Other Risk Management Approaches and Concluding Observations

While this chapter focuses on decision technologies in dealing with risk, we recognize that it is just one tool for dealing with it. Indeed, in the development and delivery of project management training for research and development personnel at Bandag, Inc., one of the authors of this chapter helped to develop a Project Management Planning Guide for the management of schedule risk in the development of new products and services. The guide is a very simple document (two pages) with the purpose of instilling a common project planning discipline in the company. The guide is a dynamic document in that action items are triggered throughout the life of the project. Also, several steps of the guide are intended to reduce project schedule risk, e.g., involving the project sponsor early on in the development process (to avoid surprises later), ensuring that the project team is involved throughout the planning process, scheduling regular review meetings, and so forth. Bandag found this disciplined approach to project planning useful and has embedded the guide in its corporate product development process.

We believe that much can be learned by studying the risk analysis techniques currently used in finance. Motivated in large part by financial risk management we suggest a variety of strategies including: diversifying risks, transferring risks to contractors, purchasing insurance, building in slack (buffers), obtaining more information about uncertainties, controlling the outcome (e.g., through project design), and building in redundancy.

Consider, in particular, the use of diversification in portfolio composition. The notion is to spread the risk among several investments. To carry this concept over to project management, the natural thought is to diversify by selecting more than one "doer" (contractor) when there is uncertainty in task completion times. However, we now show by simple example that diversification is not always the best thing to do.

Let A, B, and C denote three tasks, where the task completion time for each task is either two or four days, with a probability of 0.5 for each event. We assume that if the tasks are done by a single contractor, the completion time distributions are perfectly correlated. However, if done by different contractors the distributions on task times are independent.

If the project involves doing the tasks in series, i.e., A then B then C, it is optimal to diversify (assign each task to a different contractor) since in that case, there is a 12.5 percent chance—(0.5)³ each—that the project is completed in either six or twelve days and a 37.5 percent chance each that the completion time is either eight or ten days. By assigning each task to a single contractor, we note that the completion time would be (because of perfect correlation) either six or twelve days with a probability of 0.5 for each event. In this example, note that the expected completion time is the same (nine days), but diversification gives rise to a (desirable) lower variance of completion time. The analogy to finance portfolio theory is that in this project with serial tasks, total project time is the sum of task times.

Alternatively, suppose the tasks are done in parallel, i.e., A and B and C, and can begin at the same time. Thus, project completion time is the maximum of the completion time of the three tasks. In this case, it is optimal to assign all three tasks to a single contractor. This follows since project completion time is either two or four days with a probability of 0.5 for each event, while if independent contractors are chosen, project completion time distribution is 4 days at 87.5 percent and 2 days at 12.5 percent. Thus, non-diversification (sole-sourcing) reduces the expected completion time: three days versus 3.75 days.

The intent of the above example is to show that diversification is not always the best thing to do. Its choice depends upon project task dependencies as well as other factors such as independence of completion time distributions among contractors and so on. As with other risk strategies, the decision of what to do is not easy.

In this chapter we have advocated the use of readily available decision technologies to manage schedule risk in projects. Although our focus has been somewhat narrow, i.e., confined to schedule, we believe that the use of modern decision technology tools can be useful to a project team as it plans and executes a project. In conclusion, we hope that this chapter stimulates research in utilizing the advances in both computer hardware (the ability to solve larger problems more quickly) and decision technology software (algorithms for solving large, complex optimization problems) to assist project planners in dealing with risk.