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This problem is about the PDC Company that is a government company. They are asked to develop and support the Oman Company. PDC is expected to maximize the financial return of the Oman Company. PDC gave two possible outcomes: first one is demand and second one is weak demand.
A decision tree is a type of flowchart that is used to visualize the decision making process through mapping the different course of action as well as their outcomes. Decision trees is usually three types
Expected monetary value is a balance of its probability and the impact over the range of the possibilities scenario. If there is a choice between two scenario this has decision is taken by the EMV that provide greater potential payoff
We have chosen the D3 scenario because it has given the best and highest EMV value as compared to alternative ones. It is given the highest profit of 14.2 million profit margin that is good to have
EVM helps in realistic project planning of the concern. It also helps in gaining the real time visualization of the data in the centralized manner. Also helps in measuring the budgets and accuracies during the life cycle of the project. It also helps in managing the risk of the concerns. It provides the transparency of the performance of the project, measuring the cost performance, physical process and also helps in identifying the potential issues.
Probability = P
Hard ware = H
Software = S
Other = O
Probability of hardware
P (H) = 0.1
P (F/H) = 0.9
Probability of software
P(S) =0.6
P (F/S) =0.2
Probability of Other
P (O) =0.3
P (F/O) =0.5
P (B/F) = P(S) P (F/S)/P (H)*P (F/H) + P(S)*P (F/S) + P (O)*P (F/O) = 0.6 * 0.2/ 0.1*0.9 +0.6*0.2 +0.3*0.5= 0.12/0.36
= 0.33
Bayes theorem is mainly used for calculation of probability. This probability is conditional where it is difficult to decide anything. Where it is difficult to come to any conclusion there Bayes theorems are used. This theorem is used in machine learning also. Here the probability is 0.33.
Exposure of risk is the regularity of potential time ahead loss ensue from a particular event or activity. Basically risk exposure is used for any business frequently risking ranks according to their probability of happen multiplied by chance loss if they may do. By ranking the prospect of these possible losses, an organization or business can control the small losses and may be notable sufficient to authorize investment.
Some common risk are
Journals
Willumsen, P., Oehmen, J., Stingl, V. and Geraldi, J., 2019. Value creation through project risk management. International Journal of Project Management, 37(5), pp.731-749.
Samimi, A., 2020. Risk Management in Information Technology. Progress in Chemical and Biochemical Research, 3(2), pp.130-134.
Araz, O.M., Choi, T.M., Olson, D.L. and Salman, F.S., 2020. Data Analytics for Operational Risk Management. Decis. Sci., 51(6), pp.1316-1319.
Care, D., 2021. Addendum. 10. Cardiovascular Disease and Risk Management: Standards of Medical Care in Diabetes—2021. Diabetes Care 2021; 44 (Suppl. 1): S125–S150. Diabetes Care, 44.
Baryannis, G., Validi, S., Dani, S. and Antoniou, G., 2019. Supply chain risk management and artificial intelligence: state of the art and future research directions. International Journal of Production Research, 57(7), pp.2179-2202.
Zhou, S. and Yang, P., 2020. Risk management in distributed wind energy implementing Analytic Hierarchy Process. Renewable Energy, 150, pp.616-623.
Babich, V. and Kouvelis, P., 2018. Introduction to the special issue on research at the interface of finance, operations, and risk management (iFORM): Recent contributions and future directions. Manufacturing & Service Operations Management, 20(1), pp.1-18.
Kamiya, S., Kang, J.K., Kim, J., Milidonis, A. and Stulz, R.M., 2021. Risk management, firm reputation, and the impact of successful cyberattacks on target firms. Journal of Financial Economics, 139(3), pp.719-749.
Kuhlicke, C., Seebauer, S., Hudson, P., Begg, C., Bubeck, P., Dittmer, C., Grothmann, T., Heidenreich, A., Kreibich, H., Lorenz, D.F. and Masson, T., 2020. The behavioral turn in flood risk management, its assumptions and potential implications. Wiley Interdisciplinary Reviews: Water, 7(3), p.e1418.
Borkovskaya, V., Degaev, E. and Burkova, I., 2018. Environmental economic model of risk management and costs in the framework of the quality management system. In MATEC web of conferences (Vol. 193, p. 05027). EDP Sciences.
Yue, X.G., Shao, X.F., Li, R.Y.M., Crabbe, M.J.C., Mi, L., Hu, S., Baker, J.S. and Liang, G., 2020. Risk management analysis for novel coronavirus in Wuhan, China. Journal of Risk and Financial Management, 13(2), p.22.
Babenko, V., Lomovskykh, L., Oriekhova, A., Korchynska, L., Krutko, M. and Koniaieva, Y., 2019. Features of methods and models in risk management of IT projects. Periodicals of Engineering and Natural Sciences, 7(2), pp.629-636.
Hopkin, P., 2018. Fundamentals of risk management: understanding, evaluating and implementing effective risk management. Kogan Page Publishers.
Buganová, K. and Šimí?ková, J., 2019. Risk management in traditional and agile project management. Transportation Research Procedia, 40, pp.986-993.
Klotz, L., Havla, J., Schwab, N., Hohlfeld, R., Barnett, M., Reddel, S. and Wiendl, H., 2019. Risks and risk management in modern multiple sclerosis immunotherapeutic treatment. Therapeutic advances in neurological disorders, 12, p.1756286419836571.
Khachaturyan, M.V., Klicheva, E.V. and Velikorossov, V.V., 2019. Digital Mechanisms of Development of Possessory Risk Management Systems under New Economic Conditions. In Proceedings of the 2019 International Conference on Politics, Economics and Management (ICPEM 2019). Lecture Notes in Economics, Management, and Social Sciences. Clausius Scientific Press (Vol. 5, pp. 6-10).
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