Project risk management

the required transformations to become project uncertainty management

Introduction

Project threat management and project opportunity management are two sides of the same coin. Project risk management cannot ignore opportunity management without significantly weakening what is achievable.

The importance of some aspects of opportunity management in project risk management have been understood for some time, and interest in project opportunity management has been growing steadily. For example, the approach to project risk management adopted worldwide by BP International in the mid 1970s (Chapman, 1979) was built around the concept of risk efficiency (a minimum level of risk for a given level of expected profit or cost). This is a very basic objective for both opportunity and threat management. BP's approach also reflected a linked key objective for opportunity management: an aggressive approach to expected profit, so long as the risk/expected cost tradeoff did not expose the organization to potentially catastrophic losses at a higher level of risk than other aspects of its operations. In addition, BP's approach recognized the importance of managing good luck as well as bad luck, and the need for corporate culture changes, which would empower more explicit opportunity management. The IBM UK Forum 2 program in the late 1980s was built on these ideas to engineer a culture change involving less bureaucracy, faster responses, and more aggressive risk taking. The NatWest Bank internal project risk management process of the early 1990s transformed itself into a program benefit management process by the mid 1990s, which linked all project risk management to achieving the benefits of the program (portfolio of projects) as a whole as defined in terms of the business case used to approve the program. These developments are all reflected in “Project Risk Management: Processes, Techniques and Insights” (Chapman & Ward, 1997). And the opportunity management emphasis of that book has generated further interest in project opportunity management.

Ingemund Jordanger of Statoil recently adopted the term “uncertainty management,” instead of “risk management,” to emphasize the need for an even-handed approach to opportunities and threats. This was a bold and seminal step. However, if the term “project uncertainty management” is to achieve the full potential, which this term facilitates, it has to embrace more than a change in emphasis with respect to opportunities and threats.

This paper explores what else might be involved. It outlines some key research areas, which are emerging. It indicates some recent advances. But its focus is postulates or conjectures about what remains to be done. Its purpose is encouraging a dialogue with interested users and other interested researchers.

Ambiguity as the Core Issue

Why is it, that despite the fact that most risks are incurred in the pursuit of opportunities, the historical focus of project risk management has been threat management? An answer, the author suggests, is pervasive ambiguity in all our endeavors.

The basic postulate of this paper is that taking uncertainty management beyond uncertainty conceived as variability and addressing uncertainty conceived as ambiguity, recognizing scope for a wide range of different kinds of ambiguity, is the core issue.

The author was stimulated into adopting this position by reflecting on presentations to Statoil and Epci (European Institute of Advanced Project and Contract Management) by Per Willy Hetland (of both organizations). However, the particular frameworks for thinking about ambiguity which Hetland suggested have been supplemented by several other frameworks, which the author suggests many more may be relevant, and Hetland's distinctions between ambiguity, fuzziness, in determinacy and lack of information are not made. This implies ambiguity as defined here is itself ambiguous, and needful of careful exploration. At present the author believes that even the boundary between variability and ambiguity is not clear, and Hetland's other boundaries may not be clear either in effective operational terms.

This paper briefly explores four specific examples of ambiguity that need to be addressed, different in nature, progress to date, and scope for making an uncertainty management paradigm which resolves them more powerful than current risk management processes.

Uncertainty in Estimates of Risk Event Probability and Impact

A large proportion of those using project management processes employ probability impact matrices (PIMs). This generates unnecessary ambiguity.

For example, say an oil company project team wants to estimate the duration and the cost of the design of an offshore pipeline using the organization's own design department. Current common best practice would require a list of sources of uncertainty (a risk list or risk log), which might include entries like “change of route,” “demand for design effort from other projects,” “loss of staff,” and “moral problems.” “Project Risk Management” (Chapman & Ward, 1997) suggests each of these sources should be sized in crude quantitative terms on a first pass if qualification is useful, using a “simple scenario” approach, refining estimates of those issues that seem to matter as appropriate in later passes. However, many project risk management approaches in widespread use would rate these risks in terms of probability and impact on a “low, medium and high” scales on the first pass, perhaps with a view to quantification later if appropriate, although usually this transformation does not take place at the level of sources of risk. The Association for Project Management (APM) Project Risk Analysis and Management (PRAM) Guide (Simon, Hillson, & Newland, 1997) specifically accommodates quantitative or qualitative first passes because many members of the working party preferred qualitative approaches.

The author believes that he and Stephen Ward must accept part of the blame for the ongoing use of PIMs, because the “simple scenario” approach was not simple enough, and the arguments against PIMs not clear enough. However, we are both determined to overcome these shortcomings. Successful use of a still simpler approach in a bidding context (Chapman, Ward, & Bennell, in press) has led to a more general “minimalist approach” (Chapman & Ward, in press) which we hope will do the trick.

