.1016/j.ssci.2014.12.003
  14. Rao TR, Mitra P, Bhatt R, Goswami A. The big data system, components, tools, and technologies: a survey. Knowl Inf Syst 2018. Available: https://link.springer.com/article/10.1007/s10115-018-1248-0; 2018.
  15. Li J, Hale A. Output distributions and topic maps of safety related journals. Saf Sci 2016;82:236-44. https://doi.org/10.1016/j.ssci.2015.09.004
  16. Huang L, Wu C, Wang B, Ouyang Q. Big-data-driven safety decision-making: a conceptual framework and its influencing factors. Saf Sci 2018;109:46-56. https://doi.org/10.1016/j.ssci.2018.05.012
  17. Laney D. 3D Data management: controlling data volume, velocity and variety. Contract no: research note. 2015; vol. 6; 2001.
  18. Pfeffer J, Jeffrey P, Thomas D., Dee II Professor of Organizational Behavior Graduate School of Business Jeffrey Pfeffer. The human equation: building profits by putting people first. Harvard Business Press; 1998.
  19. Zacharatos A, Barling J, Iverson RD. High-performance work systems and occupational safety. J Appl Psychol 2005;90:77-93. https://doi.org/10.1037/0021-9010.90.1.77
  20. Li J, Goerlandt F, Li KW. Slip and fall incidents at work: a visual analytics analysis of the research domain. Int J Environ Res Publ Health 2019;16. https://doi.org/10.3390/ijerph16244972.
  21. Dadich A, Fulop L, Ditton M, Campbell S, Curry J, Eljiz K, et al. Finding brilliance using positive organizational scholarship in healthcare. J Health Organisat Manag 2015;29:750-77. https://doi.org/10.1108/JHOM-11-2013-0256
  22. Sanchez-Pi N, Marti L, Molina JM, Bicharra Garcia AC. Information fusion for improving decision-making in big data applications. In: Pop F, Kolodziej J, Di Martino B, editors. Resource management for big data platforms: algorithms, modelling, and high-performance computing techniques. Cham: Springer International Publishing; 2016. p. 171-88.
  23. Camplin JC. Demonstrating safety performance through leading indicators. ASSE Professional Development Conference and. 2012. Available, https://www.onepetro.org/conference-paper/ASSE-12-601.
  24. Dyck D, Roithmayr T. Great Safety Performance: an improvement process using leading indicators. AAOHN J 2004;52:511-20. https://doi.org/10.1177/216507990405201205
  25. Kamaletdinova GR, Onevsky MP, Skvortsov SA. Human safety in a man-made ecosystem. Int J Safe 2018;8:406-12. https://doi.org/10.2495/SAFE-V8-N3-406-412
  26. Johnsen SO. Risks, safety and security in the ecosystem of smart cities. In: Svalova V, editor. Risk assessment. InTech; 2018.
  27. Crocker J. Safety in numbers: shifting from egosystem to ecosystem. Psychol Inq 2011;22:259-64. https://doi.org/10.1080/1047840X.2011.624978
  28. Hollnagel E, Wears RL, Braithwaite J. From safety-I to safety-II: a white paper. The resilient health care net: published simultaneously by the University of Southern Denmark. Australia: University of Florida, USA, and Macquarie University. 2015. Available: http://www.qpsolutions.vn/cgi-bin/Document/Safety%20II%20WhitePaper.pdf.
  29. Shi-Nash A, Hardoon DR. Data analytics and predictive analytics IN the era OF big data. In: Geng H, editor. Internet of things and data analytics handbook. Hoboken, NJ, USA: John Wiley & Sons, Inc.; 2017. p. 329-45.
  30. Wang B, Wu C, Huang L, Kang L. 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. J Clean Prod 2019;210:1595-604. https://doi.org/10.1016/j.jclepro.2018.11.181
  31. Dasgupta T, Naskar A, Saha R, Dey L. Extraction and visualization of occupational health and safety related information from open web. In: 2018 IEEE/WIC/ACM international conference on web intelligence (WI). ieeexplore.ieee.org; 2018. p. 434-9.
  32. Badri A, Boudreau-Trudel B, Souissi AS. Occupational health and safety in the industry 4.0 era: a cause for major concern? Saf Sci 2018;109:403-11. https://doi.org/10.1016/j.ssci.2018.06.012
  33. Deloitte & Touche LLP. Workplace safety analytics. Available: https://www2.deloitte.com/content/dam/Deloitte/ca/Documents/Analytics/ca-en-analytics-workplace-safety-analytics.pdf.
