Maximizing Project Success Through Strategic Management

1. Chapter 2 Strategic Management and Project Selection

2. Problems With Multiple Projects • Delays in one project delays others • Inefficient use of resources • Bottlenecks in resource availability

3. Project Results • 30 Percent late • Over half 190 percent over budget • Over half 220 percent late

4. Challenges • Making sure projects closely tied to goals and strategy • How to handle growing number of projects • How to make projects successful

5. Project Management Maturity • Project management maturity refers to mastery of skills required to manage project competently • Number of ways to measure • Most organizations do not do well

6. Project Selection and Criteria of Choice • Project selection… • Evaluating • Choosing • Implementing • Same process as other business decisions

7. Types of Companies • Companies considering projects fall into two broad categories: • Companies whose core business is completing projects • Companies whose core business is something else • They can also be broken down as: • Companies looking at projects to do for others • Companies looking at projects to do for themselves

8. Project Companies • Must select which projects they will bid on • Generally based on… • Their expertise • Resource they have availability • Their chance of winning bid • Preparing a bid is expensive • They do not want to waste that effort on bids where they are unlikely to be successful

9. Non-Project Companies • Must decide which potential projects they will pursue • Available capital is the major constraint • Profitability is often the major criteria • Must evaluate approaches when there is more than one project that can accomplish a goal

10. Models • Models are used to select projects • All models simplify reality • That is, they only look at the key variables involved in a decision • The more variables included in a model, the more complex it becomes • Simpler models usually work better

11. Types of Models • Stochastic Model • A model that includes the probabilities of events occurring within the model. In other words, the same inputs might yield different outputs at different runs. Also known as a probabilistic model. • Deterministic Model • A model that does not include probabilities. Given the same inputs, the outputs will always be the same.

12. Criteria For Project Selection Models • Companies only want to undertake successful projects • Projects that fail waste resources and hurt profitability and competitiveness • Projects that succeed improve profitability and competitiveness • It is not possible to know ahead of time if a project will succeed or fail • In fact, there is a continuum of possible results from total success through absolute failure

13. Criteria (Continued) • Companies need a way of weeding out the bad projects while keeping the good ones • No model can predict with absolute certainty • No model could predict • The Exxon Valdez wreck • The explosion of the Challenger • What we want is a model with a “good batting average”

14. Model Criteria • Realism • Capability • Flexibility • Easy to use • Inexpensive • Easy to implement

15. Realism • Needs to include all objectives of the firm • Needs to include the firms expertise as well as its limitations • Needs to report results in a fashion that allows different projects to be compared, e.g. how do we compare a project to lower production cost and one to raise market share

16. Capability • Model needs to be sophisticated enough to deal with all projects • Varying resource requirements • Varying time periods • Varying probabilities of success • Needs to be able to select the optimum projects among all contenders

17. Flexibility • Needs to be able to work with all projects • Needs to be updated as the firm and its environment evolves

18. Easy to Use • Needs to be quick to gather the data and easy to use • Easy to be able to “fit” the project in the model

19. Inexpensive • Do not want the model to eat up all the savings that result from using the model • Expenses include the cost of writing and maintaining the model • Also includes the expense of gathering the data needed by the model

20. Easy to Implement • This is less of an issue with modern spreadsheets • However, a model to be used to evaluate all the firm’s projects should be centrally maintained

21. The Nature of Project Selection Models • Models turn inputs into outputs • Managers decide on the values for the inputs and evaluate the outputs • The inputs never fully describe the situation • The outputs never fully describe the expected results • Models are tools • Managers are the decision makers

22. Different Factors Affecting Outcome • Many factors affect the outcome of a project • Some are one-time factors • The cost of an item • Others are reoccurring • Maintenance • Not all factors are equally important • Critical factors on one project may be trivial on another project

23. Types of Project Selection Models • Nonnumeric models • Numeric models

24. Nonnumeric Models • Models that do not return a numeric value for a project that can be compared with other projects • These are really not “models” but rather justifications for projects • Just because they are not true models does not make them all “bad”

25. Types of Nonnumeric Models • Sacred Cow • A project, often suggested by top management, that has taken on a life of its own. It continues, not due to any justification, but “just because.” • Operating Necessity • A project that is required in order to protect lives or property or to keep the company in operation. • Competitive Necessity • A project that is required in order to maintain the company’s position in the marketplace.

