Project Portfolio Research Articles

Modelling for resource risk propagation in dynamic heterogeneous project portfolio network

Project portfolio (PP) exists resource risk that propagates because of the relationships among projects arising from resource sharing. While earlier research has highlighted the necessity to control PP resource risk (PPRR) propagation, research that can provide an in-depth analysis of PPRR propagation remains lacking. This paper proposes a PPRR propagation model to bridge this gap by applying the SIR model to PP network (PPN). In the PPRR propagation model, the dynamics and heterogeneity that PPN is characterized are analyzed, following which the dynamic heterogeneous PPN is visualized. Then, the susceptible-infected-removed (SIR) model is improved considering the dynamics and heterogeneity of PPN. Thereafter, the applicability of the PPRR propagation model is verified by a numerical example. This paper enriches theories related to PPRR management and infectious disease model. Meanwhile, utilizing the PPRR propagation model, control measures for PPRR propagation from the perspectives of infection source and immunization strategy can be obtained.

A simheuristic for project portfolio optimization combining individual project risk, scheduling effects, interruptions, and project risk correlations

This paper introduces a simheuristic method to the Project Portfolio Selection Problem, designed to maximize the net present value of the portfolio while considering uncertain costs, schedules, interruptions, and inter-project risk correlations. The novel approach combines techniques from Monte Carlo simulation, critical path analysis, queuing theory, and optimization, integrating baseline schedules, project-level uncertainties, budgetary constraints, and risk correlations in a single model. A computational experiment is conducted on a realistic set of ten candidate projects and validated respect to the deterministic version of the problem, demonstrating its ability to select near optimal portfolio proposals with varying combinations of risk and net present value. The findings highlight the significant impact of factors such as contingency reserve allocation policies, operational interruptions, and project risk correlations on portfolio decisions, constituting a helpful framework for the decision-makers at portfolio level.

A Portfolio Management Method for Process Mining-Enabled Business Process Improvement Projects

AbstractProcess mining has received tremendous attention from research and industry, establishing itself as a highly sought-after technology. Despite the technological maturity of process mining solutions, which has been achieved through extensive investments in research and development, organizations still face the challenge of elusive value when systematically adopting process mining. The authors attribute this dilemma to a lack of support for scaling and managing process mining project portfolios. To address this practical need and research gap, the authors propose a method for managing portfolios of so-called process mining value cases, which are defined as process mining-enabled business process improvement projects, towards an evolutionary roadmap (mapper). The method is designed to support organizations identify portfolios of process mining projects that generate value by improving business processes. The method was developed through a combination of design science research and situational method engineering and comprises five activities that each outline techniques, roles, and tools: strategize, identify, select, implement, and monitor. The method has been instantiated as a software prototype and iteratively evaluated for applicability and real-world fidelity by involving an expert panel of academics and practitioners. The usefulness of the artifact was substantiated through a real-world case study in a naturalistic setting.

Spatial Assets Value Extraction and Integrated Utilization of Old Communities: Case of Central Guangzhou, China

Extracting the economic value by the integrated utilization of space in old communities is crucial for encouraging independent participation from enterprises and residents, reducing reliance on government leadership and fiscal investment. This study starts from the active perspective of spatial assets and constructs a value activation framework for old communities by balancing “endogenous demand” and “exogenous opportunities”. By enhancing the “economic value” through the “use value”, five methods for value extraction and overall project utilization paths are proposed, guided by a dynamic “cost-revenue” balance. Using multi-source data, we identify the spatial assets of 1096 old communities in central Guangzhou and apply a market comparison method for an economic value assessment. Additionally, this study offers recommendations on the timing and project portfolios for regeneration efforts, along with strategies for establishing a coordinating implementation entity and fund account. This research provides strategic insights for advancing the regeneration of old communities by tapping into their macro-level economic potential.

