How does project planning minimize risks

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“Those who cannot remember the past are condemned to repeat it.”

—GEORGE SANTAYANA

Far too many technical projects retrace the shortcomings and errors of earlier work. Projects that successfully avoid such pitfalls are of- ten viewed as “lucky,” but there is usually more to it than that.

The Doomed Pr o ject

All projects involve risk. There is always at least some level of un- certainty in a project’s outcome, regardless of what the Microsoft Project Gantt chart on the wall seems to imply. High-tech projects are particularly risky, for a number of reasons. First, technical projects are highly varied. These projects have unique aspects and objectives that significantly dif- fer from previous work, and the environment for technical projects evolves quickly. There can be much more difference from one project to the next than in other types of projects. In addition, technical projects are fre- quently “lean,” challenged to work with inadequate funding, staff, and equipment. To make matters worse, there is a pervasive expectation that however fast the last project may have been, the next one should be even quicker. The number and severity of risks on these technical projects continues to grow. To avoid a project doomed to failure, you must con- sistently use the best practices available.

C h a p t e r

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Why Project Risk Management?

Good project practices come from experience. Experience, un- fortunately, generally comes from unsuccessful practices and poor proj- ect management. We tend to learn what not to do, all too often, by doing it and then suffering the consequences. Experience can be an invaluable resource, even when it is not your own. The foundation of this book is the experiences of others—a large collection of mostly plausible ideas that did not work out as hoped.

Projects that succeed generally do so because their leaders do two things well. First, leaders recognize that much of the work on any proj- ect, even a high-tech project, is not new. For this work, the notes, records, and lessons learned on earlier projects can be a road map for identifying, and in many cases avoiding, many potential problems. Second, they plan project work thoroughly, especially the portions that require innovation, to understand the challenges ahead and to anticipate many of the risks.

Effective project risk management relies on both of these ideas. By looking backward, past failures may be avoided, and by looking forward through project planning, many future problems can be mini- mized or eliminated.

Risk

In projects, a risk can be almost any uncertain event associated with the work. There are many ways to characterize risk. One of the sim- plest, from the insurance industry, is:

“Loss” multiplied by “Likelihood”

Risk is the product of these two factors: the expected conse- quences of the event and the probability that the event might occur. All risks have these two related, but distinctly different, components. Em- ploying this concept, risk may be characterized in aggregate for a large population of events (“macro-risk”), or it may be considered on an event- by-event basis (“micro-risk”).

Both characterizations are useful for risk management, but which of these is most applicable differs depending on the situation. In most fields, risk is primarily managed in the aggregate, in the “macro” sense. As examples, insurance companies sell a large number of policies, commer- cial banks make many loans, gambling casinos and lotteries attract crowds of players, and managers of mutual funds hold large portfolios of investments. The literature of risk management for these fields (which is extensive) tends to focus on large-scale risk management, with secondary treatment for managing single-event risks.

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To take a simple example, consider throwing two fair, six-sided dice. In advance, the outcome of the event is unknown, but through analy- sis, experimenting, or guessing, you can develop some expectations. The only possible outcomes for the sum of the faces of the two dice are the in- tegers between two and twelve. One way to establish expectations is to figure out the number of possible ways there are to reach each of these totals. (For example, the total 4 can occur three ways from two dice: 1 + 3, 2 + 2, and 3 + 1.) Arranging this analysis in a histogram results in Figure 1-1. Because each of the 36 possible combinations is equally likely, this histogram can be used to predict the relative probability for each possi- ble total. Using this model, you can predict the average sum over many tosses to be seven.

If you throw many dice, the empirical data collected (which is an- other method for establishing the probabilities) will generally resemble the theoretical histogram, but because the events are random it is ex- traordinarily unlikely that your experiments rolling dice will ever pre- cisely match the theory. What will emerge, though, is that the average sum generated in large populations (one hundred or more throws) will be close to the calculated average of seven, and the shape of the histogram will also resemble the predicted theoretical distribution. Risk analysis in the macro sense takes notice of the population mean of seven, and casino games of chance played with dice are designed by “the house” to exploit this fact. On the other hand, risk in the micro sense, noting the range of possible outcomes, dominates the analysis for the casino visitors, who may play such games only once; the risk associated with a single event— their next throw of the dice—is what matters to them.

