Improving Construction Project Schedules before Execution

The construction industry has been forever blighted by delay and disruption. To address this problem, this study proposes the Fitzsimmons Method (FM method) to improve the scheduling performance of activities on the Critical Path before the project execution. The proposed FM method integrates Bayesian Networks to estimate the conditional probability of activity delay given its predecessor and Support Vector Machines to estimate the time delay. The FM method was trained on 302 completed infrastructure construction projects and validated on a £40 million completed road construction project. Compared with traditional Monte Carlo Simulation results, the proposed FM method is 52% more accurate in predicting the projects’ time delay. The proposed FM method contributes to leveraging the vast quantities of data available to improve the estimation of time risk on infrastructure and construction projects

Improved risk analysis for large projects: Bayesian networks approach

PhDGenerally risk is seen as an abstract concept which is difficult to measure. In this thesis, we consider quantification in the broader sense by measuring risk in the context of large projects. By improved risk measurement, it may be possible to identify and control risks in such a way that the project is completed successfully in spite of the risks. This thesis considers the trade-offs that may be made in project risk management, specifically time, cost and quality. The main objective is to provide a model which addresses the real problems and questions that project managers encounter, such as: • If I can afford only minimal resources, how much quality is it possible to achieve? • What resources do I need in order to achieve the highest quality possible? • If I have limited resources and I want the highest quality, how much functionality do I need to lose? We propose the use of a causal risk framework that is an improvement on the traditional modelling approaches, such as the risk register approach, and therefore contributes to better decision making. The approach is based on Bayesian Networks (BNs). BNs provide a framework for causal modelling and offer a potential solution to some of the classical modelling problems. Researchers have recently attempted to build BN models that incorporate relationships between time, cost, quality, functionality and various process variables. This thesis analyses such BN models and as part of a new validation study identifies their strengths and weaknesses. BNs have shown considerable promise in addressing the aforementioned problems, but previous BN models have not directly solved the trade-off problem. Major weaknesses are that they do not allow sensible risk event measurement and they do not allow full trade-off analysis. The main hypothesis is that it is possible to build BN models that overcome these limitations without compromising their basic philosophy

A Framework for Leveraging Artificial Intelligence in Project Management

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThis dissertation aims to support the project manager in their daily tasks. As we use artificial intelligence (AI) and machine learning (ML) in everyday life, it is necessary to include them in business and change traditional ways of working. For the purpose of this study, it is essential to understand challenges and areas of project management and how artificial intelligence can contribute to them. A theoretical overview, applying the knowledge of project management, will show a holistic view of the current situation in the enterprises. The research is about artificial intelligence applications in project management, the common activities in project management, the biggest challenges, and how AI and ML can support it. Understanding project managers help create a framework that will contribute to optimizing their tasks. After designing and developing the framework for applying artificial intelligence to project management, the project managers were asked to evaluate. This study is essential to increase awareness among the stakeholders and enterprises on how automation of the processes can be improved and how AI and ML can decrease the possibility of risk and cost along with improving the happiness and efficiency of the employees

Mitigating Space Industry Supply Chain Risk Thru Risk-Based Analysis

Using risk-based analysis to consider supply chain disruptions and uncertainty along with potential mitigation strategies in the early stages of space industry projects can be used avoid schedule delays, cost overruns, and lead to successful project outcomes. Space industry projects, especially launch vehicles, are complicated assemblies of high-technology and specialized components. Components are engineered, procured, manufactured, and assembled for specific missions or projects, unlike make-to-stock manufacturing where assemblies are produced at a mass production rate for customers to choose off the shelf or lot, like automobiles. The supply chain for a space industry project is a large, complicated web where one disruption, especially for sole-sourced components, could ripple through the project causing delays at multiple project milestones. This ripple effect can even cause the delay or cancelation of the entire project unless project managers develop and employ risk mitigations strategies against supply chain disruption and uncertainty. The unpredictability of when delays and disruptions may occur makes managing these projects extremely difficult. By using risk-based analysis, project managers can better plan for and mitigate supply chain risk and uncertainty for space industry projects to better manage project success. Space industry project supply chain risk and uncertainty can be evaluated through risk assessments at major project milestones and during the procurement process. Mitigations for identified risks can be evaluated and implemented to better manage project success. One mitigation strategy to supply chain risk and uncertainty is implementing a dual or multi-supplier sourcing procurement strategy. This research explores using a risk-based analysis to identify where this mitigation strategy can be beneficial for space industry projects and how its implementation affects project success. First a supply chain risk assessment and mitigation decision tool will be used at major project milestones to show where a multi-sourcing strategy may be beneficial. Next, updated supplier quote evaluation tools will confirm the usage of multiple suppliers for procurement. Modeling and simulation are then used to show the impact of that strategy on the project success metrics of cost and schedule

