Managing The Continuum Certainty Uncertainty Unpredictability In Large Engineering Projects

The brief will describe how to develop a risk analysis applied to a project , through a sequence of steps: risk management planning, risk identification, risk classification, risk assessment, risk quantification, risk response planning, risk monitoring and control, process close out and lessons learning. The project risk analysis and management process will be applied to large engineering projects, in particular related to the oil and gas industry. The brief will address the overall range of possible events affecting the project moving from certainty (project issues) through uncertainty (project risks) to unpredictability (unforeseeable events), considering both negative and positive events. Some quantitative techniques (simulation, event tree, Bayesian inference, etc.) will be used to develop risk quantification. The brief addresses a typical subject in the area of project management, with reference to large engineering projects concerning the realization of large plants and infrastructures. These projects are characterized by a high level of change, uncertainty, complexity and ambiguity. The brief represents an extension of the material developed for the course Project Risk Analysis and Management of the Master in Strategic Project Management (Erasmus Mundus) developed jointly by Politecnico di Milano, Heriot Watt University (Edimburgh) and Umea (Sweden). The brief may be used both in courses addressing project management subjects and by practitioners as a guide for developing an effective project risk management plan.

Organizations of all types are consistently working on new initiatives, product lines, or implementation of new workflows as a way to remain competitive in the modern business environment. No matter the type of project at hand, employing the best methods for effective execution and timely completion of the task at hand is essential to project success. Project Management: Concepts, Methodologies, Tools, and Applications presents the latest research and practical solutions for managing every stage of the project lifecycle. Emphasizing emerging concepts, real-world examples, and authoritative research on managing project workflows and measuring project success in both private and public sectors, this multi-volume reference work is a critical addition to academic, government, and corporate libraries. It is designed for use by project coordinators and managers, business executives, researchers, and graduate-level students interested in putting research-based solutions into practice for effective project management.

Probabilistic modeling represents a subject spanning many branches of mathematics, economics, and computer science to connect pure mathematics with applied sciences. Operational research also relies on this connection to enable the improvement of business functions and decision making. Analyzing Risk through Probabilistic Modeling in Operations Research is an authoritative reference publication discussing the various challenges in management and decision science. Featuring exhaustive coverage on a range of topics within operational research including, but not limited to, decision analysis, data mining, process modeling, probabilistic interpolation and extrapolation, and optimization methods, this book is an essential reference source for decision makers, academicians, researchers, advanced-level students, technology developers, and government officials interested in the implementation of probabilistic modeling in various business applications.

Probabilistic modeling represents a subject arising in many branches of mathematics, economics, and computer science. Such modeling connects pure mathematics with applied sciences. Similarly, data analyzing and statistics are situated on the border between pure mathematics and applied sciences. Therefore, when probabilistic modeling meets statistics, it is a very interesting occasion that has gained much research recently. With the increase of these technologies in life and work, it has become somewhat essential in the workplace to have planning, timetabling, scheduling, decision making, optimization, simulation, data analysis, and risk analysis and process modeling. However, there are still many difficulties and challenges that arrive in these sectors during the process of planning or decision making. There continues to be the need for more research on the impact of such probabilistic modeling with other approaches. Analyzing Data Through Probabilistic Modeling in Statistics is an essential reference source that builds on the available literature in the field of probabilistic modeling, statistics, operational research, planning and scheduling, data extrapolation in decision making, probabilistic interpolation and extrapolation in simulation, stochastic processes, and decision analysis. This text will provide the resources necessary for economics and management sciences and for mathematics and computer sciences. This book is ideal for interested technology developers, decision makers, mathematicians, statisticians and practitioners, stakeholders, researchers, academicians, and students looking to further their research exposure to pertinent topics in operations research and probabilistic modeling.

