Analysis of risk and uncertainity

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Analysis of risk and uncertainity

  1. 1. Sooraj R, DMS, SOM, PU.
  2. 2.   The lack of certainty, A state of having limited knowledge where it is impossible to exactly describe existing state or future outcome, more than one possible outcome. Uncertainty Sooraj R, DMS, SOM, PU.
  3. 3.   A state of uncertainty where some possible outcomes have an undesired effect or significant loss. Risk Sooraj R, DMS, SOM, PU.
  4. 4.  1) It’s a technique to identify and assess factors that may jeopardize the success of a project or achieving a goal. 2) It helps to define preventive measures to reduce the probability of these factors from occurring. 3) It identify countermeasures to successfully deal with these constraints. What is Risk analysis Sooraj R, DMS, SOM, PU.
  5. 5.  Identifying The Risk  Quantifying The Risk  Risk Analysis  Presenting The Results  Beyond Presentation Phases of risk analysis Initial workshop(s) Preliminary quantification Preliminary modelling Detailed workshops and interviews Further risk modelling Using the analysis... Focus in on signifi cant risks Sooraj R, DMS, SOM, PU.
  6. 6.   The initial workshop sessions held to identify the risks.  The output of the identification exercise: the risk register.  The use of existing models to identify risks. Identifying the risks Sooraj R, DMS, SOM, PU.
  7. 7.   The output from the risk identification phase is normally a risk register; this is a document that contains some or all of the following for each risk.  · generic risk - the general heading the risk falls under, e.g. construction risks.  · specific risk - the particular risk being considered, e.g. bad weather delays construction. Risk register Sooraj R, DMS, SOM, PU.
  8. 8.   A brief commentary on which risks should be quantified.  A discussion of the theoretical background to quantifying risks.  The benefits of separating risks out.  A review of the issues that arise when quantifying risks with experts. Quantifying risks Sooraj R, DMS, SOM, PU.
  9. 9.   Industrial analysis.  Identifying the significant risks. Risk analysis Sooraj R, DMS, SOM, PU.
  10. 10.   It is a technique that takes the distributions that have been specified on the inputs to the model, and uses them to produce a probability distribution of the output of interest. It does this by running through the following sequence of actions as many times as the user specifies. Industrial analysis simulation Sooraj R, DMS, SOM, PU.
  11. 11.   Identifying the relative significance of the risks relating to each input is an important step in risk analysis: once the significant risks have been identified more time can be spent on these, at the expense of the less important ones. It may be necessary to revisit the assumptions around the significant risks, and to do more research. Also, if a small number of risks dominate all others they should be disaggregated into smaller components. Identifying the significant risks Sooraj R, DMS, SOM, PU.
  12. 12.   Graphical presentation.  Statistical measures.  Presenting it in English. Presenting the results Sooraj R, DMS, SOM, PU.
  13. 13.   Valuing risk.  NPV.  Risk analysis in PFI transactions. Beyond presentation Sooraj R, DMS, SOM, PU.
  14. 14.   In attempting to value risk there are two measures that can be considered: 1. · The expected value of the risk. 2. · The range of uncertainty. Valuing risk Sooraj R, DMS, SOM, PU.
  15. 15.   In practice, it is often the case that we are not interested in the risk around one cost, or even total cost. Usually, it is some other measure of project return, such as net present value (NPV), that is important. It might seem obvious that a distribution of NPVs would provide useful additional information about the risks in a project. What does a distribution of NPVs mean? Sooraj R, DMS, SOM, PU.
  16. 16.   Risk analysis is an integral part of the PFI procurement process, and we outline below the specific requirements for including risk within PFI transactions, from the procurer’s (i.e. the public sector’s) viewpoint. Risk analysis Sooraj R, DMS, SOM, PU.
  17. 17.  Intelligent Software for Business Planning Sooraj R, DMS, SOM, PU.
  18. 18.   The concepts of closeness to the core business and market attractiveness can be combined to analyse the risk of investing in new offerings. The proximity of the new offering to the core business is measured by its proximity to current offerings and current markets. Description of the Model Sooraj R, DMS, SOM, PU.
  19. 19.  The expert system will position your enterprise on the chart based upon your description of:  technology  familiarity with the materials  special finishes  quality standards  suppliers bargaining power  threat of substitutes  threat of new entrants  competitive rivalry  bargaining power of the buyers You can trace through the supporting analysis and its conclusions, adjusting your input until you are satisfied your description accurately characterizes your enterprise. Characterize Your Enterprise Sooraj R, DMS, SOM, PU.
  20. 20.  Ideal Risky Low Potential Poor Prospect Close to Core Business High Market Attractiveness Distant from Core Business High Market Attractiveness Close to Core Business Low Market Attractiveness Distant from Core Business Low Market Attractiveness Offerings in this category represent the least risk and will be ideal candidates for development. Offerings in this quadrant are risky to develop since they stray from the core business. They will need a high level of investment, both in terms of resources and expertise. Proceed only if the long-term corporate strategy is intended to develop in this way. The decision to proceed should be based on the evaluation of the market potential. The low attractiveness of the market may be a benefit since it will be less lucrative for competitors. Offerings in this quadrant are poor prospects. They depart from the core business and offer low market attractiveness. Analysis of Your Enterprise Position Sooraj R, DMS, SOM, PU.

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