Cost risk at the portfolio level – opportunities for aggregating cost and uncertainty analyses from investment projects

FFI-Report 2025
This publication is only available in Norwegian

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Petter Fredrik Hemnes Magnus Akre Thorup
Defence acquisitions are associated with significant cost risk. Projects often have cost escalations well beyond their initial point estimates. In recognition of this uncertainty, it has become common practice to express cost estimates as a probability distribution where a range of possible cost outcomes has probabilities assigned to them. The spread in these cost outcomes is what we typically call the cost risk.
When cost risk materializes as cost escalations, it can have large negative impacts on defence capabilities. Cost escalation in one or several acquisition projects will, under budget constraints, lead to delay, downscaling, budget rebalancing, and, in the worst-case, cancellation of projects – all of which can negatively impact defence capabilities. To reduce the negative impacts that cost risks may have, it follows that cost risk in a portfolio must be managed well.
Defence portfolio risk is not estimated or analysed in the Norwegian defence sector. We propose ways to estimate the cost risk and methods to analyse it. We apply the methods on a selection of Norwegian defence acquisition projects. Using cost and uncertainty analyses from acquisitions projects, we construct a mini portfolio for which we calculate cost risk, decompose cost risk, and describe drivers of risk. Our methods illustrate how cost risk may be made visible at a portfolio level and how analyses of the cost risk may help portfolio managers govern risk and uncertainty.
We also discuss limitations in our methods, specifically due to unknown data quality, ignoring the time dimension, and unknown covariance between project risk. To alleviate these limitations, we make four suggestions that are within portfolio managers’ scope to act on:
1. Covariance should be estimated.
2. Risk should be phased over time.
3. Cost estimates should be made more accessible.
4. The quality of the uncertainty analyses should be asserted.

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