Search Results (126)

The construction sector is under growing pressure to transition from linear, resource-intensive models to regenerative, circular practices. While Circular Economy (CE), Building Information Modelling (BIM), and Agile Project Management (APM) are each recognized for their potential to improve sustainability, their combined application in construction remains underexplored, particularly among small- and medium-sized enterprises (SMEs). In this study, we propose a conceptual framework integrating CE as a strategic objective, APM as the procedural methodology, and BIM as the digital enabler to foster circular practices in construction. Unlike previous studies, this research empirically integrates CE, BIM, and APM into a single coherent framework tailored specifically for SMEs. The framework is informed by secondary analysis of the BLOOM project dataset (n = 153) and a targeted readiness survey (n = 98) conducted among SMEs in the Mediterranean and Central European regions. The findings reveal a significant gap between awareness and implementation: while over 75% of respondents are familiar with CE and 63% use BIM tools, only 19% demonstrate readiness to integrate all three approaches. The main barriers—training gaps, regulatory ambiguity, and digital immaturity—are explored in detail. This study contributes by introducing a five-pillar framework and by identifying and analysing specific barriers that SMEs face when integrating CE–APM–BIM practices. Nevertheless, strong conceptual alignment exists, with over 80% agreeing on the potential of CE–Agile–BIM synergy. This study offers actionable insights into overcoming adoption barriers and emphasizes the need for policy-driven pilot projects, peer learning, and tailored capacity building to foster regenerative construction practices. Full article

Despite widespread adoption, traditional Agile project management practices often fail to ensure successful delivery of enterprise-scale software projects. One key limitation lies in the absence of a conceptually defined structure for the various types of Agile activities and their interactions. As a result, Agile methodologies typically lack formal indicators for evaluating the semantic content and progress status of project activities. Although widely used tools for Agile project management, such as Atlassian Jira, capture operational data, project status assessment interpretation remains largely subjective—relying on the experience and judgment of managers and team members rather than on a formal knowledge model or well-defined semantic attributes. As Agile project activities continue to grow in complexity, there is a pressing need for a modeling approach that captures their causal structure in order to describe the essential characteristics of the processes and ensure systematic monitoring and evaluation of the project. The complexity of the corresponding model must correlate with the causality of processes to avoid losing essential properties and to reveal the content of causal interactions. To address these gaps, this paper introduces a causal Agile process model that formalizes the internal structure and transformation pathways of Agile activity types. To our knowledge, it is the first framework to integrate a recursive, causally grounded structure into Agile management, enabling both semantic clarity and quantitative evaluation of project complexity and progress. The aim of the article is, first, to describe conceptually different Agile activity types from a causal modeling perspective, its internal structure and information transformations, and, second, to formally define the causal Agile management model and its characteristics. Each Agile activity type (e.g., theme, initiative, epic, user story) is modeled using the management transaction (MT) framework—an internal model of activity that comprises a closed-loop causal relationship among management function (F), process (P), state attribute (A), and control (V) informational flows. Using this framework, the internal structure of Agile activity types is normalized and the different roles of activities in internal MT interactions are defined. An important feature of this model is its recursive structure, formed through a hierarchy of MTs. Additionally, the paper presents classifications of vertical and horizontal causal interactions, uncovering theoretically grounded patterns of information exchange among Agile activities. These classifications support the derivation of quantitative indicators for assessing project complexity and progress at a given point in time, offering insights into activity specification completeness at hierarchical levels and overall project content completeness. Examples of complexity indicator calculations applied to real-world enterprise application system (EAS) projects are included. Finally, the paper describes enhancements to the Jira tool, including a causal Agile management repository and a prototype user interface. An experimental case study involving four Nordic EAS projects (using Scrum at the team level and SAFe at the program level) demonstrates that the Jira tool, when supplemented with causal analysis, can reveal missing links between themes and initiatives and align interdependencies between teams in real time. The causal Agile approach reduced the total number of requirements by an average of 13% and the number of change requests by 14%, indicating a significant improvement in project coordination and quality. Full article

