Abstract
The BowTie diagram is a graphical visualisation tool. Its purpose is to describe the structure of the safety management system in place to prevent threats from realising a top event and to mitigate the consequences if a top event were to become reality. The barriers on a BowTie diagram are traditionally shown as simple barriers (detect, diagnose, act) but in reality, are configured as complex systems. Time-series data from processing environments with the use of appropriate analytical tools can be used in conjunction to monitor and report the health of barriers and reflect upon the whole safety management system. Process Safety Performance Indicators can then be reported on a real or near-real time basis. These indicators would become more transparent from operational personnel to senior management levels thereby increasing the understanding of the health of the safety management system. Due to the real or near-real time reporting with time-series data analytical tools, management can be informed of the process status which could lead to timely decisions and actions. Process health can be monitored and reported on active dashboards with greater reliability and accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Cannataci, J., et al.: Legal challenges of big data. In: Joe, C., Oreste, P., Valeria, F. (eds.). Edward Elgar Publishing Limited, Cheltenham, UK (2020)
WEF: Big data, big impact: New possibilities for international development. https://www3.weforum.org/docs/WEF_TC_MFS_BigDataBigImpact_Briefing_2012.pdf (2012). Accessed 16 March 2022
Qin, S.J., Chiang, L.H.: Advances and opportunities in machine learning for process data analytics. Comput. Chem. Eng. 126, 465–473 (2019)
Bubbico, R., et al.: Dynamic assessment of safety barriers preventing escalation in offshore Oil&Gas. Saf. Sci. 121, 319–330 (2020)
Colegrove, L.F., Seasholtz, M.B., Khare, C.: Getting started on the journey. 112, 41
Holmstrom, D., et al.: CSB investigation of the explosions and fire at the BP texas city refinery on March 23, 2005. Proc. Saf. Prog. 25(4), 345–349 (2006)
Stemn, E., et al.: Failure to learn from safety incidents: status, challenges and opportunities. Saf. Sci. 101, 313–325 (2018)
Baker, J.A., et al.: The report of the BP U.S. refineries independent safety review panel, p. 374. (2007)
Allars, K.: BP Texas City incident Baker review p. 2. 2007
Hopkins, A.: Thinking about process safety indicators. Saf. Sci. 47(4), 460–465 (2009)
HSE: Buncefield: Why did it happen? HSE, p. 36. (2011). https://www.hse.gov.uk/comah/buncefield/buncefield-report.pdf
Theis, A.E.: Case study: T2 laboratories explosion. J. Loss Prev. Process Ind. 30, 296–300 (2014)
CSB: T2 Laboratories, Inc. runaway reaction. CSB (2009)
Singh, P., Sunderland, N., van Gulijk, C.: Determination of the health of a barrier with time-series data how a safety barrier looks different from a data perspective. J. Loss Prev. Process Ind. 80, 104889 (2022)
Manuele, F.A.: Highly unusual: CSB’s comments signal long-term effects on the practice of safety. Prof. Saf. 62(4), 26–33 (2017)
Skogdalen, J.E., Utne, I.B., Vinnem, J.E.: Develo** safety indicators for preventing offshore oil and gas deep water drilling blowouts. Saf. Sci. 49(8), 1187–1199 (2011)
Van Gulijk, C., et al.: Big data risk analysis for rail safety? In: Podofillini, L., et al. (eds). Proceedings of ESREL 2015. CRC/Balkema (2015)
Brockwell, P.J., Davis, R.A.: Time Series: Theory and Methods, 2nd edn. Springer New York, New York, NY (1991)
Seeq.: Seeq About Us. Retrieved from https://www.seeq.com/about, 25 February 2023
Seeq.: Seeq Workbench. Seeq Workbench 2023. Retrieved from https://www.seeq.com/product/workbench, 25 February 2023
CCPS.: Bow ties in risk management: a concept book for process safety, p. 224. Wiley (2018)
Khakzad, N., Khan, F., Amyotte, P.: Risk-based design of process systems using discrete-time Bayesian networks. Reliab. Eng. Syst. Saf. 109, 5–17 (2013)
Hughes, P., et al.: From free-text to structured safety management: introduction of a semi-automated classification method of railway hazard reports to elements on a bow-tie diagram. Saf. Sci. 110, 11–19 (2018)
Hughes, P., et al.: Extracting safety information from multi-lingual accident reports using an ontology-based approach. Saf. Sci. 118, 288–297 (2019)
de Dianous, V., Fiévez, C.: ARAMIS project: a more explicit demonstration of risk control through the use of bow–tie diagrams and the evaluation of safety barrier performance. J. Hazard. Mater. 130(3), 220–233 (2006)
Busch, C., et al.: Serious injuries & fatalities. Prof. Saf. 66(1), 26–31 (2021)
Sultana, S., Andersen, B.S., Haugen, S.: Identifying safety indicators for safety performance measurement using a system engineering approach. Process Saf. Environ. Prot. 128, 107–120 (2019)
Louvar, J.: Guidance for safety performance indicators. Process Saf. Prog. 29(4), 387–388 (2010)
Selvik, J.T., Bansal, S., Abrahamsen, E.B.: On the use of criteria based on the SMART acronym to assess quality of performance indicators for safety management in process industries. J. Loss Prev. Process Ind. 70, 104392 (2021)
Pasman, H., Rogers, W.: How can we use the information provided by process safety performance indicators? Possibilities and limitations. J. Loss Prev. Process Ind. 30, 197–206 (2014)
Acknowledgements
This research was supported in collaboration by the University of Huddersfield and by Syngenta Huddersfield Manufacturing Centre.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Singh, P., van Gulijk, C. (2023). Digital Safety Delivery: How a Safety Management System Looks Different from a Data Perspective. In: van Gulijk, C., Zaitseva, E., Kvassay, M. (eds) Reliability Engineering and Computational Intelligence for Complex Systems. Studies in Systems, Decision and Control, vol 496. Springer, Cham. https://doi.org/10.1007/978-3-031-40997-4_10
Download citation
DOI: https://doi.org/10.1007/978-3-031-40997-4_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-40996-7
Online ISBN: 978-3-031-40997-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)