Abstract
Occupational Health and Safety Risk Assessment can undoubtedly benefit from enabling technologies of Industry 4.0, with the aim of collecting and analyzing the big data related to the occupational risk factors arising into workplaces. In this paper, the assessment of the occupational risk is addressed by means of a multi-criteria approach. Indeed, after the pre-treatment of the time series of the said risk factors by means of a segmentation algorithm, a TOPSIS approach is implemented to assess the dynamic, individual and integrated risk to which a worker is subjected over the time. Finally, a numerical example is reported to illustrate the proposed in practice.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Liao, Y., Deschamps, F., Loures, E.D.F.R., Ramos, L.F.P.: Past, present and future of Industry 4.0 - a systematic literature review and research agenda proposal. Int. J. Prod. Res. 55, 3609–3629 (2017). https://doi.org/10.1080/00207543.2017.1308576
Lu, Y.: Industry 4.0: A survey on technologies, applications and open research issues (2017). https://doi.org/10.1016/j.jii.2017.04.005
Matt, C., Hess, T., Benlian, A.: Digital Transformation Strategies. Bus. Inf. Syst. Eng. 57(5), 339–343 (2015). https://doi.org/10.1007/s12599-015-0401-5
Neumann, W.P., Village, J.: Ergonomics action research II: a framework for integrating HF into work system design. Ergonomics 55, 1140–1156 (2012). https://doi.org/10.1080/00140139.2012.706714
İnan, U.H., Gül, S., Yılmaz, H.: A multiple attribute decision model to compare the firms’ occupational health and safety management perspectives. Saf. Sci. 91, 221–231 (2017). https://doi.org/10.1016/j.ssci.2016.08.018
Sousa, V., Almeida, N.M., Dias, L.A.: Risk-based management of occupational safety and health in the construction industry - Part 2: Quantitative model. Saf. Sci. 74, 184–194 (2015). https://doi.org/10.1016/j.ssci.2015.01.003
HSE (Health and Safety Executive): Risk Assessment: A Brief Guide to Controlling Risks in the Workplace, INDG163 (rev4). http://www.hse.gov.uk/pubns/indg163.pdf
Sgarbossa, F., Grosse, E.H., Neumann, W.P., Battini, D., Glock, C.H.: Human factors in production and logistics systems of the future. Annu. Rev. Control. 49, 295–305 (2020). https://doi.org/10.1016/j.arcontrol.2020.04.007
Badri, A., Boudreau-Trudel, B., Souissi, A.S.: Occupational health and safety in the industry 4.0 era: a cause for major concern? Saf. Sci. 109, 403–411 (2018). https://doi.org/10.1016/j.ssci.2018.06.012
Grosse, E.H., Calzavara, M., Glock, C.H., Sgarbossa, F.: Incorporating human factors into decision support models for production and logistics: current state of research. IFAC-PapersOnLine 50, 6900–6905 (2017). https://doi.org/10.1016/j.ifacol.2017.08.1214
Lolli, F., Balugani, E., Gamberini, R., Rimini, B.: Quality cost-based allocation of training hours using learning-forgetting curves. Comput. Ind. Eng. 131, 552–564 (2019). https://doi.org/10.1016/j.cie.2019.02.020
Finco, S., Battini, D., Delorme, X., Persona, A., Sgarbossa, F.: Workers’ rest allowance and smoothing of the workload in assembly lines. Int. J. Prod. Res. 58, 1255–1270 (2020). https://doi.org/10.1080/00207543.2019.1616847
Calzavara, M., Glock, C.H., Grosse, E.H., Sgarbossa, F.: An integrated storage assignment method for manual order picking warehouses considering cost, workload and posture. Int. J. Prod. Res. 57, 2392–2408 (2019). https://doi.org/10.1080/00207543.2018.1518609
Calzavara, M., Battini, D., Bogataj, D., Sgarbossa, F., Zennaro, I.: Ageing workforce management in manufacturing systems: state of the art and future research agenda. Int. J. Prod. Res. 58, 729–747 (2020). https://doi.org/10.1080/00207543.2019.1600759
