Project Portfolio Risk Research Articles

Towards a comprehensive framework for risk assessment of organizational development project portfolios

The benefits of risk management in the context of project portfolios have been widely recognized in the literature. However, approaches that assess the risk of organizational development project portfolios from the perspective of how the portfolio delivers value to the parent organization remain largely unexplored. To address this gap, our research takes a constructivist approach and an organizational perspective on project portfolios. We conducted twenty-eight semi-structured interviews and used thematic analysis to identify and relate four themes of a comprehensive project portfolio risk assessment (PPRA) framework: "project portfolio as the organizational unit for PPRA"; "organizational capabilities as portfolio outcomes in which PPR can be assessed"; "project portfolio levels as sources of risk factors in PPRA"; and "balance between project portfolio attributes complexity". Within the framework of organizational development project portfolios, this study contributes to our understanding of PPRA by providing two propositions: (1) The capabilities to be generated by the project portfolio can be used as the portfolio primary results on which PPRA can assess the risk of the project portfolio, establishing the impact of PPR on the project portfolio value delivering to the parent organization, and (2) The risk factors that impact the project portfolio expected results can be represented into PPRA as ‘output-related’ risk factors and ‘outcome-related’ risk factors.

A Sugeno ANFIS Model Based on Fuzzy Factor Analysis for IS/IT Project Portfolio Risk Prediction

Risk inherence jeopardises Information System (IS) and Information Technology (IT) Project Portfolio Management (PPM) to realise thestrategic objectives. Previous studies have mainly provided Artificial Intelligence (AI) and statistical models to predict the overall riskof IS/IT project portfolio, whereas neuro-fuzzy models were rarely used. This paper proposes a Sugeno Adaptive Neuro-Fuzzy InferenceSystem (ANFIS) model based on Fuzzy Factor Analysis (FFA) named ANFIS-OPR to predict the overall risk of IS/IT project portfolio fromhistorical IS/IT project risk data. The ANFIS-OPR inputs are the relevant factor loadings resulting from the FFA application on the IS/IT projects risks set to cope with the curse of dimensionality. Then, the Sugeno ANFIS model is adopted to give strategic interpretability to the predicted IS/IT project portfolio overall risk by implementing the IS/IT Project Management Office (PMO) expert knowledge, represented by fuzzy rules, on the relationship between IS/IT project portfolio strategic alignment and the IS/IT projects risks. The ANFISOPR outputs are the predicted Overall Portfolio Risk (OPR) and Root Mean Square Error (RMSE). The paper also presents an IS/IT PMO case study that shows the proposed ANFIS-OPR efficacy, which predicted the OPR values closely to the OPR estimates with an accepted RMSE of 0.108. The proposed ANFIS-OPR is a novel intelligent decision-making tool that enables the IS/IT PMO to monitor the OPR, considering its linkage with strategic alignment; thus, contingency plans can be carried out appropriately while ensuring that the IS/IT project portfolio is strategically aligned.

Project Risk Response Decision Making Under Uncertain Project Interdependencies

Project portfolio (PP) risk management entails making risk response decisions (RRDs) on single projects within PPs (SPPPs). However, the presence of uncertain project interdependencies (UPIs) increases the difficulty of RRDs. This study provides a decision model to make robust RRDs for SPPPs under UPIs, using a four-stage interval optimization model based on a two-tier risk-project network. A two-tier network is first constructed where the upper nodes denote the risks and the lower nodes denote the alternative projects. Next, a PP selection model is developed to obtain the probability of the projects being selected through simulation, in which the project interdependencies (PIs) are measured by the additional returns from the PP. Polytopic fuzzy linguistic term sets are formed to explore the strength of the relationships between the risks, and between the projects and risks. Third, the gravity model and the decision-making trial and evaluation laboratory are applied to prioritize the risks. Fourth, an interval optimization model under the uncertain response budget is constructed to yield the RRD outcome. The proposed model is validated through the RRDs for a Chinese construction firm. The results inform that, first, the mean utility of a decision maker (DM) considering UPIs fares better than those ignoring PIs and those considering PIs with certainty, second, the DM's mean utility considering PIs with certainty is still better than those ignoring PIs, and third, relying on the minimum budget threshold is more effective for reducing the uncertainty and improving the mean utility of a DM.