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Figure 1-1. Histogram of sums from two dice.

For projects, risk management in the large sense is useful to the organization, where many projects are undertaken. But from the per- spective of the leader of a single project, there is only the one project. Risk management for the enterprise, or for a portfolio of projects, is mostly about risk in the aggregate (a topic explored in Chapter 13). Pro- ject risk management focuses primarily on risk in the small sense, and this is the dominant topic of this book.

M a c ro - R i s k M a n a g e m e n t

In the literature of the insurance and finance industries, risk is de- scribed and managed using statistical tools: data collection, sampling, and data analysis. In these fields, a large population of individual examples is collected and aggregated, and statistics for the “loss and likelihood” can be calculated. Even though the individual cases in the population may vary widely, the average “loss times likelihood” tends to be fairly predictable and stable over time. When large numbers of data points from the popula- tion at various levels of loss have been collected, the population can be characterized using distributions and histograms, similar to the plot in Fig- ure 1-2. In this case, each “loss” result that falls into a defined range is counted, and the number of observations in each range is plotted against the ranges to show a histogram of the overall results.

Various statistics and methods are used to study such popula- tions, but the population mean is the main measure for risk in such a pop- ulation. The mean represents the typical loss—the total of all the losses divided by the number of data points. The uncertainty, or the amount of

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Losses Mean

Figure 1-2. Histogram of population data.

spread for the data on each side of the mean, also matters, but the mean sufficiently characterizes the population for most decisions.

In fields such as these, risk is mostly managed in the macro sense, using a large population to forecast the mean. This information may be used to set interest rates for loans, premiums for insurance policies, and expectations for stock portfolios. Because there are many loans, invest- ments, and insurance policies, the overall expectations depend on the av- erage result. It does not matter so much how large or small the extremes are; as long as the average results remain consistent with the business ob- jectives, risk is managed by allowing the high and low values to balance each other, providing a stable and predictable overall result.

Project risk management in this macro sense is common at the project portfolio and enterprise levels. If all the projects undertaken are considered together, performance primarily depends on the results of the “average” project. Some projects will fail and others may achieve spectacular results, but the aggregate performance is what matters to the business bottom line.

M i c ro - R i s k M a n a g e m e n t

Passive measurement, even in the fields that manage risk using large populations, is never the whole job. Studying averages is necessary, but it is never sufficient. Managing risk also involves taking action to in- fluence the outcomes.

In the world of gambling, which is filled with students of risk on both sides of the table, knowing the odds in each game is a good starting point. Both parties also know that if they can shift the odds, they will be more successful. Casinos shift the game in roulette by adding zeros to the wheel, but not including them in the calculation of the payoffs. In casino games using cards such as blackjack, casino owners employ the dealers, knowing that the dealer has a statistical advantage. In blackjack the play- ers may also shift the odds, by paying attention and counting the cards, but establishments minimize this advantage through frequent shuffling of the decks and barring known card counters from play. There are even more effective methods for shifting the odds in games of chance, but most are not legal; tactics like stacking decks of cards and loading dice are frowned upon. Fortunately, in project risk management, shifting the odds is not only completely fair, it is an excellent idea.

Managing risk in this small sense considers each case separately— every investment in a portfolio, each individual bank loan, each insur- ance policy, and in the case of projects, every exposure faced by the current project. In all of these cases, standards and criteria are used to minimize the possibility of large individual variances above the mean,

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and actions are taken to move the expected result. Screening criteria are applied at the bank to avoid making loans to borrowers who appear to be poor credit risks. (Disregarding these standards by offering “subprime” mortgages has recently led to the well-publicized consequences of devi- ating from this policy.) Insurers either raise the price of coverage or they refuse to sell insurance to people who seem statistically more likely to generate claims. Insurance firms also use tactics aimed at reducing the frequency or severity of the events, such as auto safety campaigns. Man- agers of mutual funds work to influence the boards of directors of com- panies whose stocks are held by the fund. All these tactics work to shift the odds—actively managing risk in the small sense.

