Abstract
In an ever-increasing competitive business environment, it has become increasingly important to be able to obtain efficient and sustainable business operations not only by efficient core procedures but also by being able to minimise losses incurred by risk taking. The latter by handling both operational risks and financial risks in a unified model. This is important not least in businesses that handle sets of simultaneous large projects, which is the topic of risk handling in project portfolios. In this paper, we present a novel method for business risk handling for project portfolios under strong uncertainty. The method is based on event trees representing each adverse consequence modelled, together with mitigation costs and effects. The aggregation of all consequences for all projects together constitutes the risk portfolio for the business. This method is used in one of Sweden’s largest manufacturing enterprises having a vast portfolio of projects in the form of ongoing tenders for orders.
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Notes
- 1.
The two-point uniform and trapezoid distributions have λ = 0.
- 2.
Refer to any textbook on statistics for particulars, such as [17].
- 3.
Let y = cdf(x, R) be the cumulative probability for the cost x for the project risk R. Then the inverse x = cdf−1(y, R) yields the risk exposure limit for the risk level y. Let mean(R) be the mean cost for the project risk R. Then the following is a risk measure for y > 0.75:
t(y, r) = (cdf−1(y, R) + mean(R))/2
The net gain measure for a mitigation M (with mitigation cost m(M)) compared to an unmitigated R at the risk level y is gain(y, R, M) = (t(y, R) − t(y, M))/m(M) − 1 if m(M) > 0.
- 4.
The expected cost is not the crossover at 50% CDF since it is asymmetric.
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This research was partly funded by strategic grants from the Swedish government within ICT – The Next Generation.
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Danielson, M., Ekenberg, L. (2018). Efficient and Sustainable Risk Management in Large Project Portfolios. In: Zdravkovic, J., Grabis, J., Nurcan, S., Stirna, J. (eds) Perspectives in Business Informatics Research. BIR 2018. Lecture Notes in Business Information Processing, vol 330. Springer, Cham. https://doi.org/10.1007/978-3-319-99951-7_10
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