This book provides a step-by-step guidance on how to implement analytical methods in project risk management. The text focuses on engineering design and construction projects and as such is suitable for graduate students in engineering, construction, or project management, as well as practitioners aiming to develop, improve, and/or simplify corporate project management processes. The book places emphasis on building data-driven models for additive-incremental risks, where data can be collected on project sites, assembled from queries of corporate databases, and/or generated using procedures for eliciting experts’ judgments. While the presented models are mathematically inspired, they are nothing beyond what an engineering graduate is expected to know: some algebra, a little calculus, a little statistics, and, especially, undergraduate-level understanding of the probability theory. The book is organized in three parts and fourteen chapters. In Part I the authors provide the general introduction to risk and uncertainty analysis applied to engineering construction projects. The basic formulations and the methods for risk assessment used during project planning phase are discussed in Part II, while in Part III the authors present the methods for monitoring and (re)assessment of risks during project execution.
This text covers R program coding for the implementation of two essential data analytics for practical construction problems. The first part of this book explains time series basics, models, and forecasting approaches in the context of the construction industry, accompanied by practical examples in construction. The second part describes the concept of investment valuation for construction projects and provides both deterministic and probabilistic techniques to conduct investment valuation on construction projects. R code scripts are provided in this book for solving practical problems in the construction industry. This book is also equipped with an R Package entitled “cdar” to provide the necessary functions for performing investment valuation. The book maximizes students’ understanding of the necessary theoretical background of data analytics, and explains the implementation of data analytics techniques to solve the actual problems in the construction industry.
A detailed, multi-disciplinary approach to investment analytics Portfolio Construction and Analytics provides an up-to-date understanding of the analytic investment process for students and professionals alike. With complete and detailed coverage of portfolio analytics and modeling methods, this book is unique in its multi-disciplinary approach. Investment analytics involves the input of a variety of areas, and this guide provides the perspective of data management, modeling, software resources, and investment strategy to give you a truly comprehensive understanding of how today's firms approach the process. Real-world examples provide insight into analytics performed with vendor software, and references to analytics performed with open source software will prove useful to both students and practitioners. Portfolio analytics refers to all of the methods used to screen, model, track, and evaluate investments. Big data, regulatory change, and increasing risk is forcing a need for a more coherent approach to all aspects of investment analytics, and this book provides the strong foundation and critical skills you need. Master the fundamental modeling concepts and widely used analytics Learn the latest trends in risk metrics, modeling, and investment strategies Get up to speed on the vendor and open-source software most commonly used Gain a multi-angle perspective on portfolio analytics at today's firms Identifying investment opportunities, keeping portfolios aligned with investment objectives, and monitoring risk and performance are all major functions of an investment firm that relies heavily on analytics output. This reliance will only increase in the face of market changes and increased regulatory pressure, and practitioners need a deep understanding of the latest methods and models used to build a robust investment strategy. Portfolio Construction and Analytics is an invaluable resource for portfolio management in any capacity.
Master the skills necessary to hire and manage a team of highly skilled individuals to design, build, and implement applications and systems based on advanced analytics and AI Key FeaturesLearn to create an operationally effective advanced analytics team in a corporate environmentSelect and undertake projects that have a high probability of success and deliver the improved top and bottom-line resultsUnderstand how to create relationships with executives, senior managers, peers, and subject matter experts that lead to team collaboration, increased funding, and long-term success for you and your teamBook Description In Building Analytics Teams, John K. Thompson, with his 30+ years of experience and expertise, illustrates the fundamental concepts of building and managing a high-performance analytics team, including what to do, who to hire, projects to undertake, and what to avoid in the journey of building an analytically sound team. The core processes in creating an effective analytics team and the importance of the business decision-making life cycle are explored to help achieve initial and sustainable success. The book demonstrates the various traits of a successful and high-performing analytics team and then delineates the path to achieve this with insights on the mindset, advanced analytics models, and predictions based on data analytics. It also emphasizes the significance of the macro and micro processes required to evolve in response to rapidly changing business needs. The book dives into the methods and practices of managing, developing, and leading an analytics team. Once you've brought the team up to speed, the book explains how to govern executive expectations and select winning projects. By the end of this book, you will have acquired the knowledge to create an effective business analytics team and develop a production environment that delivers ongoing operational improvements for your organization. What you will learnAvoid organizational and technological pitfalls of moving from a defined project to a production environmentEnable team members to focus on higher-value work and tasksBuild Advanced Analytics and Artificial Intelligence (AA&AI) functions in an organizationOutsource certain projects to competent and capable third partiesSupport the operational areas that intend to invest in business intelligence, descriptive statistics, and small-scale predictive analyticsAnalyze the operational area, the processes, the data, and the organizational resistanceWho this book is for This book is for senior executives, senior and junior managers, and those who are working as part of a team that is accountable for designing, building, delivering and ensuring business success through advanced analytics and artificial intelligence systems and applications. At least 5 to 10 years of experience in driving your organization to a higher level of efficiency will be helpful.
