A Multidimensional and Visual Exploration Approach to Project Prioritization and Selection

In project management, many decisions are made based on multiple attributes (dimensions) of project data. However, these dimensions are usually condensed into one or two indicators in the analysis process. For example, projects are commonly prioritized using a scoring approach: they are evaluated according to predefined categories, which are then aggregated into one or two priority numbers. We argue that aggregated scores may only offer a limited view of project importance. Such scores tend to hide information that may effectively distinguish projects; this often leads decision makers to ignore the possible differences masked by aggregation. This paper presents a visual exploration approach that integrates human intuition and maintains the multidimensionality of project data as a decision basis for project prioritization and selection. The approach is based on the examination of portfolio perceptual maps, generated by a clustering technique. The research provides a useful and complementary approach for decision makers to analyze project portfolios

A Multidimensional Perceptual Map Approach to Project Prioritization and Selection

When prioritizing projects, managers usually have to evaluate multiple attributes (dimensions) of project data. However, these dimensions are usually condensed into one or two indicators in many existing analysis processes. For example, projects are commonly prioritized using a scoring approach: they are evaluated according to predefined categories, which are then aggregated into one or two priority numbers. We argue that aggregated scores may only offer a limited view of project importance. This often leads decision makers to ignore the possible differences masked by the aggregation. Following the design science research paradigm, this paper presents a visual exploration approach based on multi-dimensional perceptual maps. It incorporates human intuition in the process and maintains the multidimensionality of project data as a decision basis for project prioritization and selection. A prototype system based on the approach was developed and qualitatively evaluated by a group of project managers. A qualitative analysis of the data collected shows its utility and usability

A Multidimensional and Visual Exploration Approach to Project Portfolio Management

Managing projects in an organization, especially a project-oriented organization, is a challenging task. Project data has a large volume and is complex to manage. It is different from managing a single project, because one needs to integrate and synthesize information from multiple projects and multiple perspectives for high-level strategic business decisions, such as aligning projects with business objectives, balancing investment and expected return, and allocating resources. Current methods and tools either do not well integrate multiple aspects or are not intuitive and easy to use for managers and executives. In this dissertation project, a multidimensional and visual exploration approach was designed and evaluated to provide a unique and intuitive option to support decision making in project portfolio management. The research followed a general design science research methodology involving phases of awareness of problem, suggestion, development, evaluation and conclusion. The approach was implemented into a software system using a prototyping method and was evaluated through user interviews. The evaluation result demonstrates the utility and ease-of-use of the approach, and confirms design objectives. The research brings a new perspective and provides a new decision support tool for project portfolio management. It also contributes to the design knowledge of visual exploration systems for business portfolio management by theorizing the system

Project portfolio management: The linchpin in strategy processes

© Cambridge University Press 2017. Introduction Project portfolio management (PPM) is a central component of organizational project management (OPM), especially through its role in both the formulation and the delivery of organizational strategy. Corporate activities are increasingly carried out in the form of projects, in a trend that has been called “projectification” (Midler, 1995). In particular, for the implementation of complex innovations, it is not enough for organizations to focus on the successful management of individual innovation projects; they must also manage a large number of interdependent projects from a portfolio perspective. In today's dynamic environment, organizing by projects has become the rule rather than the exception, and organizations face challenges in managing these large project landscapes (programs and portfolios). The management of project portfolios is closely linked to the implementation of strategies. As strategies are ultimately implemented by projects, PPM – as a link between corporate strategy and projects – plays a central role (Meskendahl, 2010). In most research and practice this role is considered from a top-down perspective: strategies are considered to be a given yardstick for the prioritization and selection of projects and the allocation of resources. From such perspectives, PPM acts as the recipient of strategic goals and requirements that need only to be operationalized. However, the strategic management literature has long recognized the importance of emergent strategy; and that the realized strategy (the strategy that is actually implemented) often strays from the intended strategy (Mintzberg, 1978). Surprisingly, this is hardly considered in existing research models and standards for PPM (PMI, 2013). While there is some empirical evidence to suggest that hierarchical, formal, top-down approaches are not the actual practice of PPM (Christiansen & Varnes, 2009; Jerbrant & Gustavsson, 2013; Martinsuo, 2013), a much broader debate is needed to fully explore the role of PPM in the context of emergent strategies. The goal of this chapter is therefore to explore the role of PPM in the relationship between the formulation and implementation of strategy and consider both the top-down approach as well as the bottom-up strategy emergence. We first discuss emergence in the context of strategy implementation and the role of different phases in the PPM process that affect strategy implementation

Environmental representativity in marine protected area networks over large and partly unexplored seascapes

