In spite of the care and effort expended to create an accurate and fair budget, it is still only an estimate made under conditions of uncertainty. Because projects are unique, risk pervades all elements of the project, and particularly the project’s goals of perfor- mance, schedule, and budget. We will discuss these issues of uncertainty and risk here, and offer some suggestions for dealing with them.
Budget Uncertainty
Perceptually, the PM sees the uncertainty of the budget like the shaded portion of Figure 4-4, where the actual project costs may be either higher or lower than the estimates the PM has derived. As we will describe later, however, it seems that more things can go wrong in a project and drive up the cost than can go right to keep down the cost. As the project unfolds, the cost uncertainty decreases as the project moves toward completion.
Figures 4-5 (a), (b), and (c) illustrate this. An estimate at the beginning of the project as in Figure 4-4 is shown as the t0estimate in Figure 4-5(a). As work on the project pro- gresses, the uncertainty decreases as the project moves toward completion. At time t1the cost to date is known and another estimate is made of the cost to complete the project, Figure 4-5(b). This is repeated at t2, Figure 4-5(c). Each estimate, of course, begins at the actual cost to date and estimates only the remaining cost to completion. The further the
There are numerous ways to improve the process of cost estimation ranging from simple but useful forms and procedures to special techniques such as learning curves and tracking signals. Most estimates are in error, however, be- cause of simpler reasons such as not using available tools, common sense, or failing to allow for problems and contingencies, such as having to replace workers midstream. In addition, there are behavioral and organizational rea- sons, such as informal incentive systems that reward inaccurate estimates.
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project progresses, the less the uncertainty in the final project cost. It is common in pro- ject management to make new forecasts about project completion time and cost at fixed points in the project life cycle, or at special milestones.
The reasons for cost uncertainty in the project are many: prices may escalate, different resources may be required, the project may take a different amount of time than we ex- pected thereby impacting overhead and indirect costs, and on and on. Earlier, we discussed ways to improve cost estimates, to anticipate such uncertainty, but change is a fact of life, including life on the project, and change invariably alters our previous budget estimates.
Three causes for change There are three basic causes for change in projects and their budgets and/or schedules. Some changes are due to errors the cost estimator made about how to achieve the tasks identified in the project plan. Such changes are due to technological uncertainty: a building’s foundation must be reinforced due to a fault in the ground that wasn’t identified beforehand; a new innovation allows a project task to be completed easier than was anticipated, and so on.
Other changes result because the project team or client learns more about the na- ture of the performance goal of the project or the setting in which it is to be used. This derives from an increase in the team’s or client’s knowledge or sophistication about the project deliverables. The medical team plans to use a device in the field as well as in the hospital. The chemists find another application of the granulated bed process if it is al- tered to include additional minerals.
The third source of change is the mandate: A new law is passed, a trade association sets a new standard, a governmental regulatory agency adopts a new policy. These changes alter the previous “rules of conduct” under which the project had been operat- ing, usually to the detriment of the budget.
Handling Changes There are different ways to handle such changes. The least pre- ferred way is simply to accept a negative change and take a loss on the project. The best approach is to prepare for change ahead of time by including provisions in the original contract for such changes. The easiest change to handle is when the change is the result of an increased specification by the client, yet even these kinds of changes are often mishandled by the project organization. The best practice is to include in the contract a formal change control procedure that allows for renegotiation of price and schedule for client-ordered changes in performance.
Figure 4-4 Estimate of project cost: estimate made at project start.
Figure 4-5 (a), (b), and (c) Estimates of project cost: esti- mates made at time t0, t1, and t2.
4.4 BUDGET UNCERTAINTY AND RISK MANAGEMENT • 127 More difficult changes are those resulting from misunderstood assumptions, techno- logical uncertainty, and mandates. Assumptions and some technological uncertainties are most easily handled by carefully listing all the assumptions, including those regard- ing technology, in the contract and stating that if these assumptions fail to hold, the project’s cost and schedule may have to be adjusted.
Mandates are the most difficult to accommodate because they can affect anything about the project and usually come without warning. The shorter the project duration, however, the less likely an unexpected mandate will impact the project. Thus, when contracting for a project of extended duration, it is best to divide it into shorter seg- ments and contract for only one segment at a time. Of course, this also gives clients the opportunity to reconsider whether they want to complete the full project, as well as giv- ing the competition an opportunity to steal the remainder of the project from you. Nev- ertheless, if a client wants to cancel a contract and is locked into a long-term agreement, the project will not have a happy ending anyway. At least with shorter seg- ments the client may be willing to finish a segment before dropping the project. In any event, if the client is pleased with your performance on one segment of the contract, it is unlikely that a competitor will have the experience and cost efficiencies that you have gained and will be able to steal the next segment. At the least, the client would be obligated to give you an opportunity to match their bid.
As changes impact the project’s costs, the budget for the remainder of the project will certainly have to be revised. There are three ways to revise a budget during the course of a project, each depending on the nature of the changes that have been experi- enced. If the changes are confined to early elements of the project and are not seen to impact the rest of the project, then the new budget can be estimated as the old budget plus the changes from the early elements.
