Accenture Academy Blog
Do you feel confident your company invests in the projects that create the most value? Have you ever had a project rejected and not understood the methods used to evaluate its potential?

Rarely does an organization spend money without subjecting the expenditure to a rigorous evaluation process. This applies to investments in equipment, facilities, IT, marketing, R&D, HR, and beyond. Keeping expenses low and financial performance high is too important to take the investment process lightly. Capital budgeting methods allow organizations to make better investment decisions and ensure they invest the capital they raise in projects that create the most value for their investors.

The most widely used capital budgeting methods are:
  • Net present value (NPV).
  • Internal rate of return (IRR).
  • Payback and discounted payback.
  • Profitability index (PI).
Each method uses its own set of assumptions and provides a different perspective on the attractiveness of investments. Each method also has strengths and weaknesses to recommend or rule out its appropriateness within specific situations. Relying on a single method when you may need multiple methods or relying on improper assumptions can make poor investments look attractive and vice versa. Understanding the perspectives each method offers can help you recommend projects that will build long-term company value.

Good capital budgeting analysis is more than making a set of assumptions, calculating cash flows and obtaining a number for NPV or IRR. The metrics you use should be consistent with the reasoning for why accepting a proposed project creates or destroys value. To make solid investment decisions, you should also answer the questions, what is most important when you ration capital? What happens when you test your assumptions?

Does your company use a capital budgeting process that provides the best results? The Accenture Academy course Applying Capital Budgeting Methods examines the main capital budgeting methods, applies them to practical examples, and reviews both how to interpret the results and test their underlying assumptions.

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