Digital transformation (DT) requires organizations to navigate complex technological and organizational changes, often under conditions of uncertainty. While agile methodologies are widely adopted to address the iterative and cross-functional nature of DT, limited attention has been paid to identifying critical success factors (CSFs) from a socio-technical systems (STS) perspective. This study addresses that gap by integrating and prioritizing CSFs as interdependent elements within a layered socio-technical framework. Drawing on a systematic review of 17 empirical and conceptual studies, we adapt Chow and Cao’s agile success model and validate a set of 14 CSFs across five domains—organizational, people, process, technical, and project—through a Delphi-informed Analytic Hierarchy Process (AHP). The findings reveal that organizational and people-related enablers, particularly management commitment, team capability, and organizational environment, carry the greatest weight in agile-based DT contexts. These results inform a three-layered framework—comprising organizational readiness, agile delivery, and project artefacts—which reflects how social, technical, and procedural factors interact systemically. The study contributes both theoretically, by operationalizing STS theory in the agile DT domain, and practically, by providing a prioritized CSF model to guide strategic planning and resource allocation in transformation initiatives. Full article

Core problems in agile software project scheduling, such as resource-constrained balancing and iteration cycle optimization, embody the pursuit of symmetry. Simultaneously, optimization algorithms find extensive applications in symmetry problems, for example, in graphs and pattern recognition. Considering the cooperation among multiple teams and environmental changes in complex agile software development, a dynamic periodic scheduling model for multi-team agile software project is constructed, which includes three tightly coupled sub-problems, namely user story selection, user story-development team allocation, and task-employee allocation. To solve the model, a group learning particle swarm optimization algorithm is proposed, which includes three novel strategies. First, the population is divided into four groups based on dual indicators of objective values and potential values. Second, different learning objects are selected according to the characteristic of each group so that the search diversity can be improved. Third, to react to the environmental changes and enhance the mining ability, heuristic population initialization and local search strategies are designed by utilizing the problem-specific information. Systematic experimental results on 13 instances indicate that compared with the state-of-the-art algorithms, the proposed algorithm is able to provide a schedule with better precision for the project manager in each sprint of the agile development. Full article

As organizations become increasingly projectified, safeguarding the resilience of project professionals and teams emerges as a critical organizational challenge. Adopting a systems lens, we investigate how agile mindsets and agile practices function as systemic antecedents of resilience at the individual and team levels. Eleven semi-structured interviews with experienced project managers, product owners, and team members from diverse industries were analyzed through inductive thematic coding and system mapping. The findings show that mindset supplies psychological resources—self-efficacy, openness and a learning orientation—while practices such as team autonomy, iterative delivery and transparent communication provide structural routines; together they trigger five interlocking mechanisms: empowerment, fast responsiveness, holistic team dynamics, stakeholder-ecosystem engagement and continuous learning. These mechanisms reinforce one another in feedback loops that boost a project system’s adaptive capacity under volatility. The synergy of mindset and practices is especially valuable in hybrid or traditionally governed projects, where cognitive agility offsets structural rigidity. This study offers the first multi-level, systems-based explanation of agile antecedents of resilience and delivers actionable levers for executives, transformation leaders, project professionals, and HR specialists aiming to sustain talent performance in turbulent contexts. Full article

This study explores the application potential of a lean–Agile hybrid method in complex construction project management. By integrating Scrum iterative development, the Last Planner System, and a BIM collaboration platform, a dual-engine model is established to optimize the dynamic priority mechanism (MoSCoW 2.0) and interface conflict entropy algorithm (ICE model). Through a combination of theoretical and practical approaches, the study elucidates the implementation pathway of this hybrid method and evaluates its benefits in enhancing project efficiency, reducing waste, and accelerating digital transformation. The study provides a replicable management framework for the construction industry and proposes a blockchain-based decentralized knowledge management framework based on blockchain technology. Full article

One of the fundamental challenges in managing software and information technology projects is monitoring and predicting project status at the end of each sprint, release or project. Agile project management has emerged over the past two decades, significantly impacting project success. However, no comprehensive approach based on the features of this approach has been found in studies to monitor and predict the status of a sprint, release or project. This study aims to develop a data-driven approach for predicting the status of software projects based on agility features. For this purpose, 22 agility features were first identified to evaluate and predict the status of projects in four aspects: Endurance, Effectiveness, Efficiency, and Complexity. The findings indicate that the aspects of Effectiveness and Efficiency have the greatest impact on project success. Additionally, the results show that features related to team work, team capacity, experience and project objectives have the most significant impact on project success. An artificial neural network algorithm was then used, and a model was developed to predict project status, which was optimized using the Neural Architecture Search algorithm with a 93 percent accuracy rate. The neural network model was interpreted using the SHapley Additive exPlanations (SHAP) algorithm, and sensitivity analysis was performed on the important components. Finally, the behavior of the projects in each category was analyzed and evaluated using the Apriori algorithm. Full article

