Project portfolio risk analysis with the consideration of project interdependencies

Bai, Libiao; Shi, Huijing; Kang, Shuyun; Zhang, Bingbing

doi:10.1108/ecam-06-2021-0555

Cited by 11 publications

(11 citation statements)

References 81 publications

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“…Wu et al (2014) established a PP risk regulation system from four dimensions, the PPM process, the risk regulation process, the approach and organization, and considered PP risk from a broader perspective. Bai et al (2021b) proposed the overall risk value by considering the interdependencies between projects as more than the mere sum of individual project risks. Breault and Cleveland (2020) identified gaps in PP risk management approaches used in the extant literature, recommended techniques for managing risks at the PP level and emphasized the role of organizations in fostering effective risk management at the PP level.…”

Section: Ppmmentioning

confidence: 99%

“…It can visually present the probabilistic relationships among a set of variables (Heckerman, 1997) and is frequently applied to address real-world problems such as those involving diagnosis, forecasting and manufacturing control (Heckerman et al, 1995). The tool has been extended to PP research fields, including project criticality modeling (Neumeier et al, 2018), risk response selection (Mokhtari and Aghagoli, 2020), PP optimization (Mancuso et al, 2019) and risk management (Bai et al, 2021b). Applying a BN, Neumeier et al (2018) analyzed the criticality of a single project in a PP context by considering both transitive dependencies and different dependency types in an integrated way.…”

Section: Bayesian Network For Risk Analysismentioning

confidence: 99%

“…Among regular risk analysis methods, a BN provides better performance since it can update beliefs and model multistate variables and dependencies (Chen et al, 2021a, b). (Bai et al, 2021b) built a BN of PP risks that models interactions between risks. Ghasemi et al (2018) and Guan and Guo (2015) proposed a BN of PP risk that considers project dependencies, though it only increases portfolio-level risks and ignores the impact of project dependencies on current risks.…”

Section: Bayesian Network For Risk Analysismentioning

confidence: 99%

“…Without considering causality, Bai et al (2020) developed a fuzzy Bayesian network (FBN) model to assess PP resource risk according to project interdependency. Bai et al (2021b) identified PP risks from the project interdependencies perspective and quantified the interactions between risks with an FBN. While these studies provide valuable insights, they either do not explicitly consider the impact of project dependency on existing risks or do not account for complex causality in the risk network.…”

Section: Introductionmentioning

confidence: 99%

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Modeling for external stakeholder risk assessment of project portfolios

Bai

Kang

Zhang

et al. 2022

ECAM

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PurposeExternal stakeholder risks (ESRs) caused by unfavorable behaviors hinder the success of project portfolios (PPs). However, due to complex project dependency and numerous risk causality in PPs, assessing ESRs is difficult. This research aims to solve this problem by developing an ESR-PP two-layer fuzzy Bayesian network (FBN) model.Design/methodology/approachA two-layer FBN model for evaluating ESRs with risk causality and project dependency is proposed. The directed acyclic graph (DAG) of an ESR-PP network is first constructed, and the conditional probability tables (CPTs) of the two-layer network are further presented. Next, based on the fuzzy Bayesian network, key variables and the impact of ESRs are assessed and analyzed by using GeNIe2.3. Finally, a numerical example is used to demonstrate and verify the application of the proposed model.FindingsThe proposed model is a useable and effective approach for ESR assessment while considering risk causality and project dependency in PPs. The impact of ESRs on PP can be calculated to determine whether to control risk, and the most critical and heavily contributing risks and project(s) in the developed model are identified based on this.Originality/valueThis study extends prior research on PP risk in terms of stakeholders. ESRs that have received limited attention in the past are explored from an interaction perspective in the PP domain. A new two-layer FBN model considering risk causality and project dependency is proposed, which can synthesize different dependencies between projects.

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Section: Ppmmentioning

confidence: 99%

Section: Bayesian Network For Risk Analysismentioning

confidence: 99%

Section: Bayesian Network For Risk Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling for external stakeholder risk assessment of project portfolios

Bai

Kang

Zhang

et al. 2022

ECAM

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“…As shown in Figure 6, the influencing factors that are topically related to participants are assigned to subgroup 1. Participants with different roles in the PP have special responsibilities (Bai et al, 2021a), but they cooperate to realize PPBs. Project managers manage employees to formulate benefits goals and directions.…”

Section: Structure and Properties Of The Influencing Factor Networkmentioning

confidence: 99%

Analysis of factors influencing project portfolio benefits with synergy considerations

Bai

Xue

Liu

et al. 2022

ECAM

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PurposeRealizing project portfolio benefits (PPBs) is considered a key challenge faced by enterprises. This challenge can largely be attributed to an unclear understanding of the factors influencing PPBs. However, synergistic relationships create complexity for the management of influencing factors. In response to this dilemma, the objective of this study is to quantitatively investigate the factors influencing PPBs while considering the synergistic effect among factors to provide guidelines for benefits management.Design/methodology/approachThrough an integration of the synergy degree of the composite system model and social network analysis (SNA), a refined model is proposed to explore the factors influencing PPBs. First, a list that includes financial and nonfinancial influencing factors is clarified. Then, the corresponding network links, which represent the synergistic relationships among the factors, are innovatively assessed based on the synergy degree of the composite system. Finally, the influencing factor network is analyzed using both individual and overall indicators of SNA.FindingsThe resulting evidence demonstrates that four critical influencing factors exist, namely, “project managers,” “purchasers,” “development capacity” and “tangible resources.” These factors are relatively important and should be prioritized. Furthermore, the factors are divided into three subgroups: participant, resource and governmental factors. A general observation from the results is that factors that share the same subgroup are more likely to have a synergistic effect advantage, which leads to an increase in PPBs.Originality/valueThe value of this paper lies in its proposition of a quantitative model that can be used to measure and analyze the factors influencing PPBs with synergy considerations. This research contributes to the body of knowledge on benefits management by linking synergy with PPBs. It presents new insights for managers on how PPBs may be effectively managed and promoted from the perspective of influencing factors.

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