Promoters as social controls to facilitate knowledge sharing, conflict management and innovation project portfolio performance

Organizational control theory emphasizes aligning members' actions with organizational goals through formal and social controls. In innovation management, formal controls like project management or stage‐gate processes are limited to measurable and standardized areas. For situations that are complex, unpredictable and highly idiosyncratic, social controls need to be applied for alignment. Knowledge sharing and competitive conflicts are two such innovation‐related phenomena that elude formal control and instead need social controls for alignment. In this research, we propose that promoters—individuals who actively support innovation—serve as social control agents. While previous research has shown the positive effect of single promoters on individual innovation project success, we propose a comprehensive implementation of all four promoter roles, and we test their joint effect on the intermediate outcomes of knowledge sharing and competitive conflict management facilitating innovation project portfolio performance. Drawing on cross‐industry data of 125 firms collected from 986 employees, this article provides empirical evidence that comprehensive implementation of all four promoter roles enhances knowledge sharing and reduces competitive conflicts, thereby improving innovation project portfolio performance. The research reframes promoters as social control agents, emphasizing their collective necessity for managing complex organizational issues and contributing to overall project success.

Optimizing Multi-Stage Project Portfolio Selection Considering Integrating Lifecycle and Interactions

Project portfolio selection is essential for a company to achieve its strategic goals. Due to constraints such as budget and manpower, companies cannot undertake all projects simultaneously and must prioritize those offering the highest value. Projects often interact and progress through various phases, adding complexity to the selection process. To address these challenges, this study introduces a model that accounts for the multi-stage execution of projects, their interactions, and multiple objectives. A novel multi-objective optimization algorithm is developed to solve this problem, along with a refined project selection method designed to offer decision-makers enhanced insights. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed approach.

FOUNTAIN: a modular research platform for integrated real-world evidence generation

BackgroundReal-world evidence (RWE) plays a key role in regulatory and healthcare decision-making, but the potentially fragmentated nature of generated evidence may limit its utility for clinical decision-making. Heterogeneity and a lack of reproducibility in RWE resulting from inconsistent application of methodologies across data sources should be minimized through harmonization.MethodsThis paper’s aim is to describe and reflect upon a multidisciplinary research platform (FOUNTAIN; FinerenOne mUlti-database NeTwork for evidence generAtIoN) with coordinated studies using diverse RWE generation approaches and explore the platform’s strengths and limitations. With guidance from an executive advisory committee of multidisciplinary experts and patient representatives, the goal of the FOUNTAIN platform is to harmonize RWE generation across a portfolio of research projects, including research partner collaborations and a common data model (CDM)–based program. FOUNTAIN’s overarching objectives as a research platform are to establish long-term collaborations among pharmacoepidemiology research partners and experts and to integrate diverse approaches for RWE generation, including global protocol execution by research partners in local data sources and common protocol execution in multiple data sources through federated data networks, while ensuring harmonization of medical definitions, methodology, and reproducible artifacts across all studies. Specifically, the aim of the multiple studies run within the frame of FOUNTAIN is to provide insight into the real-world utilization, effectiveness, and safety of finerenone across its life-cycle.ResultsCurrently, the FOUNTAIN platform includes 9 research partner collaborations and 8 CDM-mapped data sources from 7 countries (United States, United Kingdom, China, Japan, The Netherlands, Spain, and Denmark). These databases and research partners were selected after a feasibility fit-for-purpose evaluation. Six multicountry, multidatabase, cohort studies are ongoing to describe patient populations, current standard of care, comorbidity profiles, healthcare resource use, and treatment effectiveness and safety in different patient populations with chronic kidney disease and type 2 diabetes. Strengths and potential limitations of FOUNTAIN are described in the context of valid RWE generation.ConclusionThe establishment of the FOUNTAIN platform has allowed harmonized execution of multiple studies, promoting consistency both within individual studies that employ multiple data sources and across all studies run within the platform’s framework. FOUNTAIN presents a proposal to efficiently improve the consistency and generalizability of RWE on finerenone.

A Robust Human–Machine Framework for Project Portfolio Selection

Based on the project portfolio selection and scheduling problem (PPSS), the development of a systematic and scientific project scheduling plan necessitates comprehensive consideration of individual preferences and multiple realistic constraints, rendering it an NP-hard problem. Simultaneously, accurately and swiftly evaluating the value of projects as a complex entity poses a challenging issue that requires urgent attention. This paper introduces a novel qualitative evaluation-based project value assessment process that significantly reduces the cost and complexity of project value assessment, upon which a preference-based deep reinforcement learning method is presented for computing and solving project subsets and time scheduling plans. This paper first determines the key parameter values of the algorithm through specific examples. Then, using the method of controlling variables, it explores the sensitivity of the algorithm to changes in problem size and dimensionality. Finally, the proposed algorithm is compared with two classical algorithms and two heuristic algorithms across different instances. The experimental results demonstrate that the proposed algorithm exhibits higher effectiveness and accuracy.