A robust optimization model for R&D project portfolio risk response decision-making considering risk interaction and regret perception

A robust optimization model for R&D project portfolio risk response decision-making considering risk interaction and regret perception

Modeling of Project Portfolio Risk Evolution and Response under the Influence of Interactions

Due to dynamic changes in both internal and external environments, the risk evolution of the project portfolio (PP) becomes extremely complicated, thereby increasing the difficulties of effective risk response. In particular, the real-time influence of risk interactions on the evolution of project portfolio risk (PPR) often goes unnoticed. Meanwhile, risk contagiousness is completely ignored in risk response. To tackle this challenge, this study proposes a PPR evolution and response (PPRER) model by improving the Barrat–Barthelemy–Vespignani (BBV) model and by introducing the evolutionary dynamics method into the PPR response research. The feasibility and applicability of the proposed model are verified through a numerical illustration. Computational results demonstrate that the proposed model can simulate the evolution process of PPRs under the influence of their interactions and give a snapshot of the real-time interactive relationship between PPRs. Based on the obtained results, decision-makers can take effective risk responses by identifying critical strategy intrusion nodes at any time in the evolution process.

The Relation Between Risk and Portfolios Risk Management Project Portfolios

Project Portfolio Management (PPM) is a subsidiary of project management. It is used to control and coordinate the projects in various organizations' portfolios with clear objectives of maximizing the project results, balancing portfolio threats, and aligning the projects to meet the business's strategic aims. In many organizations, PPM is ranked on a higher level than the project management concept because the goal of PPM is to achieve the strategic objectives of an organization. However, risks typically arise during project implementation and management. The project managers constantly work to minimize the project risks while simultaneously maximizing the intended values of a given project. The project management teams should manage the risks while achieving the strategic goals and objectives of an organization. This paper discusses the relationship between PPM and risk management during the project implementation process. The paper explored the use of a systematic literature review to come up with these critical findings. The author recommends that there's a need to use empirical data to validate the existing theories and published literature findings. The project Portfolio Risk Management (PPRM) has practical and theoretical approaches in risk management (RM), decision theory, and modern portfolio theory. The use of a systematic literature review approach was used in the execution of this study to gain more insights into the influence of risks on project portfolio and success, identification of the project portfolio risks, and the management of risks in the project portfolio management context.

Assessing project portfolio risk via an enhanced GA-BPNN combined with PCA

Assessing project portfolio risk (PPR) is essential for organizations to grasp the overall risk levels of project portfolios (PPs) and realize PPR mitigation. However, current research is inadequate to effectively assess PPR, which brings challenges to managing PPR. In this context, the purpose of this study is to develop a PPR assessment model via an enhanced backpropagation neural network (BPNN). First, PPR assessment criteria considering project interdependencies are determined. Second, fuzzy logic is used to obtain original data for assessment criteria. Principal component analysis (PCA) is then employed to reduce the dimensionality of assessment criteria and derive the input and output of BPNN. Third, an improved genetic algorithm (IGA) is designed to optimize the initial weights and thresholds of BPNN. On this basis, the PCA-IGA-BPNN assessment model is constructed, followed by training and testing, possessing a test accuracy of 98.6%. Finally, comparison experiments are conducted from both internal and external perspectives. For internal comparison, the proposed model yields less mean absolute percentage error (MAPE), mean square error (MSE), and root mean square error (RMSE) than PCA-GA-BPNN, IGA-BPNN, PCA-BPNN and BPNN and offers the largest convergence speed (γ). As for external comparison, the presented model produces lower MAPE, MSE, and RMSE than Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN) and has the largest coefficient of determination (R2). Results indicate that the established model performs more satisfactorily in assessing PPR. This research enriches PPR assessment methods and provides managers with a useful tool to evaluate PPR.

Modeling the Risk of an Organizational Development Portfolio

Project portfolios have been seen as a structured way to implement business changes based on the organizational strategic goals. In this regard, portfolio risk could be related with the risk factors impact on strategic objectives. However, risk factors related to the project portfolio do not directly impact strategic objectives; their strategic impact is related to how the portfolio delivers value to the organization. This paper describes a risk assessment implementation conducted in an organizational development project portfolio case study, showing how the project portfolio risk is linked with the strategic goals. This was accomplished based on a proposed conceptualization for project portfolio risk assessment (PPRA). The risk assessment was conducted considering interdependencies between risk factors and how risk factors derived from both project and portfolio levels impact primarily on projects within the portfolio. And then, considering how the risk factors prior impact is transferred from projects to portfolio outcomes and, in turn, to strategic objectives. The risk assessment results allowed to identify the most influential risk factors for the portfolio case study, representing an essential input for designing the risk response plan.