  34. Li J, Reniers G, Cozzani V, Khan F. A bibliometric analysis of peer-reviewed publications on domino effects in the process industry. J Loss Prev Process Ind 2017;49:103-10. https://doi.org/10.1016/j.jlp.2016.06.003
  35. Hunter KO, Wolf EM. Cracking the code of process safety culture with organizational network analysis. Process Saf Prog 2016;35:276-85. https://doi.org/10.1002/prs.11793
  36. Ancel E, Shih AT, Jones SM, Reveley MS, Luxhoj JT, Evans JK. Predictive safety analytics: inferring aviation accident shaping factors and causation. Null 2015;18:428-51.
  37. Lian Y, Zhang G, Lee J, Huang H. Review on big data applications in safety research of intelligent transportation systems and connected/automated vehicles. Accid Anal Prev 2020;146:105711. https://doi.org/10.1016/j.aap.2020.105711
  38. Corrigan S, Kay A, Ryan M, Brazil B, Ward ME. Human factors & safety culture: challenges & opportunities for the port environment. Saf Sci 2020;125:103854. https://doi.org/10.1016/j.ssci.2018.02.030
  39. Salas E, DiazGranados D, Klein C, Burke CS, Stagl KC, Goodwin GF, et al. Does team training improve team performance? A meta-analysis. Hum Factor. 2008;50:903-33. https://doi.org/10.1518/001872008X375009
  40. Global occupational safety and health. 13 Nov 2018 [cited 10 Sep 2020]. Available: https://www.cdc.gov/niosh/topics/global/default.html.
  41. Jorgensen K. A systematic use of information from accidents as a basis of prevention activities. Saf Sci 2008;46:164-75. https://doi.org/10.1016/j.ssci.2007.05.016
  42. Government of Canada, Canadian Centre for occupational health, safety. Workplace health and well-being promotion - Getting started. [cited 12 Feb 2019]. Available: http://www.ccohs.ca/.
  43. Stanton JM, Balzer WK, Smith PC, Parra LF, Ironson G. A general measure of work stress: the stress in general scale. Educ Psychol Meas 2001;61:866-88. https://doi.org/10.1177/00131640121971455
  44. Dyck DEG. Occupational health & safety: theory, strategy & industry practice. Canada: LexisNexis; 2015.
  45. Goraya A, Amyotte PR, Khan FI. An inherent safetyebased incident investigation methodology. Process Saf Prog 2004;23:197-205. https://doi.org/10.1002/prs.10032
  46. Donaldson C. Predicting safety: analytics comes to OHS. OHS Professional. 2017. Available: http://safetyontap.com/wp-content/uploads/2017/07/SIA-OHS-Professional-March-2017-analytics-comes-to-OHS.pdf.
  47. Trevisan DG, Sanchez-Pi N, Marti L, Garcia ACB. Big data visualization for occupational health and security problem in oil and gas industry. Human interface and the management of information information and knowledge design. Springer International Publishing; 2015. p. 46-54.
  48. Provan DJ, Dekker SWA, Rae AJ. Bureaucracy, influence and beliefs: a literature review of the factors shaping the role of a safety professional. Saf Sci 2017;98:98-112. https://doi.org/10.1016/j.ssci.2017.06.006
  49. Horton J, Cameron A, Devaraj D, Hanson RT, Hajkowicz SA. Workplace Safety Futures: the impact of emerging technologies and platforms on work health and safety and workers' compensation over the next 20 years. Canberra: CSIRO. 2018. Available: https://www.inxsoftware.com/media/transfer/doc/workplacesafety.pdf.
  50. Gwilt I, Rolph J, Eimontaite I, Cameron D, Aitken J, Mokaram S, et al. Cobotics: developing a visual language for human-robotic collaborations. In: Proceedings of the cumulus conference 2018. Available: https://www.researchgate.net/profile/Jonathan_Aitken2/publication/324079462_Cobotics_developing_a_visual_language_for_human-robotic_collaborations/links/5af2db35a6fdcc0c0304fdc6/Cobotics-developing-a-visual-language-for-human-robotic-collaborations.pdf; 2018.
  51. Li X, Yi W, Chi H-L, Wang X, Chan APC. A critical review of virtual and augmented reality (VR/AR) applications in construction safety. Autom Constr 2018;86:150-62. https://doi.org/10.1016/j.autcon.2017.11.003
  52. Chan Albert PC, Javed Arshad Ali, Wong Francis KW, Hon Carol KH. Improving safety communication of ethnic minorities in the construction industry of Hong Kong. ICCREM 2014. https://doi.org/10.1061/9780784413777.056.
