Theoretical Review of Bayesian Networks

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Table of contents

1. General Introduction
1.1. Research context
1.2. Research Questions
1.3. Research goals
1.4. Working Hypothesis
1.5. Research contributions
1.5.1. A generic model to evaluate project management maturity
1.5.2. Applying the proposed PMMM to agile project management methodologies.
1.5.3. Evaluate and select the appropriated AI technique that could explain causal relationships within Project Management.
1.5.4. Propose a methodology to explain how project management maturity can explain project performance for specific projects.
1.6. Structure of the PhD Thesis
2 An Invariant-Based Project Management Maturity Model
2.1 Introduction
2.2 Literature Review on Project Management Maturity Models
2.2.1 Basic concepts and definitions in PMMMs
2.2.2 Traditional PMMMs
2.2.3 Limits of PMMMs
2.3 Building an Invariant-based Maturity Model (IB2M)
2.3.1 Step 1: Identify the most relevant results in PMMMs literature to extract invariants
2.3.2 Step 2: Integrating the best practices of PMMs in our proposed IB2M
2.4 Industrial Assessment
2.4.1 Step 3: Check IB2M’s qualitative value by interviews
2.3.2 Step 4: Check IB2M’s quantitative value by data related to past projects
2.5 Discussion
2.6 Conclusion
3 Applying Invariant-Based Management Model to traditional and agile project management 
3.1 Industry 4.0: Sopra Steria conception
3.2 Background on project management methodologies
3.3 Agile Project Management as a challenger of Traditional Project Management
3.3.1 Identification of Traditional Project Management (TPM) principles
3.3.2 Identification of Agile Project Management (APM) principles, the case of Scrum
3.3.3 Terms of comparison
3.4 Steps towards an Agile Project Management Maturity Model
3.4.1 Step 1. Build a Project Management Conceptual Model
3.4.2 Step 2. Build a Maturity Grid
3.4.3 Step 3. Define the Roadmap for Agilification
3.5 New Tools for Enabling Projects’ Agilification
3.5.1 Step 1. Set a Project Management Conceptual Model
3.5.2 Step 2. Build a Maturity Grid
3.5.3 Step 3. Define the Roadmap for Agilification
3.6 Discussion
3.6.1 Theoretical learnings
3.6.2 Practical learnings
3.7 Conclusion
4 Selection and use of the appropriate technique to solve the research problem.
4.1 Introduction
4.2 Review of Artificial Intelligence techniques used in PM literature
4.2.1. ANN Foundations
4.2.2. Reinforcement learning (RL): a brief review
4.2.3 Brief Review on Bayesian networks (BN)
4.3 Selection of the adequate technique
4.3.1. Data assessment framework
4.3.2. Data assessment related to our inquiry
4.4. Theoretical Review of Bayesian Networks
4.4.1 Interest of Synthetic Nodes
4.5. Building causal models based on BN
4.5.1. The hyperparameter setting problem
4.5.2. Eligibility Criteria selection
4.5.3. The influence of the hyperparameters on the eligibility criteria
4.6 Using Bayesian Networks for Project Management Evaluation
4.6.1. First limitation: absence of synthetic nodes and conceptual structure
4.6.2. Second limitation: incorrect number of states.
4.6.3. Third limitation: semantics undefined strictly
4.6.4. Fourth limitation: having too many target nodes
4.6.5. Fifth limitation: causal paths multiplicity and ML impossibility
4.5.6. Synthesis
4.7 Discussion: defining BN Building rules
4.8 Conclusion
5 Projects’ cost overruns prediction methodology based on Bayesian Networks
5.1 Introduction
5.2 A Bayesian Approach
5.2.1. Step 1 – Define a semantic model for Project Management Maturity Evaluation
5.2.2. Step 2 – Define how the input nodes will measure maturity.
5.2.3. Step 3 – Define and classify the synthetic nodes.
5.2.4. Step 4 – Define aggregation rules for synthetic nodes
5.2.5. Step 5 – Select, clean, and structure the database.
5.2.6. Step 6 – Define the target node
5.2.7. Step 7 – Train and test the causal model.
5.3 Case Study: Evaluation of Drift Factors in Oil and Gas Offshore Projects
5.3.1. Step 1: Define a common model for Project Management Maturity Evaluation.
5.3.2. Step 2. Define how the input nodes will measure maturity.
5.3.3. Step 3. Define and classify the synthetic nodes.
5.3.4 Step 4: Define aggregation rules for synthetic nodes
5.3.5 Step 5: Select, clean, and structure the database.
5.3.6 Step 6: Define the target node.
5.3.7 Step 7: Train and test the causal model.
5.4 Improvement Scenarios
5.5 Discussion
5.6. Conclusion
6 General Conclusion & Perspectives
6.1. Thesis Abstract
6.2. Our Contributions in detail
6.3. Research perspectives
6.2.1. Project organization as a system placed in its environment.
6.2.2 Evaluating project management maturity from traces
7 Bibliography