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Introduction to research & development decision analysis

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  1. Introduction.
  2. Project valuation framework under uncertainty.
  3. Literature.
  4. Case studies.
  5. Conclusion.
  6. References.

A firm optimizes its market value when it successfully implements strategies that generate positive discounted cash flows applied against initial investments with net present value (NPV) greater than zero. One purely economic analysis, which serves as a guidepost to this work, applies a discounted cash flow (DCF) approach using stylised assumptions to estimate that the net present value (NPV) of R&D funding In other words, the authors ( Seddiqui et .al. 2007) go on to demonstrate, the DCF approach is not an appropriate economic analysis under uncertainty.In fact, many organizations evaluate projects by estimating their net present value (NPV). NPV is calculated by projecting expected future cash flows, ?discounting? the future cash flows by the cost of capital, and then subtracting the initial investment. Conventional wisdom directs us to undertake projects if NPV is positive, but this does not guarantee funding. Organizations typically consider other factors, which incorporate their ability to fund the initial investment given their capital structure, current operating cash flow positions, strategic considerations and financial expectations (April et al. 2006). In the absence of risks, we use the risk free opportunity cost of capital to discount back the cash flows associated with the strategy. If risks are involved, and the level of uncertainty is presumably known for the cash flows generated by the strategy, given efficient market hypothesis, we will use the corporate weighted average cost of capital to calculate the worth of the strategy.

[...] Simple and transparent decision analysis models, using decision trees , were used to applications of decision analysis provide logic and consistency to the selection process, allowing a variety of criteria, such as risk, speed of development and sales level, to be taken into account. The models were easily able to clarify the sequences of decisions to managers and allowed uncertainties to be explicitly addressed. Managers judged the process to be superior to the use of intuition or checklists, which are often used to select research projects. [...]

[...] The beneficiaries of the technology discussed here include executives responsible for capital investments, finance department analysts charged with capital budgeting, and technology managers responsible for project planning and implementation. Their needs provide compelling reasons to use the technology: Executives are dissatisfied with their current risk-assessment methods. They are under continual pressure to improve capital investment performance. They need technology to help communicate the analysis and clearly identify the reasons for specific investment decisions. They worry that competitors may adopt new and more advanced technology. Capital investment decisions are usually accomplished with traditional analyses that include net present value and mean-variance analysis. [...]

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