More frequently, something systemic has changed that will impact the costs of the rest of the project tasks as well, such as a higher rate of inflation. In this case, the new budget estimate will be the accumulated costs to date plus the previous estimates of the rest of the budget multiplied by some correction factor for the systemic change. Recall that the BLS is an excellent source for such historical data that will aid the PM in esti- mating an appropriate correction factor.
Last, there may be some individual changes now perceived to impact specific ele- ments of the remaining project tasks. The new budget estimate will then be the actual costs to date plus the expected costs for the remaining project tasks. Generally, both systematic and individual changes in the project will be revised in all three ways at once.
Risk Management
The field of risk management has grown considerably over the last decade. The Project Management Institute’s PMBOK (2004) devotes Chapter 11 to this topic. In general, risk management includes three areas: (1) risk identification, (2) risk analysis, and (3) response to risk. The process of accomplishing these three tasks is broken down into six subprocesses:
1. Risk Management Planning developing a plan for risk management activities.
2. Risk Identification finding those risks that might affect the project.
3. Qualitative Risk Analysis evaluating the seriousness of the risk and the likelihood it will affect the project.
4. Quantitative Risk Analysis developing measures for the probability of the risk and its impact on the project.
5. Risk Response Planning finding ways of reducing negative impacts on the project as well as enhancing positive impacts.
6. Risk Monitoring and Control maintaining records of and evaluating the sub- processes above in order to improve risk management.
Risk Management Planning This planning process is like any other planning process. First, a method for carrying out steps 2–5 for each project must be designed.
Care must be exercised to ensure that the necessary resources can be applied in a timely and well-organized manner. The planning process, just as the task of managing risk, is a continuous process. The factors that cause uncertainty appear, disappear, and change strength as time passes and the environment of a project changes. Note that planning how to deal with uncertainty is an organizational problem, not specifically a project problem. The result is that many firms create a formal, risk management group, whose job it is to aid the project management team in doing steps 2–5. The overall risk man- agement group develops plans and maintains the database resulting from step 6.
Some of the inputs and outputs of steps 2–5 are unique to the project, some are common for all projects. The overall group helps individual project risk teams with the necessary analytic techniques, information gathering, the development of options for response, and monitoring and evaluating the results.
Risk Identification and Qualitative Risk Analysis We list these steps together because, in practice they are often carried out together. As a risk is identified, an at- tempt to measure its timing, likelihood, and impact is often made concurrently.
Risk identification consists of a thorough study of all sources of risk in the project.
Common sources of risk include the organization of the project itself; senior manage- ment of the project organization; the client; the skills and character of the project team members, including the PM; acts of nature; and all of the three types of changes de- scribed earlier under budget uncertainty.
Scenario Analysis is a well-known method for identifying serious risks. It involves envisioning likely scenariosthat may have major repercussions on the organization and then identifying the possible resulting outcomes of events such as a hurricane in New Orleans, an extended labor strike, or the freezing of a river. These types of risk can often be identified and evaluated by project stakeholders or outside parties with previous experience in similar projects. Beyond this, a close analysis of the project plan and WBS and the linear responsi- bility chart (Chapter 3), as well as the PERT chart (Chapter 5) will often identify highly probable risks, extremely serious risks, or highly vulnerable areas, should anything go wrong.
After the major risks are identified, the following data should be obtained on each to facilitate further analysis: the probability of each risk event occurring, the range or distri- bution of possible outcomes if it does occur, the probabilities of each outcome, and the ex- pected timing of each outcome. In most cases, good estimates will not be available, but getting as much data and as accurate estimates as possible will be crucial for the follow-on risk analysis. Above all, remember that a “best guess” is always better than no information.
Failure Mode and Effect Analysis (FMEA) FMEA is a structured approach similar to the scoring methods discussed in Chapter 1 to help identify, prioritize, and better manage risk. Developed by the space program in the 1960s, FMEA can be applied to projects using the following six steps.
1. List ways the project might potentially fail.
2. List the consequences of each failure and evaluate its severity. Often severity, S, is ranked using a ten-point scale, where “1” represents failures with no effect and “10”
represents very severe and hazardous failures that occur with no warning.
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3. List the causes of each failure and estimate their likelihood of occurring. The likeli- hood of a failure occurring, L, is also customarily ranked on a ten-point scale with a
“1” indicating the failure is rather remote and not likely to occur and “10” indicat- ing the failure is almost certain to occur.
4. Estimate the ability to detect each failure identified. Again, the detectability of fail- ures, D, is customarily ranked using a ten-point scale where a “1” is used when mon- itoring and control systems are almost certain to detect the failure and “10” where it is virtually certain the failure will not be detected.
5. Calculate the Risk Priority Number(RPN) by multiplying S, L, and Dtogether.
6. Sort the list of potential failures by their RPNs and consider ways for reducing the risk associated with failures with high RPNs.