The arguments will not be repeated here. However, by way of a brief summary:

1. A clear distinction is made between sources of uncertainty that are useful to quantify and sources that are best treated as conditions, following Project Risk Management (Chapman & Ward, 1997) in this respect. “Changes of route” would probably fall into the latter category for the project manager, and it would, almost certainly, fall into the latter category for the head of the design department. The following steps apply only to those risks usefully quantified.

2. The “probability” of a threat occurring is associated with an approximate order of magnitude minimum and maximum plausible probability, assuming a uniform distribution (and a mid point expected value). This captures the users feel for a “low, medium or high” probability class in a flexible manner, captures information about uncertainty associated with the probability, and yields a conservative (pessimistic) expected value. For trained users it should be easier than designing appropriate standard classes for all risks and putting a tick in an appropriate box.

3. The “impact” of a threat which occurs is associated with an approximate order of magnitude minimum and maximum plausible value (duration and cost), also assuming a uniform duration. This captures the user's feel for a “low, medium or high” impact class in a flexible manner, captures information about the uncertainty associated with the impact, and yields a conservative (pessimistic) expected value. For trained users it should be easier than designing appropriate standard classes for all risks and putting a tick in an appropriate box.

4. The expected values and associated uncertainties for all quantified sources of uncertainty are shown graphically in a way that displays the contribution of each to the total, in expected value and range terms, clearly indicating what matters and what does not, as a basis for managing subsequent passes of the risk management process in terms of data acquisition to confirm important probability and impact assessment, refinement of response strategies, and key decision choices.

The concern of the “minimalist” first pass approach is not a defensible quantitative assessment. It is a clear understanding of what seems to matter based on the views of those able to throw some light on the issues. It is an attempt to resolve the ambiguity associated with the size of uncertainty about the impact of risk events and the size of uncertainty about the probability of risk events occurring, the latter often dominating the former. It may lead to the conclusion “there is no significant uncertainty, and no need for further effort,” one of the reasons it must have a conservative bias (another is the need to manage expectations, with subsequent refinements of any uncertainty estimation process indicating less uncertainty, more uncertainty providing an explicit indication that the earlier process failed).

Uncertainty About the Conditional Nature of Estimates

A large proportion of those using probabilistic project risk management processes often fail to get to grips with the conditioned nature of probabilities and associated measures used for decision-making and control. This includes the author. It generates unnecessary ambiguity.

For example, continuing the example of the last section, say the oil company project manager decides to contract out pipeline design. Is the expected cost of the design activity for the external design company the same as the expected cost for the board of the oil company? The simple answer is no. A more helpful answer being developed in more detail elsewhere (Chapman & Ward, working paper) is there are three potential adjustments that need to be considered, one with two components:

1. Known unknowns—(a) triple Es (explicit extreme events) and (b) SAPs (scope adjustment provisions)

2. Unknown unknowns

3. Bias

Triple Es are “force majeur” events, like a change in legislation which would influence pipeline design criteria in a fundamental way.

SAPs are conditions or assumptions that may not hold, which are explicit, like the assumed operating pressure and flow value for the pipeline given the assumed oil recovery rate.

Triple Es and SAPs are “known unknowns” in the sense they are identifiable sources of uncertainty, which the design company will not size because it will design its contract to avoid bearing any associated risk and the board will not normally size for a number of understandable reasons.

Unknown unknowns are the unidentified triple Es or SAPs that should be used for the decisions or controls in question. We know that the realization of some “unknown unknowns” is usually inevitable. They do not include issues like “the world may end tomorrow.” It is sensible for most practical decision-making to assume we will still be here tomorrow. However, what is and is not appropriate may be ambiguous, perhaps to the extent of being the focus of post project litigation.

Bias may be conscious or unconscious, pessimistic or optimistic, and clues if not data may be available or not.

A “known unknown, unknown unknowns and bias (KUUUB) factor” (pronounced “cube factor”) needs to be estimated for the board to adjust the design company's expected cost. A similar KUUUB (or KU?B) factor or (F?) would need estimation if the oil company's own design department undertook the work, but the issues would be more complicated. A three-dimensional “cube diagram” can be used to illustrate the three dimensions of the “cube factor.”

What is involved here is an attempt to clarify the ambiguity associated with expected values and associated measures of variability because of the conditioned nature of all estimates that we use in practice.

Uncertainty About “Commitments”and “Targets”

“Project Risk Management” (Chapman & Ward, 1997) goes to some lengths to explain the important differences between “targets” (as aspirational tools for control) “expected values” (as best estimators of what should happen on average, as a basis for decisions) and “commitments” as values that can be “signed up to” with an appropriate chance of success, what is “appropriate” being determined by the “asymmetry of the penalty function”). However, when it is appropriate to make commitments is not explored, an important source of ambiguity.

For example, continuing the earlier example, if the “pipeline design” activity is followed by “order pipe,” “deliver the pipe,” and so on, and if the internal design department is involved, why do we need a commitment date? The simple answer is we do not. We need a target duration and an expected duration that becomes firm as early as possible in order to manage the good luck as well as the bad luck associated with variations in the duration of the design activity.