For projects, risk management is almost entirely similar to these examples, considering each project individually. Thorough screening of projects at the overall business level attempts to select only the best op- portunities. It would be excellent risk management to pick out and termi- nate (or avoid altogether) the projects that will ultimately fail—if only it were that easy. As David Packard noted, “Half the projects at Hewlett- Packard are a waste of time. If I knew which half, I would cancel them.”

Project risk management—risk management in the small sense— works to improve the chances for each individual project. The leader of a project has no large population, only the single project; there will be only one outcome. In most other fields, risk management is primarily concerned with the mean values of large numbers of independent events. For project risk management, however, what generally matters most is predictability—managing the variation expected in the result for this project.

For a given project, you can never know the precise outcome in advance, but through review of data from earlier work and project plan- ning, you can predict the range and frequency of potential outcomes that you can expect. Through analysis and planning, you can better un- derstand the odds and take action to improve them. The goals of risk management for a single project are to establish a credible plan con- sistent with business objectives and then to minimize the range of pos- sible outcomes.

One type of “loss” for a project may be measured in time. The dis- tributions in Figure 1-3 compare timing expectations graphically for two similar projects. These plots are different from what was shown in Figure 1-2. In that case, the plot was based on empirical measurements of a large number of actual, historical cases. The plots in Figure 1-3 are projections of what might happen for these two projects, based on assumptions and data for each. These histograms are speculative and require you to pre- tend that you will execute the project many times, with varying results. Developing this sort of risk characterization for projects is explored in

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Chapter 9, where quantifying and analyzing project risk is discussed. For the present, assume that the two projects have expectations as displayed in the two distributions.

For these two projects, the average (or mean) duration is the same, but the range of expected durations for Project A is much larger. Project B has a much narrower spread (the statistical variance, or stan- dard deviation), and so it will be more likely to complete close to the ex- pected duration. The larger range of possible durations for Project A represents higher risk, even though it also includes a small possibility of an outcome even shorter than expected for Project B. Project risk in- creases with the level of uncertainty, both negative and positive.

Project risk management uses the two fundamental parameters of risk—likelihood and loss—just as any other area of risk management does. Likelihood is generally characterized as “probability” and may be esti- mated in several ways for project events (though often by guessing, so it can be quite imprecise). Loss is generally referred to for projects as “im- pact,” and it is based on the consequences to the project if the risk does oc- cur. Impact is usually measured in time (as in the examples in Figure 1-3) or cost, particularly for quantitative risk assessment. Other risk impacts

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Expected Duration of Project A

Expected Duration of Project B

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Figure 1-3. Possible outcomes for two projects.

include increased effort, issues with stated deliverable requirements, and a wide range of other more qualitative consequences that are not easily measured, such as team productivity and conflict and impact on other proj- ects and other operations. Applying these concepts to project risk is cov- ered in Chapter 7.

Managing project risk depends upon the project team understand- ing the sources of variation in projects, and then working to minimize threats and to maximize opportunities wherever it is feasible. Because no project is likely to be repeated enough times to develop distributions like those in Figure 1-3 using measured, empirical data, project risk analyses de- pend on projections and range estimates.

Benef i t s and Uses o f R isk Data

Can you manage risk? This fundamental question is unfortunately not trivial, because uncertainty is always present, regardless of what we choose to do. For projects, we can at least answer “Yes, sometimes,” de- pending on tactics such as those outlined earlier and throughout the sec- ond half of this book.

Because our ability to manage risk is imperfect, it’s fair to ask a second question: Should you manage risk? As with any business decision, the answer has to do with cost and benefits. Developing a project plan with thorough risk analysis can involve significant effort, which may seem unnecessary overhead to many project stakeholders and even to some project leaders. There are many benefits from project risk management, though, and particularly for complex projects, they far outweigh the costs. Some of these benefits of project risk management follow, and each is amplified later in this book.

P ro j e c t J u s t i f i c at i o n

Project risk management is primarily undertaken to improve the chances of projects achieving their objectives. Although there are never any guarantees, broader awareness of common failure modes and ideas that make projects more robust can significantly improve the odds for success. The primary benefit of project risk management is either to develop a credible foundation for each project by showing that it is possible, or to demonstrate that the project is not feasible so it can be avoided, aborted, or transformed. Risk analysis can also reveal op- portunities for improving projects that can result in increased project value.