Probabilistic Risk Management in Project Portfolios

Love Ekenberg, Mats Danielson
Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications, PMLR 147:361-364, 2021.

Abstract

We discuss a novel method for business risk handling in project portfolios under strong uncertainty, where we utilise event trees including adverse consequences together with mitigation costs and expected effects, where consequence and event probabilities and costs are represented by random parameters. The method has been developed to support large-scale real-life applications of portfolio risk management, where the properties of the probabilities and values are entered by domain experts with often very limited knowledge of probability theory, and we demonstrate how this can be accomplished with minor information loss. The method is currently in use in one of the world’s largest telecom equipment manufacturers which has a vast project portfolio of tenders, with each successful tender subsequently becoming an order in the order book portfolio.

Cite this Paper

BibTeX
@InProceedings{pmlr-v147-ekenberg21a, title = {Probabilistic Risk Management in Project Portfolios}, author = {Ekenberg, Love and Danielson, Mats}, booktitle = {Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications}, pages = {361--364}, year = {2021}, editor = {Cano, Andrés and De Bock, Jasper and Miranda, Enrique and Moral, Serafı́n}, volume = {147}, series = {Proceedings of Machine Learning Research}, month = {06--09 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v147/ekenberg21a/ekenberg21a.pdf}, url = {https://proceedings.mlr.press/v147/ekenberg21a.html}, abstract = {We discuss a novel method for business risk handling in project portfolios under strong uncertainty, where we utilise event trees including adverse consequences together with mitigation costs and expected effects, where consequence and event probabilities and costs are represented by random parameters. The method has been developed to support large-scale real-life applications of portfolio risk management, where the properties of the probabilities and values are entered by domain experts with often very limited knowledge of probability theory, and we demonstrate how this can be accomplished with minor information loss. The method is currently in use in one of the world’s largest telecom equipment manufacturers which has a vast project portfolio of tenders, with each successful tender subsequently becoming an order in the order book portfolio.} }
Endnote
%0 Conference Paper %T Probabilistic Risk Management in Project Portfolios %A Love Ekenberg %A Mats Danielson %B Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications %C Proceedings of Machine Learning Research %D 2021 %E Andrés Cano %E Jasper De Bock %E Enrique Miranda %E Serafı́n Moral %F pmlr-v147-ekenberg21a %I PMLR %P 361--364 %U https://proceedings.mlr.press/v147/ekenberg21a.html %V 147 %X We discuss a novel method for business risk handling in project portfolios under strong uncertainty, where we utilise event trees including adverse consequences together with mitigation costs and expected effects, where consequence and event probabilities and costs are represented by random parameters. The method has been developed to support large-scale real-life applications of portfolio risk management, where the properties of the probabilities and values are entered by domain experts with often very limited knowledge of probability theory, and we demonstrate how this can be accomplished with minor information loss. The method is currently in use in one of the world’s largest telecom equipment manufacturers which has a vast project portfolio of tenders, with each successful tender subsequently becoming an order in the order book portfolio.
APA
Ekenberg, L. & Danielson, M.. (2021). Probabilistic Risk Management in Project Portfolios. Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications, in Proceedings of Machine Learning Research 147:361-364 Available from https://proceedings.mlr.press/v147/ekenberg21a.html.

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