TY - JOUR
T1 - Quantifying the yield of risk-bearing IT-portfolios
AU - Peters, R.J.
AU - Verhoef, C.
N1 - PV2007
PY - 2008
Y1 - 2008
N2 - We proposed a method to quantify the yield of an IT-investment portfolio in an environment of uncertainty and risk. For various common implementation scenarios such as growing demands during implementation without deadline extensions we showed how to monetize their impact on the net present value. Depending on the business case this can lead to higher or lower gains. We also took failure of projects within an IT-investment portfolio into account, by appraising the loss in case of failure, resulting in a more realistic yield. To provide maximal insight into this yield, we proposed to treat it as a stochastic variable. We explained how to infer various portfolio yield distributions: discrete, continuous, and cumulative distributions, leading to useful summaries such as box plots and histograms. We argued that these information-rich characterizations support decision makers in taking calculated risks, and provided insight in how to address IT-specific risks and what such risk mitigation may cost. We explained our approach by quantifying the expected yield of a small four project portfolio under uncertainty and risk, and we provided the results for a larger and realistic IT-investment portfolio. © 2007 Elsevier B.V. All rights reserved.
AB - We proposed a method to quantify the yield of an IT-investment portfolio in an environment of uncertainty and risk. For various common implementation scenarios such as growing demands during implementation without deadline extensions we showed how to monetize their impact on the net present value. Depending on the business case this can lead to higher or lower gains. We also took failure of projects within an IT-investment portfolio into account, by appraising the loss in case of failure, resulting in a more realistic yield. To provide maximal insight into this yield, we proposed to treat it as a stochastic variable. We explained how to infer various portfolio yield distributions: discrete, continuous, and cumulative distributions, leading to useful summaries such as box plots and histograms. We argued that these information-rich characterizations support decision makers in taking calculated risks, and provided insight in how to address IT-specific risks and what such risk mitigation may cost. We explained our approach by quantifying the expected yield of a small four project portfolio under uncertainty and risk, and we provided the results for a larger and realistic IT-investment portfolio. © 2007 Elsevier B.V. All rights reserved.
U2 - 10.1016/j.scico.2007.11.001
DO - 10.1016/j.scico.2007.11.001
M3 - Article
SN - 0167-6423
VL - 71
SP - 17
EP - 56
JO - Science of Computer Programming
JF - Science of Computer Programming
ER -