Neumann, W.P., Winkelhaus, S., Grosse, E.H., Glock, C.H.: Industry 4.0 and the human factor – a systems framework and analysis methodology for successful development. Int. J. Prod. Econ. 233 (2021). https://doi.org/10.1016/j.ijpe.2020.107992
Gul, M.: A review of occupational health and safety risk assessment approaches based on multi-criteria decision-making methods and their fuzzy versions. Hum. Ecol. Risk Assess. 24, 1723–1760 (2018). https://doi.org/10.1080/10807039.2018.1424531
Verma, S., Chaudhari, S.: Highlights from the literature on risk assessment techniques adopted in the mining industry: a review of past contributions, recent developments and future scope. Int. J. Min. Sci. Technol. 26, 691–702 (2016). https://doi.org/10.1016/j.ijmst.2016.05.023
Grassi, A., Gamberini, R., Mora, C., Rimini, B.: A fuzzy multi-attribute model for risk evaluation in workplaces. Saf. Sci. 47, 707–716 (2009). https://doi.org/10.1016/j.ssci.2008.10.002
John, A., Yang, Z., Riahi, R., Wang, J.: A Decision Support System for the Assessment of Seaports’ Security Under Fuzzy Environment. In: Konstantopoulos, C., Pantziou, G. (eds.) Modeling, Computing and Data Handling Methodologies for Maritime Transportation. ISRL, vol. 131, pp. 145–177. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-61801-2_6
Ozturkoglu, Y., Kazancoglu, Y., Ozkan-Ozen, Y.D.: A sustainable and preventative risk management model for ship recycling industry. J. Clean. Prod. 238 (2019). https://doi.org/10.1016/j.jclepro.2019.117907
Cao, X., Lam, J.S.L.: A fast reaction-based port vulnerability assessment: case of Tianjin Port explosion. Transp. Res. Part A Policy Pract. 128, 11–33 (2019). https://doi.org/10.1016/j.tra.2019.05.019
Shen, X., Wang, Z., Sun, Y.: Wireless sensor networks for industrial applications. Proc. World Congr. Intell. Control Autom. 4, 3636–3640 (2004). https://doi.org/10.1109/wcica.2004.1343273
Lovrić, M., Milanović, M., Stamenković, M.: Algoritmic methods for segmentation of time series: An overview (2014)
Tixier, J., Dusserre, G., Salvi, O., Gaston, D.: Review of 62 risk analysis methodologies of industrial plants. J. Loss Prev. Process Ind. 15, 291–303 (2002). https://doi.org/10.1016/S0950-4230(02)00008-6
Martins, C.G.: Risk identification techniques knowledge and application in the Brazilian construction. J. Civ. Eng. Constr. Technol. 2, 242–252 (2011). https://doi.org/10.5897/jcect11.024
Fanger, P.O.: Calculation of Thermal Comfort, Introduction of a Basic Comfort Equation. undefined (1967)
Muggeo, V.M.R.: Selecting number of breakpoints in segmented regression: implementation in the R package segmented, pp. 1–3 (2020). https://doi.org/10.13140/RG.2.2.12891.39201
Muggeo, V.M.R.: Estimating regression models with unknown break-points. Stat. Med. 22, 3055–3071 (2003). https://doi.org/10.1002/sim.1545
Gul, M.: A fuzzy-based occupational health and safety risk assessment framework and a case study in an international port authority. J. Mar. Eng. Technol. 19, 161–175 (2020). https://doi.org/10.1080/20464177.2019.1670994
EN-ISO-7730: ISO 7730:2005 (2005)
Sanità , I.S.: Qualità dell’aria indoor: attuale situazione nazionale e comunitaria (2014)
EN-12464: UNI EN 12464–1:2004
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Lolli, F. et al. (2021). The Dynamic, Individual and Integrated Risk Assessment: A Multi-criteria Approach Using Big Data. In: Arezes, P.M., Boring, R.L. (eds) Advances in Safety Management and Human Performance. AHFE 2021. Lecture Notes in Networks and Systems, vol 262. Springer, Cham. https://doi.org/10.1007/978-3-030-80288-2_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-80288-2_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-80287-5
Online ISBN: 978-3-030-80288-2
eBook Packages: EngineeringEngineering (R0)