Qualitative and quantitative risk assessment
Table of Contents
Qualitative and quantitative risk assessment 1
Individual report
Qualitative and quantitative risk assessment
It is significant to conduct risk assessment as it helps to identify the risks so that it enables further to mitigate the risks with suitable actions. There are two risk assessment approaches: qualitative and quantitative and each has benefits as well as limitations in a comparison to each other. For instance, quantitative risk assessment has consideration of the numerical data to assess the risk. For processing of the numerical data, there is use of the specific tools and techniques such as Monte Carlo simulation. The outcomes are used to estimate the risk distribution, risk probability and other significant aspects associated to the project. The course work has used Monte Carlo simulation to estimate the revenue, profitability and batch size for an organisation who is willing to produce souverain books during the event. Qualitative risk assessment uses descriptive data in the assessment and on analysis, it produces comprehensive results (Simmons, 2017). Such data is hard to process using statistical tools and therefore, the process of qualitative risk assessment demands time and efforts. However, the technique of qualitative risk assessment is significant as it helps to understand the nature of the risks and therefore, risk prioritisation becomes easier and accurate.
There is a similarity between two techniques in term of uncertainties in values. The coursework has consideration of the outcomes and variables for simulation so that there is inclusion of uncertain values while adjusting the demands. It has indirect impact on the outcome. In qualitative risk assessment, there is use of a risk assessment framework and risk matrix so that each risk is assessed for its probability of occurrence and potential impact on the decisions and outcomes (Chen and Chen, 2017). However, there is no standard approach to assess the risks and their impacts on the outcome. Therefore, the both techniques have concerns with inclusion of uncertain values. Further, the results are mainly based on the assessment of the historical data available for the analysis.
Advantages and disadvantages
Quantitative risks are predicted using Excel or more sophisticated tools. These tools are designed with required functions and features so that it becomes easier to perform the analysis and obtain accurate and reliable data. However, it also makes the technique expensive and complex, especially when there is no need of sophisticated tools for qualitative risk assessment. If there is significant error in a dependent variable in the data, it is quite possible to have erroneous outcome (Garg et al., 2019). The core benefit is that the quantitative risk assessment has no impact of the personal bias and perceptions because there is high dependency on the data. However, qualitative assessment has impact of personal bias so that the output might be different when multiple perspectives are applied. The analysis of risks using qualitative technique is poor because of personal bias and no inclusion of the data.
Application of technique
The coursework uses quantitative approach for prediction of the profit, batch size and other requirements in the simulation. The selected technique is significant to estimate the outcome considering frequency and a number of the sample. Therefore, the data outcomes are not much away from the accurate predictions. Further, the technique has applications in automation of the decision-making process for the organisation as there is need of minimum inputs and efforts to generate the intended results (Chen and Chen, 2017). The analysis indicates that the organisation can generate the profitability of £14,000 when it is planning to produce 2,000 books. The same technique is also used to identify mean and standard deviation value. Therefore, the technique has application to predict or forecast the results using quantitative data (Garg et al., 2019).
References
Chen, D.G. and Chen, J.D. eds., 2017. Monte-Carlo simulation-based statistical modeling. Springer.
Simmons, D.C., 2017. 2.1 Qualitative and quantitative approaches to risk assessment. Risk, 2, pp.1-1.
Garg, N., Yadav, S. and Aswal, D.K., 2019. Monte Carlo simulation in uncertainty evaluation: strategy, implications and future prospects. Mapan, 34(3), pp.299-304.
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