OPEN ACCESS
Despite the evolution of regulations in the field of occupational health and safety promoted in EU countries, the number of accidents and victims has not significantly decreased in recent years, especially in constructions and agriculture sectors, as underlined by official reports of the Italian Workers’ Compensation Authority. Main reasons of such a situation are due to the characteristics of working activities in these sectors. The variety of operations, the frequent exchange of tasks among workers within the same company, the continuous change of workplaces, the frequent exchange of workers for the same activity (e.g. seasonal workers), and the workers’ stress caused by seasonal jobs. For these reasons both risk assessment and safety management activities result in being more difficult than in other working sectors. Thus, it is important to provide methodologies and tools that allow companies to carry out these tasks more effectively. In such a context, the study proposed by Esra Bas in 2014 certainly represents an attempt to provide a supporting methodology for engineers engaged in risk assessment activities. This approach consists in the use of the Quality Function Deployment (QFD) method, and it is aimed at evaluating how specific tasks can be in relationship with specific hazards, which in turn are related to specific events, and finally at defining what preventive/protective measures can be introduced against those events. Based on this, we tried to further investigate such an approach, with the goal of providing an easier-to-use tool, which can be used in risk assessment activities of critical contexts as the agriculture one. With this aim in mind, a case study concerning the risk assessment of an agricultural machinery was carried out.
agricultural equipment, house of quality, machinery safety, occupational safety, quality function deployment, risk assessment
[1] Bas, E., An integrated quality function deployment and capital budgeting methodology for occupational safety and health as a systems thinking approach: the case of the construction industry. Accident Analysis & Prevention, 68, pp. 42–56, 2014. https://doi.org/10.1016/j.aap.2013.10.005
[2] Eurostat, available at http://ec.europa.eu/eurostat/statistics-explained/index.php/Accidents_at_work_statistics, 2016 (Accessed 13 April 2017).
[3] Frank, A. L., McKnight, R., Kirkhorn, S. R. & Gunderson, P., Issues of agricultural safety and health. Annual Review of Public Health, 25, pp. 225–245, 2004. https://doi.org/10.1146/annurev.publhealth.25.101802.123007
[4] Caffaro, F., Mirisola, A. & Cavallo, E., Safety signs on agricultural machinery: pictorials do not always successfully convey their messages to target users. Applied Ergonomics, 58, pp. 156–166, 2017. https://doi.org/10.1016/j.apergo.2016.06.003
[5] Douphrate, D.I., Stallones, L., Lunner Kolstrup, C., Nonnenmann, M.W., Pinzke, S., Hagevoort, G.R. & Jarvie, P., Work-related injuries and fatalities on dairy farm operations—a global perspective. Journal of Agromedicine, 18(3), pp. 256–264, 2013. https://doi.org/10.1080/1059924x.2013.796904
[6] Burgus, S. & Duysen, E., Identifying topics and dissemination methods for agricultural safety and health messages. Safety, 3(3), p. 3, 2017. https://doi.org/10.3390/safety3010003
[7] Fargnoli, M., Vita, L., Gattamelata, D., Laurendi, V. & Tronci, M., A reverse engineering approach to enhance machinery design for safety. Proceedings of the 12th International Design Conference - DESIGN 2012, pp. 627–636, 2012.
[8] Abubakar, M.S.A., Ahmad, D. & Akande, F.B., A review of farm tractor overturning accidents and safety. Pertanika Journal of Science & Technology, 18(2), pp. 377–385, 2010.
[9] Mayrhofer, H., Quendler, E. & Boxberger, J., Occupational incidents with self-propelled machinery in Austrian agriculture. Journal of Agromedicine, 18(4), pp. 359–367, 2013. https://doi.org/10.1080/1059924x.2013.827997
[10] Cavallo, E., Ferrari, E., Bollani, L. & Coccia, M., Attitudes and behaviour of adopters of technological innovations in agricultural tractors: a case study in Italian agricultural system. Agricultural Systems, 130, pp. 44–54, 2014. https://doi.org/10.1016/j.agsy.2014.05.012
[11] Li, Z., Mitsuoka, M., Inoue, E., Okayasu, T. & Hirai, Y., Development of stability indicators for dynamic Phase I overturn of conventional farm tractors with front axle pivot. Biosystems Engineering, 134, pp. 55–67, 2015. https://doi.org/10.1016/j.biosystemseng.2015.03.016
[12] Myers, M.L., Cole, H.P. & Westneat, C., Injury severity related to overturn characteristics of tractors. Journal of Safety Research, 40, pp. 165–170, 2009. https://doi.org/10.1016/j.jsr.2009.02.007
[13] Myers, J.R. & Hendricks, K.J., Agricultural tractor overturn deaths: assessment of trends and risk factors. American Journal of Industrial Medicine, 53(7), pp. 662–672, 2010. https://doi.org/10.1002/ajim.20775
[14] Fargnoli, M., Laurendi, V. & Tronci, M., Design for safety in agricultural machinery. Proceedings of the 11th International Design Conference - DESIGN 2010, pp. 701–710.
