A Model for Estimating Amount of Personal Protective Equipment Employed within Occupational Health and Safety

Year 2020, Volume: 23 Issue: 3, 895 - 900, 01.09.2020

Mehmet Burak Şenol Metin Dağdeviren

https://doi.org/10.2339/politeknik.723956

Cited By: 1

Abstract

When Occupational Health and Safety studies are examined, it is seen that many different methods are employed in the risk analysis process, but there is no study in the literature to estimate the amount of Personal Protective Equipment (PPE) usage, which is one of the precautions to be taken after risk analysis. In this study, a model was developed based on Autoregressive Integrated Moving Averages (ARIMA) for estimation of PPE usage of enterprises. In an enterprise operating in the white goods sector, the usage data of respiratory protective masks in 2015-2019 has been examined and the need for respiratory protective masks that may arise for 2020-2022 has been estimated. The effectiveness of our method was examined by comparing the estimates made with the amount of PPE usage. Increasing relationships between man and machine in the industrial era require the creation of human-compatible businesses, and in this sense, ergonomics can benefit from occupational health and safety practices in order to improve productivity. The PPE estimates made gave close results to the actual use of respiratory protective masks. This study for the estimation of the amount of use of protective masks can also be applied for the prediction of other personal protective equipment used throughout the business in the future.

Keywords

Occupational Health and Safety, Ergonomics, Personal Protective Equipment, Forecasting Methods, ARIMA

İş Sağlığı ve Güvenliği Kapsamında Kullanılan Kişisel Koruyucu Donanım Miktarının Tahminine Yönelik Bir Model

Abstract

İş Sağlığı ve Güvenliği (ISG) çalışmaları incelendiğinde risklerin analizi sürecinde çok farklı yöntemlerin kullanıldığı görülmekle birlikte, risk analizi sonrasında alınacak önlemlerin başında yer alan Kişisel Koruyucu Donanım (KKD) kullanımı miktarının tahminine yönelik bir çalışma literatürde görülememiştir. Bu çalışmada işletmelerin KKD kullanımlarının tahmini için Otoregresif Entegre Hareketli Ortalamalar (ARIMA) temel alınarak bir model geliştirilmiştir. Beyaz eşya sektöründe faaliyet gösteren bir işletmede 2015-2019 yıllarındaki solunum koruyucu maskelerin kullanım verileri incelenmiş, 2020-2022 yılları için ortaya çıkabilecek solunum koruyucu maskesi ihtiyacı tahmin edilmiştir. Yapılan tahminlerle KKD kullanım miktarları karşılaştırılarak yöntemimizin etkinliği incelenmiştir. Endüstri çağında insan-makine arasında artan ilişkiler, insana uyumlu işletmelerin oluşturulmasını gerektirmektedir ve bu anlamda ergonomi bilimi, verimliliği artırmak için iş sağlığı güvenliği uygulamalarından istifade edebilir. Yapılan KKD tahminleri solunum koruyucu maskelerin gerçekleşen kullanım miktarlarına yakın sonuçlar vermiştir. Koruyucu maskelerin kullanım miktarının tahmini için yapılan bu çalışma gelecekte işletme genelinde kullanılan diğer kişisel koruyucu donanımların tahmini için de uygulanabilir.   

Keywords

İş Sağlığı ve Güvenliği, Ergonomi, Kişisel Koruyucu Donanım, Tahmin Yöntemleri, ARIMA