The needs of modern businesses are constantly evolving, and project management must continuously adapt by proposing new solutions to emerging challenges. Building on previous research—a systematic literature review (SLR) identifying international best practices and a questionnaire survey capturing insights from the Moroccan context—this study aims to enhance the practices of Moroccan project management. By integrating global methodologies from the SLR with local business realities from the survey, this research lays the foundation for improving project management practices in Moroccan enterprises. Rather than presenting a finalized framework, this study explores key project management methodologies, with a particular focus on Lean and Agile principles, and evaluates their relevance to the local context. The findings highlight the benefits of combining these approaches to enhance efficiency, adaptability, and overall project performance. This paper thus introduces the initial foundation for the subsequent development of a comprehensive hybrid framework aligned with both global standards and local realities. Full article

This study investigates the prevalence and impact of Agile practices by leveraging metadata from thousands of public GitHub repositories through a novel data-driven methodology. To facilitate this analysis, we developed the AgileScore index, a metric designed to identify and evaluate patterns, characteristics, performance and community engagement in Agile-oriented projects. This approach enables comprehensive, large-scale comparisons between Agile methodologies and traditional development practices within digital environments. Our findings reveal a significant annual growth of 16% in the adoption of Agile practices and validate the AgileScore index as a systematic tool for assessing Agile methodologies across diverse development contexts. Furthermore, this study introduces innovative analytical tools for researchers in software project management, software engineering and related fields, providing a foundation for future work in areas such as cost estimation and hybrid project management. These insights contribute to a deeper understanding of Agile’s role in fostering collaboration and adaptability in dynamic digital ecosystems. Full article

The design phase is critical in construction projects, as it directly impacts cost, quality, and execution efficiency. However, it suffers from structural deficiencies in communication, coordination, and early problem detection, leading to delays, cost overruns, and inefficiencies. While Lean Construction has been widely applied in execution phases, its adoption in design remains fragmented, lacking a clear framework for identifying and evaluating Lean tools in this context. This study aims to identify, classify, and evaluate Lean tools applicable to the building design phase, emphasizing their functionalities, benefits, and limitations. A systematic literature review and expert validation process led to the identification of 16 Lean tools and 26 design-related functionalities. Among these tools, Building Information Modeling (BIM), Last Planner System (LPS), and Agile Design Management (ADM) were identified as the most impactful, collectively addressing 88% of design functionalities. Expert insights revealed that ADM improves task control and decision-making clarity, LPS reduces uncertainty and enhances workflow reliability, and BIM strengthens coordination and early conflict detection. This study provides a structured perspective on Lean tool integration during design, highlighting their benefits and limitations and offering guidance for their implementation. The findings contribute to improving design efficiency, minimizing waste, and fostering collaboration in construction projects. Full article

Common practice in agile delivery planning is based on user requirements-related artifacts. However, an aspect of business process alignment to software product functions comes into focus in the phase of inception of enterprise-aware disciplined agile software projects. This research proposes a method for mapping business process model elements to sets of semantically coupled and prioritized software functions to obtain ordered software product backlog, i.e., work items list. These software functions are derived from primitive business processes and software functional patterns. The mapping table enables assignment of primitive business processes to categorized software functions. Derived and prioritized software functions are related to a work item list pattern according to selected technology implementation. This way, a prioritized work items list is formulated, which enables development iteration planning. This method could be useful in software functional design alternatives comparison, change management, multi-project integration of software modules to support business processes in information systems, etc. Feasibility of the proposed method has been demonstrated with a case study, related to the development of a billing and reporting software utilized in a private hospital. This case study shows usability of the proposed method in the case of two related development projects that enable software functionality enhancement. Full article