Converting assemblages of marine protected areas (MPAs) into functional MPA networks requires political will, multidisciplinary information, coordinated action and time. We developed a new framework to assist planning environmental representativity in a network across the marine space of Portugal, responding to a political commitment to protect 14% of its area by 2020. An aggregate conservation value was estimated for each of the 27 habitats identified, from intertidal waters to the deep sea. This value was based on expert-judgment scoring for environmental properties and features relevant for conservation, chosen to reflect the strategic objectives of the network, thus providing an objective link between conservation commitments and habitat representativity in space. Additionally, habitats' vulnerability to existing anthropogenic pressures and sensitivity to climate change were also scored. The area coverage of each habitat in Portugal and within existing MPAs (regionally and nationally) was assigned to a scale of five orders of magnitude (from 10%) to assess rarity and existing representation. Aggregate conservation value per habitat was negatively correlated with area coverage, positively correlated with vulnerability and was not correlated with sensitivity. The proposed framework offers a multi-dimensional support tool for MPA network development, in particular regarding the prioritization of new habitats to protect, when the goal is to achieve specific targets while ensuring representativity across large areas and complex habitat mosaics. It requires less information and computation effort in comparison to more quantitative approaches, while still providing an objective instrument to scrutinize progress on the implementation of politically set conservation targets.Agência financiadora Número do subsídio Oceanic Observatory of Madeira M1420-01-0145-FEDER-000001-OOM national funds through FCT UID/BIA/00329/2013 UID/Multi/04326/2013 Fundacao para a Ciencia e a Tecnologia SFRH/BPD/95334/2013 CESAM - FCT/MEC through national funds UID/AMB/50017 - POCI-01-0145-FEDER-007638 FEDER FCT SFRH/BPD/94320/2013 MARE - UID/MAR/04292/2019 EU through the Cohesion Fund POSEUR-03-2215-FC-000046 POSEUR-03-2215-FC-000047 FCT national funds ECO/28687/2017info:eu-repo/semantics/publishedVersio

A roadmap to integrate astrocytes into Systems Neuroscience.

Systems neuroscience is still mainly a neuronal field, despite the plethora of evidence supporting the fact that astrocytes modulate local neural circuits, networks, and complex behaviors. In this article, we sought to identify which types of studies are necessary to establish whether astrocytes, beyond their well-documented homeostatic and metabolic functions, perform computations implementing mathematical algorithms that sub-serve coding and higher-brain functions. First, we reviewed Systems-like studies that include astrocytes in order to identify computational operations that these cells may perform, using Ca2+ transients as their encoding language. The analysis suggests that astrocytes may carry out canonical computations in a time scale of subseconds to seconds in sensory processing, neuromodulation, brain state, memory formation, fear, and complex homeostatic reflexes. Next, we propose a list of actions to gain insight into the outstanding question of which variables are encoded by such computations. The application of statistical analyses based on machine learning, such as dimensionality reduction and decoding in the context of complex behaviors, combined with connectomics of astrocyte-neuronal circuits, is, in our view, fundamental undertakings. We also discuss technical and analytical approaches to study neuronal and astrocytic populations simultaneously, and the inclusion of astrocytes in advanced modeling of neural circuits, as well as in theories currently under exploration such as predictive coding and energy-efficient coding. Clarifying the relationship between astrocytic Ca2+ and brain coding may represent a leap forward toward novel approaches in the study of astrocytes in health and disease

Many-objectives optimization: a machine learning approach for reducing the number of objectives

Solving real-world multi-objective optimization problems using Multi-Objective Optimization Algorithms becomes difficult when the number of objectives is high since the types of algorithms generally used to solve these problems are based on the concept of non-dominance, which ceases to work as the number of objectives grows. This problem is known as the curse of dimensionality. Simultaneously, the existence of many objectives, a characteristic of practical optimization problems, makes choosing a solution to the problem very difficult. Different approaches are being used in the literature to reduce the number of objectives required for optimization. This work aims to propose a machine learning methodology, designated by FS-OPA, to tackle this problem. The proposed methodology was assessed using DTLZ benchmarks problems suggested in the literature and compared with similar algorithms, showing a good performance. In the end, the methodology was applied to a difficult real problem in polymer processing, showing its effectiveness. The algorithm proposed has some advantages when compared with a similar algorithm in the literature based on machine learning (NL-MVU-PCA), namely, the possibility for establishing variable–variable and objective–variable relations (not only objective–objective), and the elimination of the need to define/chose a kernel neither to optimize algorithm parameters. The collaboration with the DM(s) allows for the obtainment of explainable solutions.This research was funded by POR Norte under the PhD Grant PRT/BD/152192/2021. The authors also acknowledge the funding by FEDER funds through the COMPETE 2020 Programme and National Funds through FCT (Portuguese Foundation for Science and Technology) under the projects UIDB/05256/2020, and UIDP/05256/2020, the Center for Mathematical Sciences Applied to Industry (CeMEAI) and the support from the São Paulo Research Foundation (FAPESP grant No 2013/07375-0, the Center for Artificial Intelligence (C4AI-USP), the support from the São Paulo Research Foundation (FAPESP grant No 2019/07665-4) and the IBM Corporation

Information Technology Project Prioritization

This thesis provides a contemporary review of several topics related to information technology project prioritization, which will help managers create their own custom methodology. Traditional prioritization tools such as weighted average scoring models are used for simultaneous comparison of a number of proposed projects on multiple dimensions, to facilitate alignment with organization goals. These methods are used for the analysis of information related to the weight preferences over criteria used. If used correctly with this procedure, it is possible to bring forward an authentic figure of merit, which is used as the projects strategic potential. This allows the projects to be ranked and the highest-ranking projects to be considered for selection. Visual tools can then be used for selection of optimum project portfolio. The literature dedicates less time on tools beyond the selection of projects. This study aims to bridge this gap by proposing a final phase of project prioritization as Project Portfolio Management