From Nobel Prize to Project Management: Getting Risks Right

A major source of risk in project management is inaccurate forecasts of project costs, demand, and other impacts. The paper presents a promising new approach to mitigating such risk, based on theories of decision making under uncertainty which won the 2002 Nobel prize in economics. First, the paper documents inaccuracy and risk in project management. Second, it explains inaccuracy in terms of optimism bias and strategic misrepresentation. Third, the theoretical basis is presented for a promising new method called "reference class forecasting," which achieves accuracy by basing forecasts on actual performance in a reference class of comparable projects and thereby bypassing both optimism bias and strategic misrepresentation. Fourth, the paper presents the first instance of practical reference class forecasting, which concerns cost forecasts for large transportation infrastructure projects. Finally, potentials for and barriers to reference class forecasting are assessed.Comment: arXiv admin note: text overlap with arXiv:1302.254

Constrained bayesian inference of project performance models

Project performance models play an important role in the management of project success. When used for monitoring projects, they can offer predictive ability such as indications of possible delivery problems. Approaches for monitoring project performance relies on available project information including restrictions imposed on the project, particularly the constraints of cost, quality, scope and time. We study in this paper a Bayesian inference methodology for project performance modelling in environments where information about project constraints is available and can be exploited for improved project performance. We apply the methodology to probabilistic modelling of project S-curves, a graphical representation of a project’s cumulative progress. We show how the methodology could be used to improve confidence bounds on project performance predictions. We present results of a simulated process improvement project in agile setting to demonstrate our approach

Project Controls and Management Systems : current practice and how it has changed over the past decade

Project Controls and Management System (PCMS) refers to an ecosystem of processes, tools and personnel required for the proper planning and execution of capital projects throughout the different phases of design, procurement, construction and startup. This can be divided into different focus areas (functions) that would include Estimating, Planning, Scheduling, Cost Control, Change Management, Progressing, and Forecasting. Various trends such as globalization, contractor specialization and information technology developments have impacted the way PCMS are implemented and made it the subject of extensive research over the past years to investigate how to best utilize those trends. Replicating the research methodology used in a 2011 report published by the Construction Research Institute (CII), this work aims to investigate the current status of PCMS implementation and how it has changed over the past decade. It was concluded that while the original PCMS principles are still valid, adoption has drastically changed in terms of efficiency for the majority of the functions. The research also identifies areas of potential concerns and provides recommendations for further improvement.Civil, Architectural, and Environmental Engineerin

Project regularity : development and evaluation of a new project characteristic

The ability to accurately characterize projects is essential to good project management. Therefore, a novel project characteristic is developed that reflects the value accrue within a project. This characteristic, called project regularity, is expressed in terms of the newly introduced regular/irregular-indicator RI. The widely accepted management system of earned value management (EVM) forms the basis for evaluation of the new characteristic. More concretely, the influence of project regularity on EVM forecasting accuracy is assessed, and is shown to be significant for both time and cost forecasting. Moreover, this effect appears to be stronger than that of the widely used characteristic of project seriality expressed by the serial/parallel-indicator SP. Therefore, project regularity could also be useful as an input parameter for project network generators. Furthermore, the introduction of project regularity can provide project managers with a more accurate indication of the time and cost forecasting accuracy that is to be expected for a certain project and, correspondingly, of how a project should be built up in order to obtain more reliable forecasts during project control

An overview of recent research results and future research avenues using simulation studies in project management

This paper gives an overview of three simulation studies in dynamic project scheduling integrating baseline scheduling with risk analysis and project control. This integration is known in the literature as dynamic scheduling. An integrated project control method is presented using a project control simulation approach that combines the three topics into a single decision support system. The method makes use of Monte Carlo simulations and connects schedule risk analysis (SRA) with earned value management (EVM). A corrective action mechanism is added to the simulation model to measure the efficiency of two alternative project control methods. At the end of the paper, a summary of recent and state-of-the-art results is given, and directions for future research based on a new research study are presented

Progress in large-shared projects : method for forecasting and optimizing project duration in a distributed project

In large-shared projects, it is still difficult to measure progress due to the complexities involved, because the realization is shared among departments of a company or among companies in the world. Project management and operations research literature is reviewed for discovering various techniques applicable. Widely used tools for progress measurement and forecasting, such as Earned Value Analysis, Progress Plot, Milestone and Resource slip charts, concurrent engineering, can be employed. This paper is based on a problem of pharmaceutical industry where the effectiveness of a certain medical treatment is examined on patients in a number of countries. The number of variables involved increase the complexity of this problem. The main objective is to analyze the effectiveness of a solution in different situations during the project such that a better project duration and a lower cost can be achieved. Our findings suggest that reallocation of patients among countries produces better results in terms of progress

An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014

Results from the centers for disease control and prevention's predict the 2013-2014 Influenza Season Challenge

Background: Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season. Methods: Challenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013-March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). Results: Nine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones. Conclusion: Forecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts. © 2016 The Author(s)