[15] Donham, K.J. & Thelin, A., Prevention of Illness and Injury in Agriculture. Agricultural Medicine: Rural Occupational and Environmental Health, Safety, and Prevention, John Wiley & Sons, pp. 503–550, 2016.
[16] Lombardi, M., Guarascio, M. & Rossi, G., The management of uncertainty: model for evaluation of human error probability in railway system. American Journal of Applied Sciences, 11(3), pp. 381–390, 2013. https://doi.org/10.3844/ajassp.2014.381.390
[17] Rautiainen, R.H., Ledolter, J., Donham, K.J., Ohsfeldt, R.L. & Zwerling, C., Risk factors for serious injury in Finnish agriculture. American Journal of Industrial Medicine, 52(5), pp. 419–428, 2009. https://doi.org/10.1002/ajim.20688
[18] Taattola, K., Rautiainen, R.H., Karttunen, J.P., Suutarinen, J., Viluksela, M.K., Louhelainen, K. & Mäittälä, J., Risk factors for occupational injuries among full-time farmers in Finland. Journal of Agricultural Safety and Health, 18(2), pp. 83–93, 2012. https://doi.org/10.13031/2013.41348
[19] Liu, H.T. & Tsai, Y.L., A fuzzy risk assessment approach for occupational hazards in the construction industry. Safety Science, 50, pp. 1067–1078, 2012. https://doi.org/10.1016/j.ssci.2011.11.021
[20] Kogler, R., Near accidents with agricultural vehicles, machinery and equipment in Austria in the year 2013. Agricultural Engineering International: CIGR Journal, 17(1), pp. 141–157, 2015.
[21] Kogler, R., Quendler, E. & Boxberger, J., Occupational accidents with agricultural machinery in Austria. Journal of Agromedicine, 21(1), 61–70, 2016. https://doi.org/10.1080/1059924x.2015.1075451
[22] Underwood, P. & Waterson, P., Systemic accident analysis: examining the gap between research and practice. Accident Analysis & Prevention, 55, pp. 154–164, 2013. https://doi.org/10.1016/j.aap.2013.02.041
[23] European Union. The ‘Blue Guide’ on the implementation of EU products rules 2016. Official Journal of the European Union, C 272/1, 59, 2016.
[24] INAIL, ‘Informo’ database concerning occupational accidents causing fatal and serious injuries, available at https://appsricercascientifica.inail.it/getinf/informo/home_informo.asp, 2016 (accessed 13 April 2017).
[25] ASAPS, Database of the Association for traffic safety, available at http://www.asaps.it/,2016 (accessed 13 April 2017).
[26] INAIL, Osservatorio INAIL sugli infortuni nel settore agricolo e forestale (National observatory on accidents in agricultural and forestry activities), 2017.
[27] Akao, Y., Quality Function Deployment: Integrating Customer Requirements into Product Design. Productivity Press, Cambridge, MA, 1990.
[28] ReVelle, J.B., Moran, J.W. & Cox, C.A., The QFD Handbook, John Wiley & Sons, Inc., 1998.
[29] Fargnoli M. & Sakao T., Uncovering differences and similarities among Quality Function Deployment based methods in Design for X - benchmarking in different domains. Quality Engineering, pp. 1–23, 2016. https://doi.org/10.1080/08982112.2016.1253849
[30] Moores, B.M., Radiation safety management in health care – The application of Quality Function Deployment. Radiography, 12(4), pp. 291–304, 2006. https://doi.org/10.1016/j.radi.2005.07.011
[31] Wang, X., Wang, L., Xu, X. & Ji, P., Identifying employee turnover risks using modified quality function deployment. Systems Research and Behavioral Science, 31(3), pp. 398–404, 2014. https://doi.org/10.1002/sres.2282
[32] Carnevalli, J.A. & Miguel, P.C., Review, analysis and classification of the literature on QFD-Types of research, difficulties and benefits. International Journal of Production Economics, 114(2), pp. 737–754, 2008. https://doi.org/10.1016/j.ijpe.2008.03.006
[33] Wang, C.H. & Chen, J.N., Using quality function deployment for collaborative product design and optimal selection of module mix. Computers & Industrial Engineering, 63, pp. 1030–1037, 2012. https://doi.org/10.1016/j.cie.2012.06.014
[34] Vinayak, K. & Kodali, R., Benchmarking the quality function deployment models. Benchmarking: An International Journal, 20(6), pp. 825–854, 2013. https://doi.org/10.1108/bij-07-2011-0052
[35] Fargnoli, M. & Pighini, U., SCRM: a new Design Tool for improving Safety Level of Mechanical Systems. Proceedings of the 14th International Conference on Engineering Design, ICED 03, pp. 553–554 (exec.summ.), 2003.