GA-BP neural network modeling for project portfolio risk prediction

PurposeProject portfolio risk (PPR) management plays an important role in promoting the smooth implementation of a project portfolio (PP). Accurate PPR prediction helps managers cope with risks timely in complicated PP environments. However, studies on accurate PPR impact degree prediction, which consists of both risk occurrence probabilities and risk impact consequences considering project interactions, are limited. This study aims to model PPR prediction and expand PPR prediction tools.Design/methodology/approachIn this study, the authors build a PPR prediction model based on a genetic algorithm and back-propagation neural network (GA-BPNN) integrated with entropy-trapezoidal fuzzy numbers. Then, the authors verify the proposed model with real data and obtain PPR impact degrees.FindingsThe test results indicate that the proposed method achieves an average absolute error of 0.002 and an average prediction accuracy rate of 97.8%. The former is reduced by 0.038, while the latter is improved by 32.1% when compared with the results of the original BPNN model. Finally, the authors conduct an index sensitivity analysis for identifying critical risks to effectively control them.Originality/valueThis study develops a hybrid PPR prediction model that integrates a GA-BPNN with entropy-trapezoidal fuzzy numbers. The authors use this model to predict PPR impact degrees, which consist of both risk occurrence probabilities and risk impact consequences considering project interactions. The results provide insights into PPR management.

Disclosing the Tacit Links between Risk and Success in Organizational Development Project Portfolios

Project portfolios aim to impact organizational strategic goals, influencing both the organization’s business model and its processes. Nonetheless, the actual impact is dependent on the portfolio’s success, which is affected by the materialization of risk factors. This study aims to examine the tacit conceptualization of project portfolio risk as a risk measure explicitly based on project portfolio success itself. In order to focus on the portfolios of organizational development projects, Social Representation Theory was adopted to analyze empirical evidence from twenty-eight semi-structured interviews conducted with project portfolio practitioners. Findings showed that strategic fit, future preparedness, and stakeholder satisfaction were dimensions of success within which project portfolio risk could be conceptualized. Additionally, results evidenced that risk factors influenced project portfolio success through systematic and non-systematic impacts on project portfolio outputs, and also had direct impacts on project portfolio outcomes. This paper provides empirical evidence to back up the conceptualization of project portfolio risk explicitly oriented to portfolio success as a multidimensional risk measure. It represents a new avenue for conducting portfolio risk analysis for both practitioners and academics, orienting the decision-making process based on the portfolio success rather than only on the success of each project.

Critical Interactive Risks in Project Portfolios from the Life Cycle Perspective

The need for enterprises to manage project portfolio risks over the life cycle has become increasingly prominent. It is essential to evaluate and manage them to achieve project portfolios and organizations’ success. Unlike project risk, project portfolio risk is more complex and uncertain due to risk interactions. Risk management is unsatisfactory in project portfolios due to the lack of awareness of risk interactions and the life cycle. The purpose of this paper is to identify the critical risks of project portfolios over the life cycle considering risk interactions. We primarily verified 20 identified risks through a questionnaire survey and an expert interview method and evaluated the interactions among them using the Delphi method. Furthermore, risk interactions were analyzed using the social network analysis (SNA) methodology to determine the important risks. Finally, a comprehensive evaluation of important risks was carried out to identify critical risks according to the evaluation principles. The results identified six critical portfolio risks, two key risk contagion paths and revealed risk characteristics of different life cycle phases. This research considerably contributes to the body of knowledge pertaining to project portfolio management that will enable organizations that implement project portfolios and similar multi projects to emphasize critical risks.

Project portfolio risk categorisation – factor analysis results

The subject of this article is project portfolio risk categorisation. Research conducted indicated categories containing the most probable and significant risks. The research described in this paper was carried out in two stages. In the first stage, the relevant literature was reviewed and the Delphi method was used to identify 36 risks specific to a project portfolio. In the second stage, the respondents (project portfolio managers) assessed the probability of each risk occurring and the impact of that risk on the objectives of the project portfolio. The empirical data obtained in this way made it possible to conduct an exploratory factor analysis and to identify the risk categories of the project portfolio. The presented results may also contribute to a broader discussion concerning the validity of identifying project portfolio risks and how to categorise them. The results may be useful for further discussion on the empirical confirmation of three categories of portfolio risks proposed by the Project Management Institute.

