Question

Telecommunication Governance 1:

Note: Add at least 4 bibliographical sources

Explain in detail, illustrate examples and applications:

Explain the impact vs. probability risk management actions and their individual application.

Answer 1

Risk management actions can be measured with the help of the two primary tools which are - Impact and Probability.

Impact - A risk is defined by its nature and it always have a negative consequences.

Probability - A risk is an event that may occurs.The occurrence can be defined in the range above 0 percent to below 100 percent.

The impact vs probability risk management actions are -

i. Low impact/ low probability - Risk is at the bottom left corner which is in low level and which is having low impact.The probability of occurrence is negligible.

ii. Low impact/high probability - Risk is at the bottom left corner.Risk is at the moderate level and probability of occurrence will be in a moderate level, but this risk can be reduced when occurs.

iii. High impact/low probability - Risk is at the bottom right corner and should be provided with high level of importance if it occurs, but the probability of occurrence is low.However,necessary steps must be taken to avoid the risk and its occurrence.

iv. High impact/high probability - Risk level is at the right corner.It should be provided with critical importance and also be given a first priority high level of importance.The probability of occurrence of this kind of risk is extremely high.

Applications of Risk Management and its examples

• NASA's international Space station shows high level and low level of risk.
• It is also implies in information technology as IT risk in which risk related to information technology.
• In petroleum and natural gas is regulated by safe case or device in many countries to avoid hazardous risk.
• It is important to assess risk in regard to natural disasters like floods,earthquake.

Bibliography -

Women’s Occupational Health: Improving Medical Protocols with Artificial Intelligence Solutions. Gerassis, S.; Abad, A.; Saavedra, Á.; García, J., F.; and Taboada, J. pages 1193-1199. Springer, Cham, 9 2019.

Societal Risk and Resilience Analysis: Dynamic Bayesian Network Formulation of a Capability Approach. Tabandeh, A.; Gardoni, P.; Murphy, C.; and Myers, N. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 5(1): 04018046. 3 2019.

Assessment of Safety Integrity Level by simulation of Dynamic Bayesian Networks considering test duration. Simon, C.; Mechri, W.; and Capizzi, G. Journal of Loss Prevention in the Process Industries, 57: 101-113. 1 2019.

Bayesian network model for quality control with categorical attribute data. Cobb, B., R.; and Li, L. Applied Soft Computing, 84:105746. 11 2019