Development Research in Practice leads the reader through a complete empirical research project, providing links to continuously updated resources on the DIME Wiki as well as illustrative examples from the Demand for Safe Spaces study. The handbook is intended to train users of development data how to handle data effectively, efficiently, and ethically. “In the DIME Analytics Data Handbook, the DIME team has produced an extraordinary public good: a detailed, comprehensive, yet easy-to-read manual for how to manage a data-oriented research project from beginning to end. It offers everything from big-picture guidance on the determinants of high-quality empirical research, to specific practical guidance on how to implement specific workflows—and includes computer code! I think it will prove durably useful to a broad range of researchers in international development and beyond, and I learned new practices that I plan on adopting in my own research group.†? —Marshall Burke, Associate Professor, Department of Earth System Science, and Deputy Director, Center on Food Security and the Environment, Stanford University “Data are the essential ingredient in any research or evaluation project, yet there has been too little attention to standardized practices to ensure high-quality data collection, handling, documentation, and exchange. Development Research in Practice: The DIME Analytics Data Handbook seeks to fill that gap with practical guidance and tools, grounded in ethics and efficiency, for data management at every stage in a research project. This excellent resource sets a new standard for the field and is an essential reference for all empirical researchers.†? —Ruth E. Levine, PhD, CEO, IDinsight “Development Research in Practice: The DIME Analytics Data Handbook is an important resource and a must-read for all development economists, empirical social scientists, and public policy analysts. Based on decades of pioneering work at the World Bank on data collection, measurement, and analysis, the handbook provides valuable tools to allow research teams to more efficiently and transparently manage their work flows—yielding more credible analytical conclusions as a result.†? —Edward Miguel, Oxfam Professor in Environmental and Resource Economics and Faculty Director of the Center for Effective Global Action, University of California, Berkeley “The DIME Analytics Data Handbook is a must-read for any data-driven researcher looking to create credible research outcomes and policy advice. By meticulously describing detailed steps, from project planning via ethical and responsible code and data practices to the publication of research papers and associated replication packages, the DIME handbook makes the complexities of transparent and credible research easier.†? —Lars Vilhuber, Data Editor, American Economic Association, and Executive Director, Labor Dynamics Institute, Cornell University
A detailed, multi-disciplinary approach to investment analytics Portfolio Construction and Analytics provides an up-to-date understanding of the analytic investment process for students and professionals alike. With complete and detailed coverage of portfolio analytics and modeling methods, this book is unique in its multi-disciplinary approach. Investment analytics involves the input of a variety of areas, and this guide provides the perspective of data management, modeling, software resources, and investment strategy to give you a truly comprehensive understanding of how today's firms approach the process. Real-world examples provide insight into analytics performed with vendor software, and references to analytics performed with open source software will prove useful to both students and practitioners. Portfolio analytics refers to all of the methods used to screen, model, track, and evaluate investments. Big data, regulatory change, and increasing risk is forcing a need for a more coherent approach to all aspects of investment analytics, and this book provides the strong foundation and critical skills you need. Master the fundamental modeling concepts and widely used analytics Learn the latest trends in risk metrics, modeling, and investment strategies Get up to speed on the vendor and open-source software most commonly used Gain a multi-angle perspective on portfolio analytics at today's firms Identifying investment opportunities, keeping portfolios aligned with investment objectives, and monitoring risk and performance are all major functions of an investment firm that relies heavily on analytics output. This reliance will only increase in the face of market changes and increased regulatory pressure, and practitioners need a deep understanding of the latest methods and models used to build a robust investment strategy. Portfolio Construction and Analytics is an invaluable resource for portfolio management in any capacity.