Businesses consistently work on new projects, products, and workflows to remain competitive and successful in the modern business environment. To remain zealous, businesses must employ the most effective methods and tools in human resources, project management, and overall business plan execution as competitors work to succeed as well. Advanced Methodologies and Technologies in Business Operations and Management provides emerging research on business tools such as employee engagement, payout policies, and financial investing to promote operational success. While highlighting the challenges facing modern organizations, readers will learn how corporate social responsibility and utilizing artificial intelligence improve a company’s culture and management. This book is an ideal resource for executives and managers, researchers, accountants, and financial investors seeking current research on business operations and management.

The oil and gas industry is a complex sector with significant reach in terms of providing the energy needs of the global economy and the security, environmental and development consequences thereof. In particular, the sector is extremely important for the economic growth of emerging markets and developing countries. Furthermore, the life span of oil and gas resources is finite, with high health and safety risks and substantial environmental costs that require careful management and sustainability practices to ensure optimal extraction and utilisation of these resources. This book examines the challenges and opportunities in the oil and gas industry, in the context of emerging markets and developing economies. It provides comprehensive coverage of the management and sustainability practices of the sector, the environmental impact and sustainability of resources as well as the businesses that operate in the sector across the entire value chain. It addresses the current discourse on topics such as the Sustainable Development Goals, the Green Economy, the Paris Agreement and Glasgow Climate Pact and concludes with a chapter on the future of the oil and gas industry. The discussions around energy and energy transitions in particular continue to gain momentum and the book provides a wide-reaching and up-to-date overview of the industry. The book introduces readers to the concepts and formal models of analysis in the oil and gas sector and will serve as a useful resource for students, scholars and researchers in operations, marketing, procurement and supply chain management, project management, health and safety management, environmental economics, natural resource economics, development finance, and development studies. Researchers and practitioners working in these areas will also find the book a useful reference material.

The brief will describe how to develop a risk analysis applied to a project , through a sequence of steps: risk management planning, risk identification, risk classification, risk assessment, risk quantification, risk response planning, risk monitoring and control, process close out and lessons learning. The project risk analysis and management process will be applied to large engineering projects, in particular related to the oil and gas industry. The brief will address the overall range of possible events affecting the project moving from certainty (project issues) through uncertainty (project risks) to unpredictability (unforeseeable events), considering both negative and positive events. Some quantitative techniques (simulation, event tree, Bayesian inference, etc.) will be used to develop risk quantification. The brief addresses a typical subject in the area of project management, with reference to large engineering projects concerning the realization of large plants and infrastructures. These projects are characterized by a high level of change, uncertainty, complexity and ambiguity. The brief represents an extension of the material developed for the course Project Risk Analysis and Management of the Master in Strategic Project Management (Erasmus Mundus) developed jointly by Politecnico di Milano, Heriot Watt University (Edimburgh) and Umea (Sweden). The brief may be used both in courses addressing project management subjects and by practitioners as a guide for developing an effective project risk management plan.

Managing uncertainty has become a new business imperative. Technological discontinuities, regulatory upheavals, geopolitical shocks, abrupt shifts in consumer tastes or behavior, and many other factors have emerged or intensified in recent years and together conspire to undermine even the most carefully constructed business strategies. Managing Uncertainty: Strategies for Surviving and Thriving in Turbulent Times addresses these new challenges, assessing the sources of business turbulence, how to classify uncertainty, and the different ways in which uncertainty can be embraced to allow greater innovation and growth. Drawing on examples from around the world, the book presents the most recent ideas on what it means to manage uncertainty, from practitioners, academics, and consultants. • Addresses the challenges of managing uncertainty in business • Presents a step-by-step guide to managing business uncertainty • Draws examples from major international companies, including Intel, Procter & Gamble, Siemens, Boeing, Quinetiq, Philips, China Telecom, Ford, Apple, Shell, Glaxo SmithKline and many more Written for business leaders and managers looking for new ways to ensure that their businesses continue to thrive in a world of increasing complexity, Managing Uncertainty presents new and innovative ideas about reducing risk by understanding difficult-to-predict shifts.