A new approach for supply chain risk management: Mapping SCOR into Bayesian network

Purpose: Increase of costs and complexities in organizations beside the increase of uncertainty and risks have led the managers to use the risk management in order to decrease risk taking and deviation from goals. SCRM has a close relationship with supply chain performance. During the years different methods have been used by researchers in order to manage supply chain risk but most of them are either qualitative or quantitative. Supply chain operation reference (SCOR) is a standard model for SCP evaluation which have uncertainty in its metrics. In This paper by combining qualitative and quantitative metrics of SCOR, supply chain performance will be measured by Bayesian Networks. Design/methodology/approach: First qualitative assessment will be done by recognizing uncertain metrics of SCOR model and then by quantifying them, supply chain performance will be measured by Bayesian Networks (BNs) and supply chain operations reference (SCOR) in which making decision on uncertain variables will be done by predictive and diagnostic capabilities. Findings: After applying the proposed method in one of the biggest automotive companies in Iran, we identified key factors of supply chain performance based on SCOR model through predictive and diagnostic capability of Bayesian Networks. After sensitivity analysis, we find out that ‘Total cost’ and its criteria that include costs of labors, warranty, transportation and inventory have the widest range and most effect on supply chain performance. So, managers should take their importance into account for decision making. We can make decisions simply by running model in different situations. Research limitations/implications: A more precise model consisted of numerous factors but it is difficult and sometimes impossible to solve big models, if we insert all of them in a Bayesian model. We have adopted real world characteristics with our software and method abilities. On the other hand, fewer data exist for some of the performance metrics. Practical implications: Mangers often use simple qualitative metrics for SCRM. However, combining qualitative and quantitative metrics will be more useful. Industries can recognize the important uncertain metrics by predicting supply chain performance and diagnosing possible happenings. Originality/value: This paper proposed a Bayesian method based on SCOR metrics which has the ability to manage supply chain risks and improve supply chain performance. This is the only presented case study for measuring supply chain performance by SCOR metrics.Peer Reviewe

Project Risk Management : a study on the risk management approach utilized by ConocoPhillips Capital Projects

Master's thesis in Risk managementThe oil and gas industry on the Norwegian continental shelf is currently experiencing a record-breaking activity level and optimism fueled by high oil prices and major new discoveries made during 2011. The total investments have been at an all-time high the last couple of years and it will most likely continue to rise due to the amount of modification and redevelopment projects, as well as new field developments. However, with every great opportunity there are normally accompanying threats that need to be managed in order to ensure success. In such a heated climate as experienced in the oil and gas industry, there are many potential pitfalls related to infrastructure development projects, which are best exemplified by the reported cost overruns and delays affecting the Yme-redevelopment project. This report asks the question whether the current risk management system utilized by ConocoPhillips is providing value in the execution of major projects by assisting the projects in steering clear of threats with the potential to cause serious cost overruns and schedule delays. To answer the question, a common background of knowledge related to project and risk management is outlined, before introducing ConocoPhillips as a company, the worldwide project organization and the Norwegian business unit. With the context set, an overview of the project development process is given before going more into the details on the risk management process, the risk analysis modeling and the way risk management is tied into the overall development process. Based on analysis of current practices, processes and internal requirements, it becomes clear that ConocoPhillips has an extensive and rigorous system set up in order to gradually mature projects until they are ready to be implemented. Risk management plays a key part in the development process where a lot of focus and resources are used to build highly advanced integrated cost and schedule risk models generating P50 values of both project cost and completion dates that are used for project sanction. The report comes to a conditional positive conclusion, where the risk management system in light of the overall development process is deemed to create value in its contribution of providing predictability in terms of project cost and schedule compared to the project premise. However, although predictability has an inherent value for the project owners and government, the full benefits of risk management are not being realized. To unlock the full potential of risk management at ConocoPhillips, this report makes recommendations intended to shift the focus of risk management from the current reporting and verification role, to promoting the use of risk analysis in the early concept-screening phase and in the wider context of value based decision-making that must take into account more than just cost and schedule uncertainty