Enhancing IT Project Portfolio Management Through Dynamic Dashboard Visualization and Integrated Chatbot and Mathematical ML Model for Strategic Decision Making

This project paper presents an innovative approach to enhance IT Project Portfolio Management (IT PPM) using dynamic dashboard visualization and machine learning (ML) techniques. Effective IT PPM is essential for organizations to align IT projects with business objectives and optimize resource allocation. However, conventional methods often lack innovation and real-time visibility, leading to suboptimal decision-making and costly software investments. The objective of this project is to develop a dynamic dashboard visualization system integrated with a machine learning model derived from the Meta-Portfolio Method (MPM). The MPM leverages advanced analytics and historical project data to dynamically allocate resources and prioritize projects based on risk-return profiles. By incorporating the MPM into the dashboard, organizations gain real-time insights into project status, progress, and resource allocation, enabling informed decision-making and proactive portfolio optimization. The ML model derived from the MPM analyzes historical project data, including success rates, resource intensity, criticality, regulatory compliance, and portfolio diversification, to predict optimal project selection and resource allocation. By dynamically adapting to changing market conditions and project dynamics, the ML model facilitates strategic decision-making and enhances portfolio performance. Through the integration of dynamic dashboard visualization and ML-driven decision support, organizations can streamline IT PPM processes, improve project outcomes, and achieve strategic objectives effectively and efficiently. This innovative approach empowers organizations to navigate the complexities of IT project portfolios with agility, foresight, and confidence.

An integrated machine learning approach for evaluating critical success factors influencing project portfolio management adoption in the construction industry

PurposeIn today’s intricate and dynamic construction sector, traditional project management techniques, which view projects in isolation, are no longer sufficient. Project Portfolio Management (PPM) has proven to be an efficient alternative solution for handling multiple construction projects. As such, based on a Machine Learning (ML) approach, this study aims to explore the Critical Success Factors (CSFs) influencing the adoption of PPM, aiming to enhance PPM implementation in construction projects.Design/methodology/approachA questionnaire based on CSFs gathered from prior studies was developed and validated by experts. Afterward, exploratory data analysis is conducted to understand CSF–PPM relationships. Preprocessing techniques ensure uniformity in variable magnitudes. Lastly, ML techniques, namely Linear Discriminant Analysis (LDA), Logistic Regression (LR) and Extra Trees Classifier (ETC) are developed to model and investigate CSFs' impact on PPM adoption.FindingsThe findings pointed out that the ETC model marginally outperforms other ML models with a classification accuracy of 93%. Also, the project size, utilized PPM tool and resource allocation-related factors are the most significant CSFs that influenced the PPM performance by about 48.5%.Originality/valueThis study contributes to the existing body of knowledge by raising awareness among construction companies and other project stakeholders about the pivotal CSFs that must be considered when prioritizing projects and designing an optimal PPM approach.

A mathematical model for managing national portfolio of basic research projects

A mathematical model for managing national portfolio of basic research projects

Mission-oriented innovation for sustainable polymers in liquid formulation.

Industrial chemical producers and formulators are increasingly conscious of their responsibility in stewarding planetary resources and minimizing harm to the environment. In 2019, the Royal Society of Chemistry (RSC) engaged an industry task force from across the value chain to drive technical research to classify a new class of polymer-polymers in liquid formulation (PLFs). Building on this, the task force called for step change in sustainability practices for PLFs and instigated a design and development process to identify research themes and priorities that could accelerate innovation in this area. However, a key challenge was that as a novel classification, PLFs were largely unknown outside the chemistry community and entirely absent from the mainstream research agenda. To establish the demand-pull requirements of the value chain for sustainable PLFs, the RSC used a 'mission-oriented' innovation framework to enable the taskforce to co-design an ideal-type portfolio of research and innovation projects, and to set out a realistic roadmap for transition. This perspective article presents a summary of the activities carried out by the task force in its pursuit of mission-oriented innovation for PLFs and describes the strategic design method used to enable cross-value chain consensus on action for PLF sustainability, build system-wide innovation ecosystems and explore common-good scenarios. This article is part of the discussion meeting issue 'Green carbon for the chemical industry of the future'.