References

• 1. Marhavilas P.K., Koulouriotis D., Gemeni V., “Risk analysis and assessment methodologies in the work sites: on a review, classification and comparative study of the scientific literature of the period 2000–2009”, Journal of Loss Prevention in the Process Industries, 24:5, 477-523, 2011.
• 2. Pinto A., Nunes I.L., Ribeiro R.A., “Occupational risk assessment in construction industry–overview and reflection”, Safety Science, 49:5, 616-624, 2011.
• 3. Zhang Z., Chu X., “Risk prioritization in failure mode and effects analysis under uncertainty”, Expert Systems with Applications, 38, 206-214, 2011.
• 4. Padma T., Balasubramanie P., “A fuzzy analytic hierarchy processing decision support system to analyze occupational menace forecasting the spawning of shoulder and neck pain”, Expert Systems with Applications, 38, 15303-15309, 2011.
• 5. Liu H.C., Liu L, Liu N., Mao L. X., “Risk evaluation in failure mode and effects analysis with extended VIKOR method under fuzzy environment”, Expert Systems with Applications, 39, 12926-12934, 2012.
• 6. Hadjimichael M., “A fuzzy expert system for aviation risk assessment”, Expert Systems with Applications, 36, 6512-6519, 2009.
• 7. Kutlu A.C., Emekçioğlu M., “Fuzzy failure modes and effects analysis by using fuzzy TOPSIS based fuzzy AHP”, Expert Systems with Applications, 39, 61-67,2012.
• 8. Mokhtari K., Ren J., Roberts C., Wang J., “Decision support framework for risk management on sea ports and terminals using fuzzy set theory and evidential reasoning approach”, Expert Systems with Applications, 39, 5087-5103, 2012.
• 9. Fan Z.P., Suo W.L., Feng B., “Identifying risk factors of it outsourcing using interdependent information: an extended DEMATEL method”, Expert Systems with Applications, 39, 3832-3840, 2012.
• 10. Ceylan H., Başhelvacı V.S., “Risk analysis with risk assessment matrix method: an application”, International Journal of Engineering Research and Development, 3 (2), 25-33, 2011.
• 11. Samantra C., Datta S., Mahapatra S.S., “Risk assessment in it outsourcing using fuzzy decision-making approach: an Indian perspective”, Expert Systems with Applications, 41, 4010-4022, 2014.
• 12 . Mandal S., Maiti J., “Risk analysis using FMEA: fuzzy similarity value and possibility theory based approach”, Expert Systems with Applications, 41, 3527-3537, 2014.
• 13. Yılmaz N., Şenol, M.B., “İş sağlığı ve güvenliği risk değerlendirme süreci için bulanık çok kriterli bir model ve uygulaması”, Journal of the Faculty of Engineering and Architecture of Gazi University, 32:1, 77-87, 2017.
• 14. Personal protective equipment guidelines for health care facility staff, Annals of Emergency Medicine, 68:3, 406-407, 2016.
• 15. Montgomery D. C., Jennings C., Kulahci M., “Introduction to time series analysis and forecasting”, USA, 2008.
• 16. Lu Y., AbouRizk S.M., “Automated box–jenkins forecasting modeling”, Automation in Construction, 18:5, 547-558, 2009.
• 17. Lee C.M., Ko C.N., “Short-term load forecasting using lifting scheme and ARIMA models”, Expert Systems with Applications, 38:5, 5902-5911, 2011.
• 18. Ediger V. Ş., Akar S., “ARIMA forecasting of primary energy demand by fuel in Turkey”, Energy Policy, 35:3, 1701-1708, 2007.
• 19. Erdoğdu E., “Electricity demand analysis using cointegration and ARIMA modelling: a case study of Turkey”, Energy Policy , 35,1129-1146, 2007.
• 20 Christodoulos C., Michalakelis C., Varoutas D., “Forecasting with limited data: combining ARIMA and diffusion models”, Technological Forecasting and Social Change, 77:4, 558-565, 2010.
• 21. Pappas S.Sp., Ekonomou L., Karamousantas D.Ch., Chatzarakis G.E., Katsikas S.K., Liatsis P., “Electricity demand loads modeling using autoregressive moving average (ARMA) models”, Energy, 33, 1353-1360, 2008.
• 22. Bianco V., Manca O., Nardini S., “Electricity consumption forecasting in italy using linear regression models”, Energy, 34, 1413-1421 , 2009.
• 23. Dilaver Z., Hunt L.C., “Industrial electricity demand for Turkey: a structural time series analysis”, Energy Economics, 33:33, 426-436, 2011.
• 24 Abdel-Aal R.E., Al Gami A.Z., “Forecasting monthly electric energy consumption in esatern saudi arabia using univariate time series analysis”, Energy , 22:11, 1059-1069, 1997. 25.Unakıtan G., Akdemir B., “Tractor demand projection in Turkey”, Biosystems Engineering, 97:1, 19-25, 2007.
• 26. Yurekli K., Kurunc A., “Simulating agricultural drought periods based on daily rainfall and crop water consumption” , Journal of Arid Environments, 67, 629-640 , 2006.
• 27. Chavez S.G., Bernat J.X., Coalla H.C., “forecasting energy production and consumption in northern Spain”, Energy, 24, 183-198, 1999.
• 28. Zhang G.P., “Time series forecasting using hybrid ARIMA and neural network model”, Neurocomputing , 50, 159-175 , 2001.