This paper explores the transition from traditional Project Management Offices (PMOs) to Value Delivery Offices (VDOs), marking a shift from a process-centric to a value-centric approach in project management. Utilizing a mixed-methods research approach, the study combines quantitative data analysis with qualitative interviews and case studies from various industries. This study provides clear insights into the benefits and strategic impacts of adopting VDOs. Key findings include the pivotal role of change management in facilitating this transition and the introduction of the Value Delivery Maturity Model (VDMM) to assess value delivery progression. The paper discusses practical implications for organizations, recommends strategies for effective implementation and management of VDOs, and identifies potential challenges and solutions in the transition process. This research contributes to the evolving field of project management by highlighting the significance of value-centric approaches in contemporary business environments. Full article

Agile software project management (ASPM) serves modern industries to conduct iterative development of complicated code bases. The decision-making process in Agile environments regularly depends on individual opinions, creating ineffective results for risk management and resource distribution. Artificial intelligence (AI) is a promising approach for handling these challenges by delivering data-based choices to project management. This research introduces an AI-based decision support system for improving risk reduction and resource distribution in ASPM. The system merges optimization frameworks and predictive analytics to enhance operational decision efficiency. The machine learning solution anchors data evaluation using AI models that simultaneously predict risks and strengthen decision power for resource scheduling. This analysis relied on project records and recent operational data to perform model validation and training procedures. Tests determined how the framework performed against contemporary Agile project management systems by measuring the completion speed of sprints, resource management practices, and risk prediction accuracy. The framework demonstrated better performance by predicting risks and simultaneously maximizing resources utilized during projects. The proposed framework outperformed traditional Agile applications, achieving 94% accuracy in risk identification and enhancing workload management by 25%, leading to an 18% improvement in sprint completion rates and overall project efficiency. These findings confirm that AI-driven decision support systems (DSSs) are crucial in enhancing Agile project management by enabling proactive risk mitigation and optimized resource allocation. By integrating AI-powered decision-making, the framework empowers organizations to improve project outcomes, streamline resource management, and facilitate the adoption of AI-driven methodologies within Agile systems. Full article

Model-based systems engineering (MBSE) offers significant advantages over traditional document-based approaches, particularly in improving the clarity, traceability, and efficiency of requirements engineering (RE). However, MBSE also introduces challenges, particularly in maintaining consistent semantics and handling evolving system models. This paper presents ARMADE, an agile requirements management and definition environment developed at DLR, which aims to address these challenges. ARMADE enables the flexible, user-friendly modeling of system requirements using a data model that incorporates natural language patterns. The tool supports the dynamic adaptation of metamodels and facilitates collaborative, project-wide requirements management. A case study based on two systems of systems (SoS) from the EU-funded HE COLOSSUS project—firefighting and advanced aerial mobility (AAM)—demonstrates ARMADE’s ability to manage complex, interdisciplinary requirements. The study highlights the tool’s potential to reduce data inconsistencies, improve adaptability, and enhance the overall efficiency of the RE process. By enabling seamless updates and changes to requirements, ARMADE shows promise as a versatile solution for dynamic metamodeling in complex systems, with potential applications extending beyond aeronautics to various industries reliant on intricate requirements management. Full article

The MAISTRO methodology introduces a comprehensive and integrative, agile framework for managing Artificial Intelligence (AI) system development projects, addressing familiar challenges such as technical complexity, multidisciplinary collaboration, and ethical considerations. Designed to align technological capabilities with business objectives, MAISTRO integrates iterative practices and governance frameworks to enhance efficiency, transparency, and adaptability throughout the AI lifecycle. This methodology encompasses seven key phases, from business needs understanding to operation, ensuring continuous improvement and alignment with strategic goals. A comparative analysis highlights MAISTRO’s advantages over traditional methodologies such as CRISP-DM and OSEMN, particularly in flexibility, governance, and ethical alignment. This study applies MAISTRO in a simulated case study of the PreçoBomAquiSim supermarket, demonstrating its effectiveness in developing an AI-powered recommendation system. Results include a 20% increase in product sales and a 15% rise in average customer ticket size, highlighting the methodology’s ability to deliver measurable business value. By emphasizing iterative development, data quality, ethical governance, change and risk management, MAISTRO provides a robust approach for AI projects and suggests directions for future research across diverse industries context for facilitating large-scale adoption. Full article