Table 4-5 illustrates the results of a FMEA conducted to assess the risk of a new drug development project at a pharmaceutical company. As shown in the table, five potential failures for the project were identified: (1) The new drug is not effective at treating the ailment in question, (2) the drug is not safe, (3) the drug interacts with other drugs, (4) another company beats it to the market with a similar drug, and (5) the company is not able to produce the drug in mass quantities. According to the results, the most sig- nificant risk is the risk of developing a new drug that is not effective. While it is un- likely that much can be done to reduce the severity of this outcome, steps can be taken to reduce the likelihood of this outcome as well as increase its detectability. For exam- ple, advanced computer technologies can be utilized to generate chemicals with more predicable effects. Likewise, perhaps earlier human clinical and animal trials can be used to help detect the effectiveness of new drugs sooner. In this case, if both Land D could each be reduced by one, the overall RPN would be reduced from 240 to 160.
Quantitative Risk Analysis Hertz and Thomas (1983) and Nobel prize winner Her- bert Simon (1997) have written two classic books on this topic. As we noted in Chapter 1, the essence of risk analysis is to state the various outcomes of a decision as probability distributions and to use these distributions to evaluate the desirability of certain manager- ial decisions. The objective is to illustrate to the manager the distribution or risk profileof the outcomes (e.g., profits, completion dates, return on investment) of investing in some project. These risk profiles are one factor to be considered when making the decision, along with many others such as intangibles, strategic concerns, behavioral issues, fit with the organization, and so on. This is illustrated later in this chapter in Figure 4-8.
A case in point is Sydney, Australia’s M5 East Tunnel (PMI, March 2005). It was constructed under strict budgetary and schedule requirements, but given the massive traf- fic delays now hampering commuters, the requirements may have been seriously underes- timated. Due to an inexpensive computer system with a high failure rate, the tunnel’s security cameras frequently fail, requiring the operators to close the tunnel due to inability Table 4-5 FMEA for New Product Development Project
at Pharmaceutical Company
Failure S L D RPN
Not effective 8 6 5 240
Not safe 8 4 5 160
Drug interacts with other drugs 6 3 8 144
Beat to market 7 3 2 42
Can’t produce 6 4 4 96
to react to an accident, fire, or excessive pollution inside the tunnel. The tunnel was built to handle 70,000 vehicles a day, but it carries 100,000 so any glitch can cause immediate traffic snarls. A risk analysis, including the risk of overuse, probably would have antici- pated these problems and mandated a more reliable set of computers once the costs of fail- ure had been included.
Estimates Before discussing the risk analysis techniques, we need to discuss some issues concerning the input data coming out of the qualitative analysis of Step 3. We assume here that estimating the range and timing of possible outcomes of a risky event is not a problem but that the probabilities of each may be harder to establish. Given no ac- tual data on the probabilities, the best guesses of people familiar with the problem is a rea- sonable substitute. An example of such guesses (a.k.a. estimates) can be seen in Table 4-6.
As we saw in Chapter 1, knowledgeable individuals are asked for three estimates of the cost of each activity, a normal estimate plus optimistic and pessimistic estimates of the cost for each. From these an expected value for the cost of an activity can be found, but we will delay discussing this calculation until Chapter 5 where we show such calculations for either cost or durations.
If approximations cannot be made, there are other approaches that can be used. One approach is to assume that all outcomes are equally probable, though there is no more justi- fication for this assumption than assuming any other arbitrarily chosen probability values.
Remember that there is a case when using the expected-value approach (see below) and es- timating the probability of an event occurring is not very helpful. The probability of a dis- aster may be very low, but such risks must be carefully managed none-the-less (cf: the end of Section 1-6).
Game Theory Another approach is to assume that competitors and the environment are enemies, trying to do you in. This is the game theoryapproach. The decision maker takes a pessimistic mind-set and selects a course of action that minimizes the maximum harm (the minimaxsolution) any outcome can render regardless of the probabilities. With this approach, each decision possibility is evaluated for the worst possible outcome, all these worst outcomes are compared, and the decision with the “best” worst outcome is selected.
For example, assume an investor would like to choose one of two mutual funds in which to invest. If interest rates rise, the return on mutual fund A will be 5 percent, while the return on mutual fund B will be 3 percent. On the other hand, if interest rates decrease, the return on A will be 7 percent while the return on B will be 12 percent. With the pessimistic ap- proach, the worst outcome if mutual fund A is selected is a 5 percent return. Similarly, the worst return if B is selected is 3 percent. Since A has the better worst return (5 percent is better than 3 percent), the investor would choose to invest in mutual fund A using the pes- simistic approach. Of course, by investing in A the investor is eliminating the chance of achieving a 12 percent return. There are other methods besides those we have mentioned, but these are representative of approaches when probability information is unavailable.
Expected Value When probability information is available or can be estimated, many risk analysis techniques use the concept of expected valueof an outcome—that is, the value of an outcome multiplied by the probability of that outcome occurring. For example, in a coin toss using a quarter, there are two possible outcomes and the ex- pected value of the game is the sum of the expected values of all outcomes. It is easily calculated. Assume that if the coin comes up “head” you win a quarter, but if it is “tails”
you lose a quarter. We also assume that the coin being flipped is a “fair” coin and has a .5 probability of coming up either heads or tails. The expected value of this game is
E(coin toss) .5($.25) .5($.25) 0