Methodological Planning to Determine the Technological Expansion of Smart Metering Systems for Utilities

This research uses data analysis and mining techniques to determine the technological expansion of measurement systems in a public service company. It integrates technical, economic, geographic, and social variables into the analysis using machine learning techniques to discover patterns and relationships in large data sets. The fuzzy logic methodology is applied using the MATLAB programming tool “Fuzzy Logic” to build algorithms that allow for the correct selection of measurement, achieving greater efficiency and precision in the assignment of meter types. The results show that 98% of the metering systems in the significant part are electronic meters, with the “Residential BT” rate being the most extensive data set. Implementing the “fuzzy logic” technique recognizes that more than 60% of the meters are electronic, with the registration of active energy, reactive energy, and demand, allowing for greater control over the marketing variables of the distribution system operator. This research suggests that a future restructuring of electrical metering systems benefits the company and its users. By applying the analysis, a portfolio of viable projects for the replacement of measurement systems is obtained, and they are grouped into two clusters based on the total cost of the technological change.

Automata-based Model of Scientific Activity

The automata model of scientific activity is defined as an extension of the automata model of arbitrary activity. The automata model is built hierarchically and consists of a static part in the form of a set of data sections and a dynamic part defining a set of model processes. The structure of arbitrary activity is considered from the perspective of requirements engineering. To describe the structure of objects, a new data type is introduced: tree set. Tree sets of objects and activities are defined in the activity model. Science as a whole has a hierarchical tree structure and is defined as a tree set of scientific disciplines. The objects of the model of scientific activity are a theory and a set of scientific projects for each scientific discipline. The organisation of scientific activity in Russia is still inefficient. As an alternative, an organisation based on a portfolio of scientific projects formed by a team of leaders for each scientific discipline is considered. The team of leaders is created and updated in accordance with the principle of maximum competence. The apparatus of formal methods and requirements engineering is used to build a model of scientific activity. The static part of the model proposed by the author is formalised.

Assessing the impact of taxation on the effective tax rate and operational risk of capital investment projects under optimal capital structures

This paper proposes two metrics to correctly measure under optimal capital structures the impact of corporate statutory tax rates (a) on the effective tax rate, and (b) on the operational risk of capital investment projects and their parent firm's project portfolio. For illustrative and explanatory purposes as well as for ensuring a realistic coverage of a wide spectrum of risky capital investment projects, both metrics are applied to a stationary in mean and variance stochastic NPVτ model under the Canadian open-class corporate taxation system. The Marshallian partial equilibrium adjustment mechanism is brought into play to ensure through efficient financial markets that firms operating within any domestic economic sector are concomitantly subjected to financial equilibrium conditions, thereby enabling firms to converge in the very short run to an optimal after-tax leveraged net cost of capital. This paper concludes that current macroeconomic neo-classical rate-based marginal effective tax rate (METR) metrics systematically and significantly underestimate business organizations' microeconomic NPVτ-based corporate effective tax rates. Furthermore, increasing both the corporate statutory tax rate and the cross-correlation coefficient between portfolio projects will markedly increase a parent firm's project portfolio operational risk while impeding its capacity to reduce it.

Lean project planning – Bridging last planner system and earned value management

Earned Value Management (EVM) has become a de facto standard for project planning after decades of applications worldwide, and most industry contracts that require some of the reporting features of EVM. However, EVM has shortcomings such as too early release of work and no process perspective to facilitate improvements. The Last Planner System (LPS) was developed to overcome the shortcomings of EVM utilizing insights from lean manufacturing. Unfortunately, LPS has not reached the same level of acceptance as EVM. A new approach, Lean Project Planning (LPP), is developed to take advantage of the strengths of both approaches. In this paper, LPS and EVM are compared to each other, and the resulting LPP is presented. The paper discusses a LPP implementation across the project portfolio at a Norwegian pressure vessel fabrication company. The results show that LPP provided accurate Estimate At Completion (EAC) for both costs and delivery times despite taking on many major Variation Orders. The approach is arguably a step forward for turning plans into planning in project-based industries such as shipbuilding and fabrication of pressure vessels. The complexity of pressure vessel fabrication stress-tested the LPP, and some improvements were identified for future work.

