Investigation of the industrial injuries state at the machine-building enterprises of the western region of Ukraine
Received: September 14, 2021
Revised: November 18, 2021
Accepted: December 28, 2021
Lviv Polytechnic National University

The article describes the prerequisites for creating an automated system for planning measures to prevent occupational injuries at machine-building enterprises. The results of a study of occupational injuries based on statistical data depending on the employee’s experience, gender, working conditions, days of the week and month are given for the leading machine-building enterprises. The research of  the influence of technical and economic indices of  machine-building enterprises  of  the  Western region of Ukraine on industrial injuries is described. It has been established that out  of all significant technical and economic indicators of machine-building enterprises, only 3 factors significantly impact the level of occupational injuries: stock armament, energy armament, and occupational health and safety costs. The practical value of research results is to  adjust  plans to prevent injuries, taking into account situations with the highest probability  of employee  emergencies. Further research will develop and implement an automated system for planning injuries at the machine-building enterprise.  

[1] N. Stupnytska, V. Stupnytskyy, “Optimization model for planning set of measures to prevent occupational injuries  in  machine-building  enterprises”,  Journal  of  KONBiN,  Vol.50, no 1,  pp. 117-129,  2020., 
[2] J. Branke, K. Deb, K. Miettinen, R. Slowiński, Multiobjective Optimization Interactive and Evolutionary Approaches. Berlin Heidelberg: Springer-Verlag, 2008, 
[3] S. Polinder, H. Toet, M. Panneman , Ed van Beeck. Methodological approaches for cost–effectiveness and cost–utility analysis of injury prevention measures. World Health Organization Report , 2011. 
[4]  K.  Torén,  T.  Sterner.  “How  to  promote  prevention  economic  incentives  or  legal  regulations  or  both?”, Work Environ Health, vol. 29, no 3, 2003., 
[5]  N.  Stupnytska,  “Technical  and  Economic  Analysis  of  the  Consequences of Occupational  Injuries  at Machine-Building Enterprises”, Journal of KONBiN, Vol.51, no 1, p.1-13, 2021.,; 
[6]  T.  Conklin,  Pre-Accident  Investigations:  An  Introduction  to  Organizational  Safety,  London:  Kindle Edition, 2012.,  
[7] L. Harms-Ringdahl, Safety Analysis. Principles and Practice in Occupational Safety. London : CRC Press, 2001.,;  
[8]  N.  Hyatt,  Guidelines  for  Process  Hazards  Analysis  (PHA,  HAZOP),  Hazards  Identification, and  Risk Analysis, Boca Raton: CRC Press, 2003.,  
[9]  F. A. Manuele, Advanced Safety Management: Focusing on Z10 and Serious Injury Prevention, London: Kindle Edition, Wiley, 2014.,; 
[10] J. Mandel, The Statistical Analysis of Experimental Data, NY: Dover Publications, 1984. 
[11] S. Shikano, T. Bräuninger, M. Stoffel, Statistical Analysis of Experimental Data. In: Kittel B., Luhan W.J., Morton R.B. (eds)  Experimental  Political Science. Research Methods Series. London: Palgrave  Macmillan, 2012.,; 
[12] R. Little, D. Rubin, Statistical  Analysis with Missing Data, London: Wiley, 2019.,;  
[13]  G.F. Newell, Applications of Queueing Theory. London: Chapmen and Hall, 2013. 
[14]  P.E Greenwood, M.S. Nikulin, A guide to chi-squared testing. New York: Wiley, 1996. 
[15]  T. E. McSween,  Values-Based  Safety  Process:  Improving  Your  Safety  Culture  with  Behavior-Based Safety. London: John Wiley & Sons, 2003.,;  
[16]  E. Stemn, “Analysis of Injuries in the Ghanaian Mining Industry and Priority Areas for Research”, Safety and Health at Work, Vol. 10, no 2, pp. 151-165, 2019.,;