A development of logistics management models for the Space Transportation System

A new analytic queueing approach was described which relates stockage levels, repair level decisions, and the project network schedule of prelaunch operations directly to the probability distribution of the space transportation system launch delay. Finite source population and limited repair capability were additional factors included in this logistics management model developed specifically for STS maintenance requirements. Data presently available to support logistics decisions were based on a comparability study of heavy aircraft components. A two-phase program is recommended by which NASA would implement an integrated data collection system, assemble logistics data from previous STS flights, revise extant logistics planning and resource requirement parameters using Bayes-Lin techniques, and adjust for uncertainty surrounding logistics systems performance parameters. The implementation of these recommendations can be expected to deliver more cost-effective logistics support

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

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

Requirement Risk Level Forecast Using Bayesian Networks Classifiers

Requirement engineering is a key issue in the development of a software project. Like any other development activity it is not without risks. This work is about the empirical study of risks of requirements by applying machine learning techniques, specifically Bayesian networks classifiers. We have defined several models to predict the risk level for a given requirement using three dataset that collect metrics taken from the requirement specifications of different projects. The classification accuracy of the Bayesian models obtained is evaluated and compared using several classification performance measures. The results of the experiments show that the Bayesians networks allow obtaining valid predictors. Specifically, a tree augmented network structure shows a competitive experimental performance in all datasets. Besides, the relations established between the variables collected to determine the level of risk in a requirement, match with those set by requirement engineers. We show that Bayesian networks are valid tools for the automation of risks assessment in requirement engineering