Seas the opportunity: multi-criteria decision analysis to identify and prioritise blue carbon wetland restoration sites

IntroductionThe emergence of voluntary carbon markets is creating new opportunities to sustainably finance Natural Climate Solution (NCS) projects. In Australia, the federal government recently enacted the Tidal Restoration of Blue Carbon Ecosystems Methodology Determination 2022 (Tidal Reconnection Method), whereby restoration activities that reintroduce tidal flows to allow the re-establishment of coastal wetland (blue carbon) ecosystems, through the removal or modification of a tidal restriction, can be used to gain and sell Australian carbon credit units. Australia has the highest net blue carbon wealth in the world, with 5%–11% of global carbon stocks, yet there is currently a lack of large-scale feasibility assessments and supporting methodologies to identify and prioritise sites with the greatest potential for NCS project implementation to help inform investment decisions.MethodsIn this study, we applied a spatial Multi-Criteria Decision Analysis (MCDA) to identify, map, and prioritise potential sites for blue carbon coastal wetland restoration in South Australia that meet criteria outlined in the Tidal Reconnection Method. This study compared information on 1) predicted flooding extent following tidal reconnection and under sea level rise (SLR; present-day, 2050 and 2,100); 2) project implementation complexity (e.g. who possesses land tenure); and 3) carbon sequestration potential through predicted area of vegetation change under the above SLR scenarios. ResultsOur results identified 64 sites of interest, of which 32 received an overall “high” prioritisation score of 3 or more out of 5. This equates to approximately 21,114 ha of high priority potential blue carbon restoration sites.DiscussionThe MCDA enables development of a portfolio of viable restoration projects through a rapid “desktop” prioritisation of sites of interest, which can then guide investment in further detailed cost/benefit feasibility assessments. This study demonstrates an adaptable MCDA approach to map potential NCS projects at meaningful spatial scales and in-line with carbon market-based opportunities.

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.

Together, taking the next step: using system dynamics modelling to build community renewable energy programmes in Aotearoa New Zealand

ABSTRACT The Aotearoa New Zealand electricity sector is undergoing a transition to more distributed electricity generation, largely driven by the private sector. Local actors, however, are increasingly interested in playing a role in the energy transition, and addressing their resilience, with community renewable energy (CRE). These projects often have a wide range of impacts, dependent on the community’s motivations, as well as the design and operation of the project. However, there is a lack of tools that can accurately quantify the impacts derived from CRE projects, and guide communities to build a portfolio of projects. This study used system dynamics modelling to analyse the impacts of CRE projects over a 40-year time span. The Energise Ōtaki community group in Aotearoa New Zealand was used as a case study given their already established CRE project and goals to expand their project portfolio. The system dynamics model showed if the community reinvested 80% of the profits from the first project into a second project would provide the greatest long-term benefit for the community in terms of the impact areas important to them. System dynamics modelling proved a useful tool in evaluating the wide range of impacts, and how they interact.

CDC Prioritizes HIV Prevention and Treatment to Reduce HIV Disparities Among Cis-Gender Black Women.

To succeed in ending the HIV epidemic in the United States, the Centers for Disease Control and Prevention (CDC) focuses on delivering combinations of scientifically proven, cost-effective, and scalable interventions to priority populations. Systemic factors continue to contribute to persistent health disparities and disproportionately higher rates of HIV diagnosis in some communities. The National HIV/AIDS Strategy has designated cis-gender Black women (CgBW) as a priority population to address the racial and ethnic inequities in HIV. This report presents the portfolio of projects, programs, and initiatives funded by the CDC's Division of HIV Prevention (DHP) to address disparities in HIV and improve health and QOL among CgBW. These funded activities include the development, planning, and implementation of HIV prevention programs, mass media campaigns, and behavioral interventions focused on CgBW. This report also summarizes DHP's community engagement, capacity building, and partnership efforts, and highlights research and surveillance activities focusing on CgBW. Finally, this report outlines future directions for CDC's efforts to improve access to HIV testing, treatment, and prevention for CgBW in the United States.