  53. Guo SY, Ding LY, Luo HB, Jiang XY. A Big-Data-based platform of workers' behavior: observations from the field. Accid Anal Prev 2016;93:299-309. https://doi.org/10.1016/j.aap.2015.09.024
  54. Branson N. Re-weighting the OHS and LFS national household survey data to create a consistent series over time: a cross entropy estimation approach. Southern Africa Labour and Development Research Unit, University of Cape Town; 2009.
  55. Bifet A. Mining big data in real time. Informatica 2013;37. Available: http://www.informatica.si/index.php/informatica/article/download/428/432.
  56. Dibenedetto DV. Identifying requirements for an occupational health and safety information system. AAOHN J 2004;52:368-72. https://doi.org/10.1177/216507990405200904
  57. Salazar MK, Kemerer S, Amann MC, Fabrey LJ. Defining the roles and functions of occupational and environmental health nurses. Results of a national job analysis. AAOHN J 2002;50:16-25. https://doi.org/10.1177/216507990205000107
  58. Franchini L, Caillaud E, Nguyen P, Lacoste G. Workload control of human resources to improve production management. Int J Prod Res 2001;39:1385-403. https://doi.org/10.1080/00207540010022322
  59. Thissen-Roe A. Adaptive selection of personality items to inform a neural network predicting job performance. University of Washington. 2005. Available: http://iacat.org/sites/default/files/biblio/th05-01.pdf.
  60. Strohmeier S, Piazza F. Domain driven data mining in human resource management: a review of current research. Expert Syst Appl 2013;40:2410-20. https://doi.org/10.1016/j.eswa.2012.10.059
  61. Morabito V. Big data and analytics innovation practices. In: Morabito V, editor. Big data and analytics: strategic and organizational impacts. Cham: Springer International Publishing; 2015. p. 157-76.
  62. Johnson J, Haegeli P, Hendrikx J, Savage S. Accident causes and organizational culture among avalanche professionals. J Outdoor Recreation Tourism 2016;13:49-56. https://doi.org/10.1016/j.jort.2015.11.003
  63. Bail CA. The cultural environment: measuring culture with big data. Theory Soc 2014;43:465-82. https://doi.org/10.1007/s11186-014-9216-5
  64. Pence J, Mohaghegh Z, Kee E, Yilmaz F, Grantom R, Johnson D. Toward monitoring organizational safety indicators by integrating probabilistic risk assessment, socio-technical systems theory, and big data analytics. In: Proceedings of 12th international topical meeting on probabilistic safety assessment and analysis (PSAM12). psam12.org 2014. Available: http://psam12.org/proceedings/paper/paper_549_1.pdf; 2014.
  65. Grossman R, Siegel K. Organizational models for big data and analytics; 2014. Available: https://papers.ssrn.com/abstract=2458909.
  66. Davenport TH. Competing on analytics. Harv Bus Rev 2006;84. 98-107, 134.
  67. Fernandez FB, Perez MAS. Analysis and modeling of new and emerging occupational risks in the context of advanced manufacturing processes. Procedia Eng 2015;100:1150-9. https://doi.org/10.1016/j.proeng.2015.01.478
  68. Felt M. Social media and the social sciences: how researchers employ Big Data analytics. Big Data Soc 2016;3. 2053951716645828.
  69. Segall RS, Niu G. Overview of big data and its visualization. In: Global IGI, editor. Handbook of research on big data storage and visualization techniques. IGI Global; 2018. p. 1-32.
  70. McCue C. Data mining and predictive analytics in public safety and security. IT Prof 2006;8:12-8. https://doi.org/10.1109/MITP.2006.84
  71. Robson LS, Clarke JA, Cullen K, Bielecky A, Severin C, Bigelow PL, et al. The effectiveness of occupational health and safety management system interventions: a systematic review. Saf Sci 2007;45:329-53. https://doi.org/10.1016/j.ssci.2006.07.003
  72. Robson LS, Amick III BC, Moser C, Pagell M, Mansfield E, Shannon HS, et al. Important factors in common among organizations making large improvement in OHS performance: results of an exploratory multiple case study. Saf Sci 2016;86:211-27. https://doi.org/10.1016/j.ssci.2016.02.023
  73. Cooren F, Kuhn T, Cornelissen JP, Clark T. Communication, organizing and organization: an overview and introduction to the special issue. Organ Stud 2011;32:1149-70. https://doi.org/10.1177/0170840611410836
  74. Wilhoit ED. Space, place, and the communicative constitution of organizations: a constitutive model of organizational space. Commun Theory 2018;28: 311-31. https://doi.org/10.1093/ct/qty007
  75. Badri A, Gbodossou A, Nadeau S. Occupational health and safety risks: towards the integration into project management. Saf Sci 2012;50:190-8. https://doi.org/10.1016/j.ssci.2011.08.008

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