Discuss About The Associated Violations The Accident Severity?
There are many risk factors which are present in the transport sector (Gerr et al. 2014). The transport sector is related to the infrastructure related to the automation, roads, flyovers, trains and the river transport system. The risk assessment method is the technique which is used to find out several risk factors which can lead to create several damages in the transportation system. Risk can be caused due to several reasons and there are many effects which can be possible for the risk factors (Baumgartner et al. 2014). There are different types of risk factors like the internal and external risk factors. Risks can also be classified based upon the type of source of the risk. Some risks can be happened due to natural phenomenon and these are called the natural risks. Some risks can be happened due to human made reasons and those are called human made risks. There are also some risk factors which are occurred due to the in the risk assessment method there is also an important parameter which is used to refer the level of the risk factors (Dingus et al. 2016). Risks can be of different levels like high, low and medium. In case of transportation there are several types of risk factors which are caused due to the different types of the transportation infrastructure. Different industries are dependent upon the transport sector and the risks related to the transport sector are important to be assessed for this reason (Taylor et al. 2014). There are several concepts regarding the risks in the transport sector. Sometimes people think that the cars are safer than the airplanes and the ships (Fajersztajn et al. 2013). Actually it may differ sometimes as the actual safety is dependent upon the degree of risk. In reality the safety is concerned with people’s concept which may differ from the actual concept of the risk. The common risk factors are given below in case of the transportation sector.
From the above diagram it is cleared that there are many parameters on the basis of which the supplier is chosen. The parameters are cost, timing, quality and the services and warranties after supply (Zhang, Yau and Chen 2013). There are several amounts of the percentage which are also important for the determination of the best suuplier. From the above table of information it is cleared that the supplier 5 is the best choice as the supplier 5 provides the best possible cos and the time taken is also very good. They are also providing 100% quality which is the best among all the suuplies. The post sell services are also good enough keeping all the other parameters in consideration.
If the cost weighting is increased by 5% and the time weighting is decreased by 5% then the choice of the supplier is differed. In this case the cost is given more priority (Widanarko et al. 2014). IN this case the cost has the weighting of 45% and the time has the weighting of 15%. From the above table it is seen that supplier 1 has the best assessment score with respect to the cost. Supplier 5 although has the best assessment in the time but due to the increment of the weight age of the cost supplier 1 has become the best choice for the system.
In this case the quality is increased by almost 10 % and the weighting of the cost is decreased by 5% and for this reason the choice is also changed. As the quality had already 30% priority, after adding 10% more priority it is the most important factor and the effect of the time is negligible in this case In this case the quality has 40% and the cost has 35% weightings respectively. For that reason the supplier 3 is chosen as the best option (Sacks et al. 2014). The supplier 4 is also better in this case regarding the timing but as the quality is given the highest priority. Therefore the supplier 3 is undoubtedly the best choice. The sub criteria weighting is also important in this case.
For a manufacturing company there are many factors which are taken into account and the main factors are like cost timing, quality and the after selling services and the warranty. The main concern related to the system is that the best supplier is chosen based upon the priority which is set for the parameters (Kannan et al. 2014). In case of the situation when there is a tie among two or more suppliers with respect to the parameters then any one of the parameter should be given higher priority with respect to the requirements and any of the other parameters should be given lower priority. In that case the parameters are to be given priority based on the requirements. The main importance should be given on the parameters which are related to the productivity.
For the better choice of the suppliers it is important to make sure that the quality and the cost are given the maximum amount of priority (Kannan et al. 2015). These parameters are the most important parameters which can be used as the decisive factors to choose the best supplier. It is also important to consider the time of the project. It is also a decisive factor in case of considering the best and final offer (Govindan et al. 2015). It is clear that the highest amount of the percentage for all the parameter is important for making the best choice.
Baumgartner, J., Zhang, Y., Schauer, J.J., Huang, W., Wang, Y. and Ezzati, M., 2014. Highway proximity and black carbon from cookstoves as a risk factor for higher blood pressure in rural China. Proceedings of the National Academy of Sciences, 111(36), pp.13229-13234.
Dingus, T.A., Guo, F., Lee, S., Antin, J.F., Perez, M., Buchanan-King, M. and Hankey, J., 2016. Driver crash risk factors and prevalence evaluation using naturalistic driving data. Proceedings of the National Academy of Sciences, 113(10), pp.2636-2641.
Fajersztajn, L., Veras, M., Barrozo, L.V. and Saldiva, P., 2013. Air pollution: a potentially modifiable risk factor for lung cancer. Nature reviews. Cancer, 13(9), p.674.
Gerr, F., Fethke, N.B., Merlino, L., Anton, D., Rosecrance, J., Jones, M.P., Marcus, M. and Meyers, A.R., 2014. A prospective study of musculoskeletal outcomes among manufacturing workers: I. Effects of physical risk factors. Human factors, 56(1), pp.112-130.
Govindan, K., Rajendran, S., Sarkis, J. and Murugesan, P., 2015. Multi criteria decision making approaches for green supplier evaluation and selection: a literature review. Journal of Cleaner Production, 98, pp.66-83.
Kannan, D., de Sousa Jabbour, A.B.L. and Jabbour, C.J.C., 2014. Selecting green suppliers based on GSCM practices: Using fuzzy TOPSIS applied to a Brazilian electronics company. European Journal of Operational Research, 233(2), pp.432-447.
Kannan, D., Govindan, K. and Rajendran, S., 2015. Fuzzy Axiomatic Design approach based green supplier selection: a case study from Singapore. Journal of Cleaner Production, 96, pp.194-208.
Klauer, S.G., Guo, F., Simons-Morton, B.G., Ouimet, M.C., Lee, S.E. and Dingus, T.A., 2014. Distracted driving and risk of road crashes among novice and experienced drivers. New England journal of medicine, 370(1), pp.54-59.
Sacks, F.M., Carey, V.J., Anderson, C.A., Miller, E.R., Copeland, T., Charleston, J., Harshfield, B.J., Laranjo, N., McCarron, P., Swain, J. and White, K., 2014. Effects of high vs low glycemic index of dietary carbohydrate on cardiovascular disease risk factors and insulin sensitivity: the OmniCarb randomized clinical trial. Jama, 312(23), pp.2531-2541.
Taylor, J.B., Goode, A.P., George, S.Z. and Cook, C.E., 2014. Incidence and risk factors for first-time incident low back pain: a systematic review and meta-analysis. The Spine Journal, 14(10), pp.2299-2319.
Widanarko, B., Legg, S., Devereux, J. and Stevenson, M., 2014. The combined effect of physical, psychosocial/organisational and/or environmental risk factors on the presence of work-related musculoskeletal symptoms and its consequences. Applied ergonomics, 45(6), pp.1610-1621.
Zhang, G., Yau, K.K. and Chen, G., 2013. Risk factors associated with traffic violations and accident severity in China. Accident Analysis & Prevention, 59, pp.18-25.
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