26. Types of Nonnumeric Models Continued • Product Line Extension • Often, projects to expand a product line are evaluated on how well the new product meshes with the existing product line rather than on overall benefits. • Comparative Benefit • Projects are subjectively rank ordered based on their perceived benefit to the company.

27. Numeric Models • Models that return a numeric value for a project that can be easily compared with other projects • Two major categories: • Profit/profitability • Scoring

28. Profit/Profitability Models • Models that look at costs and revenues • Payback period • Discounted cash flow (NPV) • Internal rate of return (IRR) • Profitability index • NPV and IRR are the more common

29. Payback Period • The length of time until the original investment has been recouped by the project • A shorter payback period is better

30. Payback Period Example

31. Payback Period Drawbacks • Does not consider time value of money • More difficult to use when cash flows change over time • Less meaningful over longer periods of time (due to time value of money)

32. Discounted Cash Flow • The value of a stream of cash inflows and outflows in today’s dollars • Also know as discounted cash flow or just discounting • Widely used to evaluate projects • Includes the time value of money • Includes all inflows and outflows, not just the ones through payback point

33. Discounted Cash Flow Continued • Requires a percentage to use to reduce future cash flows • This is known as the discount rate • The discount rate may also be know as a hurdle rate or cutoff rate • There will usually be one overall discount rate for the company

34. NPV Formula

35. NPV Formula Terms A0 Initial cash investment Ft The cash flow in time period t (negative for outflows) k The discount rate T The number of years of life • A higher NPV is better • The higher the discount rate, the lower the NPV

36. NPV Example

37. Internal Rate of Return [IRR] • The discount rate (k) that causes the NPV to be equal to zero • The higher the IRR, the better • While it is technically possible for a series to have multiple IRR’s, this is not a practical issue • Finding the IRR requires a financial calculator or computer • In Excel “=IRR(Series,Guess)”

38. Profitability Index • a.k.a. Benefit cost ratio • NPV divided by initial cash investment • Ratios greater than 1.0 are good

39. Advantages of Profitability Models • Easy to use and understand • Based on accounting data and forecasts • Familiar and well understood • Give a go/no-go indication • Can be modified to include risk

40. Disadvantages of Profitability Models • Ignore non-monetary factors • Some ignore time value of money • Discounting models (NPV, IRR) are biased to the short-term • Payback models ignore cash flow after payback

41. Scoring Models • Unweighted factor model • Weighted factor model

42. Unweighted Factor Model • Each factor is weighted the same • Less important factors are weighted the same as important ones • Easy to compute • Just total or average the scores

43. Unweighted Factor Model Example Figure 2-2

44. Weighted Factor Model • Each factor is weighted relative to its importance • Weighting allows important factors to stand out • A good way to include non-numeric data in the analysis • Factors need to sum to one • All weights must be set up so higher values mean more desirable • Small differences in totals are not meaningful

45. Weighted Factor Model Example Figure B

46. Analysis Under Uncertainty—The Management of Risk • Everything to do with projects is risky • Some projects, like R&D, are more risky than others, like construction • Risks include… • The timing of the project and its associated cash flow • Risk regarding the outcome of the project • Risk about the side effects

47. Risk and Uncertainty • What the decision maker does • What nature does

48. Uncertainty • Pro forma financial statements • Risk analysis • Simulation (requires detailed probability information)

49. Comments on the Information Base for Selection • Accounting data • Measurements • Uncertain information

50. Accounting Data • Cost and revenue are linear • Cost-revenue data derived using standard cost standardized revenue assumptions • Costs may include overhead