A method for project portfolio risk assessment considering risk interdependencies – a network perspective

Project portfolios represent the bridge between projects and strategy. However, the final results may not be as expected because materialization of risk factors. Hence, literature has acknowledged project portfolio risk assessment as an element of the project portfolio risk management, being the element that provides information on the importance of risk factors. For that, some specific characteristics should be considered, such as risk interdependency influence and the risk factors impact over portfolio higher levels. Thus, this study is focused on the development of a method for project portfolio risk assessment that considers both risk factor interdependencies and their impact on the strategic objectives as a network. In addition, the method also allows incorporating both risk factors derived from projects and derived at project portfolio level. A representative example is provided to illustrate the proposed method.

A New Model for Project Portfolio Risk Evolution and Response Under the Influence of Interactions

A New Model for Project Portfolio Risk Evolution and Response Under the Influence of Interactions

Domain Analytics for Project Portfolio Risk Management. A Scientometric Approach of Bibliometry and Collaboration Networks

Domain Analytics for Project Portfolio Risk Management. A Scientometric Approach of Bibliometry and Collaboration Networks

USING MONTE CARLO SIMULATION TO QUANTIFY THE COST IMPACT OF SYSTEMIC RISK FACTORS IN A PROJECT PORTFOLIO: A CASE STUDY

In terms of project risk management, ‘systemic risk’ is identified as risks which are artefacts of the environment which a project is executed in, and are related to (i) the project team’s actions, (ii) how project controls are managed and interact, and (iii) how the project is planned and executed. This paper proposes a methodology to estimate the cost impact of systemic risk on a portfolio of projects by using risk quantification and Monte Carlo simulation, in the absence of a validated parametric risk model, to estimate the systemic risks in an entire portfolio of projects. The case study simulation results indicate a significant effect of systemic risks on the project portfolio risk profile, where systemic risks increased the P80 value of the contingency requirement by +85.6%. The successful management of systemic risk would contribute to project success by limiting unnecessary waste.

Project portfolio risk analysis with the consideration of project interdependencies

PurposeComprehensive 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.Design/methodology/approachIn 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.FindingsThe 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.Originality/valueThe 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.

Project portfolio risk assessment in digital transformation: challenges and opportunities

Digital Transformation (DT) has its own challenges and demands on organizational strategy and processes. Consequently, DT also has implications on Project Portfolio Risk Assessment (PPRA). Thus, this study is focused on identifying these implications based on a structured search and content analysis of the literature in the field, which led to the identification of eight implications for risk assessment. Then, through qualitative analysis and according to the nature of each implication identified, a set of five challenges and three opportunities were established for PPRA considering DT context. This research shows that DT not only demands new considerations for PPRA but also offers opportunities to face them, leading to improve the PPRA impact on project portfolio management decision-making process. The present study contributes to the current PPRA research by outlining a view of how PPRA can be integrated into organizational DT. The challenges and opportunities identified could be used by practitioners and scholars to research, design, and implement PPRA in DT context.

Project portfolio risk management: a structured literature review with future directions for research

Project Portfolio Risk Management (PPRM) has been identified as a relevant area regarding project portfolio success. This paper reports on a structured literature review of PPRM. A structured search and selection process was carried out and conventional content analysis was conducted in the literature analysis of 62 papers published in international journals. PPRM has its theoretical and practical bases in the modern theory of portfolios, decision theory and risk management (RM). The content analysis reveals four main recurrent topics in PPRM: (1) The influence of RM on project portfolio success, based on project portfolio impact level, moderators or contingency factors between RM and project portfolio success, and PPRM dimensions; (2) risk and project interdependencies, highlighting resources, technology, outcome, value, and accomplishment project interdependencies; (3) project portfolio risk (PPR) identification, where four main risk source categories are identified; and (4) PPR assessment, composed of risk measures and the main methods used for risk assessment. Therefore, this study provides an overview of PPRM as a research field, while it also promotes four future research directions: (1) PPRM as part of organizational RM; (2) RM,success dimensions and strategic impact; (3) mechanisms for PPR assessment, and (4) PPRM as a complex and dynamic system.

Управление рисками портфеля сетевой взаимосвязи проектов (на примере агрохолдинга)

The article is devoted to the problem of portfolio risk management of network project interdependencies in an agricultural company, as the lack of existing methods of project portfolio risk assessment and proper consideration of interdependencies are traced. During the study, we conducted an in-depth interview with the CFO of the company to identify the pool of ongoing projects of the company, determine the types of interproject dependencies, the likelihood of the corresponding risk in the project and the level of losses in monetary terms. Using the method of expert assessments and the noisy-OR gate tool, calculations were made in the GeNIe software product to determine the overall risk of the project portfolio after identifying high-risk projects and then recommendations were given for the application of a dynamic portfolio risk management model based on the software product used in the company.