Investment in any new project invariably carries risk but the construction industry is subject to more risk and uncertainty than perhaps any other industry. This guide for construction managers, project managers and quantity surveyors as well as for students shows how the risk management process improves decision-making. Managing Risk in Construction Projects offers practical guidance on identifying, assessing and managing risk and provides a sound basis for effective decision-making in conditions of uncertainty. The book focuses on theoretical aspects of risk management but also clarifies procedures for undertaking and utilising decisions. This blend of theory and practice is the real message of the book and, with a strong authorship team of practitioners and leading academics, the book provides an authoritative guide for practitioners having to manage real projects. It discusses a number of general concepts, including projects, project phases, and risk attitude before introducing various risk management techniques. This third edition has been extended to recognize the reality of multi-project or programme management and the risks in this context; to highlight the particular problems of risk in international joint ventures; and to provide more coverage of PFI and PPP. With case studies and examples of good practice, the book offers the distilled knowledge of over 100 man-years of experience in working on all aspects of project risk, giving sound practical guidance on identifying, assessing and managing risk.
Modelled on the concept of Industry 4.0, the idea of Construction 4.0 is based on a confluence of trends and technologies that promise to reshape the way built environment assets are designed, constructed, and operated. With the pervasive use of Building Information Modelling (BIM), lean principles, digital technologies, and offsite construction, the industry is at the cusp of this transformation. The critical challenge is the fragmented state of teaching, research, and professional practice in the built environment sector. This handbook aims to overcome this fragmentation by describing Construction 4.0 in the context of its current state, emerging trends and technologies, and the people and process issues that surround the coming transformation. Construction 4.0 is a framework that is a confluence and convergence of the following broad themes discussed in this book: Industrial production (prefabrication, 3D printing and assembly, offsite manufacture) Cyber-physical systems (actuators, sensors, IoT, robots, cobots, drones) Digital and computing technologies (BIM, video and laser scanning, AI and cloud computing, big data and data analytics, reality capture, Blockchain, simulation, augmented reality, data standards and interoperability, and vertical and horizontal integration) The aim of this handbook is to describe the Construction 4.0 framework and consequently highlight the resultant processes and practices that allow us to plan, design, deliver, and operate built environment assets more effectively and efficiently by focusing on the physical-to-digital transformation and then digital-to-physical transformation. This book is essential reading for all built environment and AEC stakeholders who need to get to grips with the technological transformations currently shaping their industry, research, and teaching.
Within the overarching theme of “Managing the Digital Transformation of Construction Industry” the 23rd International Conference on Construction Applications of Virtual Reality (CONVR 2023) presented 123 high-quality contributions on the topics of: Virtual and Augmented Reality (VR/AR), Building Information Modeling (BIM), Simulation and Automation, Computer Vision, Data Science, Artificial Intelligence, Linked Data, Semantic Web, Blockchain, Digital Twins, Health & Safety and Construction site management, Green buildings, Occupant-centric design and operation, Internet of Everything. The editors trust that this publication can stimulate and inspire academics, scholars and industry experts in the field, driving innovation, growth and global collaboration among researchers and stakeholders.
With the widespread availability of high-speed, high-capacity microprocessors and microcomputers with high-speed communication ability, and sophisticated energy analytics software, the technology to support deployment of automated diagnostics is now available, and the opportunity to apply automated fault detection and diagnostics to every system and piece of equipment in a facility, as well as for whole buildings, is imminent. The purpose of this book is to share information with a broad audience on the state of automated fault detection and diagnostics for buildings applications, the benefits of those applications, emerging diagnostic technology, examples of field deployments, the relationship to codes and standards, automated diagnostic tools presently available, guidance on how to use automated diagnostics, and related issues.
