In the last article we analyzed some of the facts about Work Break Down and Estimation planning techniques, today we will focus on Decision Making; now this one requires deep knowledge and proficiency of the situation and is widely used for financial situations, let’s go through it.
It is used to support decision-making when dealing with complex, difficult, or uncertain situations; it examines and models the possible consequences of different decisions.
Effective decision analysis requires that the analyst fully understands the goals and objectives, nature, areas of uncertainty of the decision/problem as well as the consequences of each possible decision.
In the case of financial valuation techniques you most include:
1.Discounted Cash Flow: future value on a specific data.
2.Net Present Value: future view of costs and benefits converted to today’s value.
3.Internal Rate of Return: the interest rate (or discount) when the net present value is equal to zero.
4.Average Rate of Return: estimate of rate of return on an investment.
5.Pay Back Period: the amount of time it takes for an investment to pay for itself.
6.Cost-Benefit Analysis: quantification of costs and benefits for a proposed new solution.
For the non-financial valuations techniques include:
1.Uncertainty: relevant to a decision problem when it is impossible to know which outcome will occur.
2.Trade-offs: relevant whenever a decision problem involves multiple, possibly conflicting, objectives.
3.Elimination of dominated alternatives: any option that is clearly inferior to some other option.
4.Ranking objectives on a similar scale: One method of converting rankings to a similar scale is proportional scoring. Using this method, the best outcome is assigned a rating of 100, the worst a rating of 0, and all other outcomes are given a rating based on where they fall between those two scores.
Effective technique to determine the expected value of an alternative scenario to the organization.
Provides decision makers with quantitative measures upon which to make project investment decisions.
May force stakeholders to honestly assess the importance they place on different alternatives.
Requires specialized knowledge and skills, including mathematical knowledge, an understanding of probability, and similar concepts.
Results may be treated as more certain than they actually are, if decision-makers do not understand the limitations of the model and the assumptions behind it.
Decision-makers may be reluctant to revisit decisions, even when more information is available on areas of uncertainty that might change the optimal decision.