http://dspace.ensta.edu.dz/jspui/handle/123456789/174 2026-10-06T09:18:36Z http://dspace.ensta.edu.dz/jspui/handle/123456789/431 Title: The Implications of Digitalization in Improving Manufacturing Operational Performances Authors: BELLOULOU, Ilhem; BOUDHAR, Hamza (Directeur de thèse) Abstract: The rapid rise of Industry 4.0 has positioned digital technologies as essential tools for enhancing manufacturing performance. However, empirical evidence indicates that these technologies often fail to produce sustainable improvements. This thesis explores the paradox of Digitalization in manufacturing, arguing that digital tools alone are insufficient to deliver lasting performance gains. Through a historical and conceptual analysis, the study demonstrates that technological revolutions have historically fallen short when not accompanied by human development, organizational learning, and managerial alignment. Drawing on the principles of the Toyota Production System, Lean thinking, and the Shingo Model of Operational Excellence, the thesis positions Digitalization as a supporting element within a broader system of Continuous Improvement. This article also suggests that successful Digital Transformation is more likely when organizations pursue purpose driven and incremental adaptation rather than disruptive, tool centered strategies. Ultimately, the study advocates for a principle-based approach in which Digitalization is integrated into human-centered systems that prioritize learning, alignment, and sustained excellence Description: Master : Programme Complémentaire d'Ingéniorat: Génie Industriel : Alger: Ecole Nationale Supérieure des Technologie Avancées: 2025 2025-01-01T00:00:00Z http://dspace.ensta.edu.dz/jspui/handle/123456789/430 Title: Sustainability in Software Engineering Lifecycle Authors: YOUSFI, Abderrahmane; GHOMARI, Leila (Directeur de thèse) Abstract: As digital technologies expand, their environmental impact has become a growing concern. Sustainable Software Engineering (SSE) offers a promising pathway to address this issue by embedding sustainability principles throughout the Software Development Life Cycle (SDLC). This research explores how methods and tools from industrial engineering can be applied to software development processes to support sustainability goals. It focuses on improving resource efficiency and structuring sustainable workflows using models like GREENSOFT. The study aims to equip software practitioners with practical, scalable strategies drawn from industrial engineering to guide responsible and efficient development. By adapting these methods, software and industrial engineers can work together to reduce CO2 emissions, improve energy efficiency, and integrate environmental concerns without compromising software functionality. This approach provides actionable strategies tailored to industrial engineering contexts. It contributes to a broader understanding of how sustainability can be systematically embedded into software design and engineering processes to meet the dual goals of technological advancement and ecological responsibility. Description: Master : Management et ingénierie de la maintenance industrielle : Alger: Ecole Nationale Supérieure des Technologie Avancées: 2025 2025-01-01T00:00:00Z http://dspace.ensta.edu.dz/jspui/handle/123456789/389 Title: Integrating Lean Manufacturing and Sustainable Practices Authors: BOKHARI, Meriem Lyna; BENFERHAT, Yasmine; RAHMOUNE, Mahdi (Directeur de thèse) Abstract: This study examines the application of Green and Lean Six Sigma (GLSS) methods in manufacturing as tools that ensure the sustainable develop ment of a company. The litterature review highlights the trade-offs and synergetic effects of these methodologies and defines the key factors that favor their success. The study addresses critical questions such as the necessity of GLSS implemen tation, what obstacles will arise during implementation and what improvements can be expected in terms of business processes and ecology. This study proposes a conceptual model to help practitioners address specific issues and promote ef fective use of GLSS across a range of manufacturing environments. Furthermore, the study explores the integration of GLSS within the frameworks of Industry 4.0 and 5.0. It highlights the synergies between digital transformation, human-centric innovation, and sustainable operational excellence.This work could form a relevant documentary basis for future research. It also opens up new avenues of reflection for corporate practitioners, providing several practical suggestions for enhancing the use of GLSS and integrating production with environmental requirements. Description: Master : Programme Complémentaire d'Ingéniorat: Génie Industriel : Alger: Ecole Nationale Supérieure des Technologie Avancées: 2025 2025-01-01T00:00:00Z http://dspace.ensta.edu.dz/jspui/handle/123456789/388 Title: AI Meets Lean Six Sigma: Authors: SALAH, Walid; RAHMOUNE, Mahdi (Directeur de thèse) Abstract: This article explores the use of Artificial Intelligence (AI) within the framework of Lean Six Sigma (LSS) through the DMAIC approach and its five stages, namely Define, Measure, Analyze, Improve, and Control for an improvement of quality management. In addition to increasing productivity, combining AI’s predictive powers with LSS’s methodical approach ensures that most, if not all, quality control standards are achieved. This leads to continuous improvement in a variety of industries where operational effectiveness is essential for sustainability and success. However, several companies tried to apply LSS, only a few of them have been successful in improving their operations to achieve the expected results. This research focuses on how AI -through Machine Learning (ML) models and data-driven approaches- improves each phase of LSS, from problem identification and data mining to root cause analysis and process optimization. In particular, neural networks, anomaly detection, ML algorithms, and digital simulations are described. It also outlines the issues that are associated with it (such as data privacy constraints, specialized skills requirements, and technical compatibility). To sum up, it confirms that applying AI to LSS enhances industrial process advances in efficiency, quality, and sustainability Description: Master : Programme Complémentaire d'Ingéniorat: Génie Industriel : Alger: Ecole Nationale Supérieure des Technologie Avancées: 2025 2025-01-01T00:00:00Z