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External 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.

A 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.

The 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.

This 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.

Research and development (R&D) projects are often pursued through a project portfolio (PP). R&D PPs involve many stakeholders, and without proactive management, their interactions may lead to conflict risks. These conflict risks change dynamically with different stages of the PP life cycle, increasing the challenge of PP risk management. Existing conflict risk research mainly focuses on source identification but lacks risk assessment work. To better manage the stakeholder conflict risks (SCRs) of R&D PPs, this study employs the dynamic Bayesian network (DBN) to construct its dynamic assessment model.

This study constructs a DBN model to assess the SCRs in R&D PP. First, an indicator system of SCRs is constructed from the life cycle perspective. Then, the risk relationships within each R&D PPs life cycle stage are identified via interpretative structural modeling (ISM). The prior and conditional probabilities of risks are obtained by expert judgment and Monte Carlo simulation (MCS). Finally, crucial SCRs at each stage are identified utilizing propagation analysis, and the corresponding risk responses are proposed.

The results of the study identify the crucial risks at each stage. Also, for the crucial risks, this study suggests appropriate risk response strategies to help managers better perform risk response activities.

This study dynamically assesses the stakeholder conflict risks in R&D PPs from a life-cycle perspective, extending the stakeholder risk management research. Meanwhile, the crucial risks are identified at each stage accordingly, providing managerial insights for R&D PPs.

Comprehensive project portfolio risk (PPR) analysis is essential for the success and sustainable development of project portfolios (PPs). However, project interdependency creates complexity for PPR analysis. In this study, considering the interdependency effect among projects, the authors develop a quantitative evaluation model to analyze PPR based on a fuzzy Bayesian network.

In this paper, the primary purpose is to comprehensively evaluate project portfolio risk considering the interdependency effect using a systematical model. Accordingly, a fuzzy Bayesian network (FBN) is developed based on the existing studies. Specifically, first, the risks in project portfolios are identified from the project interdependencies perspective. Second, a fuzzy Bayesian network is adopted to model and quantify the interaction relationships among risks. Finally, the model is implemented to analyze the occurrence situation and characteristics of risks.

The interdependency effect can lead to high-stake risks, including weak financial liquidity, a lack of cross-project members and project priority imbalance. Furthermore, project schedule risks and inconsistency between product supply and market demand are relatively sensitive and should also be prioritized. Also, the validity of this risk evaluation model has been proved.

The findings identify the most sensitive risks for guaranteeing portfolio implementation and reveal interdependency effect can trigger some specific risks more often. This study proposes for the first time to measure and analyze project portfolio risk by a systematical model. It can help systematically assess and manage the complicated and interdependent risks associated with project portfolios.

Panel data-based demand forecasting models have been widely adopted in various industrial settings over the past few decades. Despite being a highly versatile and intuitive method, in the literature, there is a lack of comprehensive review examining the strengths, the weaknesses, and the industrial applications of panel data-based demand forecasting models. The purpose of this paper is to fill this gap by reviewing and exploring the features of various main stream panel data-based demand forecasting models. A novel process, in the form of a flowchart, which helps practitioners to select the right panel data models for real world industrial applications, is developed. Future research directions are proposed and discussed.

It is a review paper. A systematically searched and carefully selected number of panel data-based forecasting models are examined analytically. Their features are also explored and revealed.

This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. A novel model selection process is developed to assist decision makers to select the right panel data models for their specific demand forecasting tasks. The strengths, weaknesses, and industrial applications of different panel data-based demand forecasting models are found. Future research agenda is proposed.

This review covers most commonly used and important panel data-based models for demand forecasting. However, some hybrid models, which combine the panel data-based models with other models, are not covered.

The reviewed panel data-based demand forecasting models are applicable in the real world. The proposed model selection flowchart is implementable in practice and it helps practitioners to select the right panel data-based models for the respective industrial applications.

This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. It is original.