The rationale and the tools to operate such an approach are being explored further elsewhere (Chapman & Ward, working paper). The postulate being explored is briefly outlined as follows. Most internal design departments have a “cost per design hour” rate based on an historic accounting cost. A “design hours” estimate times this rate yields a “design cost” estimate. Design actual cost is based on realized design hours. The internal contract is “cost plus.” The duration agreed to by the design department is a “commitment” date with a low chance of excedence, bearing in mind all the “risks” noted earlier.

What is needed is a fixed “nominal cost” based on the appropriate expected number of design hours, with a premium payment scale for completion earlier than an appropriate “trigger duration,” a penalty deduction scale for later completion. This needs to be tied to a “quality” trigger and a premium for correct prediction of in time for following activities. This is a form of internal “target” contract, although the use of “trigger” rather than “target” date is important. The “trigger” duration should be something like an 80 percentile value, comparable to a “commitment duration.” The target should be very ambitious; a plausible date if all goes as well as possible. Goldratt's (1997) “Critical Chain” explores the same issue from a different perspective with some related conclusions.

What is involved from the perspective of this paper is clarifying the ambiguity associated with what we should mean by “commitments,” which in operational terms requires further classification of ambiguity about what we should mean by “targets.”

Uncertainty About Objectives at Operational Levels

“Project Risk Management” (Chapman & Ward, 1997) goes to considerable lengths to explain the importance of links between a project's:

who (parties or player)

why (motives, aims and objectives of the who)

what (design of the deliverable)

which way (activity or task structure to achieve the deliverable)

wherewithal (resource requirements)

when (time frame).

The associated “six Ws” is a core framework for managing the tradeoffs between time, cost and performance, different tradeoffs for different partners, and tradeoffs that change over time. However, operational measurement and management of tradeoffs and underlying feasible relationships are not explored. Ambiguity in this area is immense.

For example, carrying on with the earlier example: how much is it worth to the project to be able to complete the design faster? If a “trigger” (form of target) contract is used, will fewer hours be required because the incentive structure will reduce multitasking? If multitasking is reduced, will the internal design department become so much more efficient that the rumor of selling off the design function (outsourcing all design), which might underpin the loss of staff and low moral “risks” be eliminated? If these downside risks are eliminated, can the “upside risks” or opportunities associated with easy hiring and low turnover of good staff, high morale, and high efficiency be managed explicitly via a “virtuous circle,” with implications way beyond the design of a particular pipeline. Generalizing in other ways, would a flexible (rerl barge layed) pipeline be better than a rigid pipeline, and is a pipeline the best way to bring the oil to market?

What is involved is ambiguity about objectives at various operational levels. The key postulate of this paper is that resolving the three kinds of ambiguity discussed earlier will help with this fourth ambiguity, and resolving this fourth kind of ambiguity should be the ultimate focus for project uncertainty analysis.

Conclusion

Given the general reluctance associated with tackling ambiguity associated with event probability and impact, it is not surprising that objective measures like “how much is it worth to a project to shorten the duration of the design activity” may not be addressed explicitly in terms of a sound approach to uncertainty management. The author's hope is that a “minimalist” approach may help here. For example, to get started, all we need is an estimate of the order of magnitude of the minimum and maximum plausible charge in the project cost (including opportunity cost) if design duration was one week shorter or longer. More generally, if we are successful with the relatively simple ambiguity issues, this should give us some of the tools to tackle the more difficult ambiguity issues.

This paper was not designed to persuade readers that a particular approach to “project uncertainty management” should be adopted. Its intent is to stimulate discussion amongst those interested in how best to approach clarification of the required transformations of “project risk management,” with a view to a collaborative attempt to achieve these transformations.

Chapman, Chris B. (1979). Large engineering project risk analysis. IEEE Transactions on Engineering Management EM26, 78–86.

Chapman, Chris B., & Ward, Stephen C. (1997). Project risk management: Processes, techniques and insights. Chichester, UK: John Wiley & Sons.

Chapman, Chris B., & Ward, Stephen C. (In press). Estimation and evaluation of uncertainty: A minimalist first pass approach. International Journal of Project Management.

Chapman, Chris B., & Ward, Stephen C. (Working paper). Understanding Uncertainty.

Chapman, Chris B., Ward, Stephen C., & Bennell, Julia A. (In press). Incorporating uncertainty in competitive bidding. International Journal of Project Management.

Simon, Peter, Hillson, David, & Newland, Ken. (1997). Project risk analysis and management (PRAM) guide. Norwich, UK: The Association for Project Managers.

This material has been reproduced with the permission of the copyright owner. Unauthorized reproduction of this material is strictly prohibited. For permission to reproduce this material, please contact PMI or any listed author.

Proceedings of PMI Research Conference 2000

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