http://dspace.ensta.edu.dz/jspui/handle/123456789/174 2026-03-25T10:27:16Z 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 http://dspace.ensta.edu.dz/jspui/handle/123456789/387 Title: Securing SCADA Systems Using Machine Learning Authors: LEBKARA, Haithem; OUAR, Narimane; Belayadi, Djahida (Directeur de thèse) Abstract: SCADA systems are used to effectively monitor and control critical industrial infrastructure . Due to Industry 4.0 , SCADA systems have evolved towards linked architectures, which has enhanced operational efficiency but also made them more vulnerable to cyberattacks. SCADA systems, which were once intended to be dependable, are now at risk from malware, DoS attacks, illegal access,and various types of threats endangering both safety and service continuity. In this context, artificial intelligence enables real-time detection of anomalies and cyberattacks especially by ML and deep DL based IDS. This paper offers a thorough analysis of current AI strategies for SCADA security, emphasising important techniques, difficulties, and results . 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/123456789/386 Title: Intelligent Manufacturing Execution Systems in the Food and Beverage Industry Authors: BOUDJENOUN, Chaima; SAIB, Malak; REZGUI, Wail (Directeur de thèse) Abstract: Manufacturing Execution Systems (MES) emerged as a software solution to bridge the gap in digital communication between two functional layers: the company’s management and the supervisory system. Initially focused on tracking and operational control, MES has evolved through the integration of Artificial Intelligence (AI), the Internet of Things (IoT), and Big Data Analytics (BDA), leading to the emergence of Intelligent Manufacturing Execution Systems (IMES), which satisfy Industry 4.0 requirements. Advanced technologies have driven industries toward digitalization, particularly within the Food and Beverage (FB) sector. This industry faces demands for traceability, compliance, agility, and sustainability, necessitating flexible, real-time solutions that traditional systems cannot provide. The digitalization of the FB sector has been propelled by the integration of advanced technologies into intelligent MES platforms, which provide the capabilities necessary to meet modern industry demands. To demonstrate these statements, we described eight case studies from the dairy, meat, dietary supplement, brewing, and beverage manufacturing sectors. The final case study, which we focus on and conducted at ECCBC, is detailed to illustrate the successful implementation of IMES, which has automated monitoring, reduced waste, and improved responsiveness to consumers’ needs. This paper analyzes the evolution of MES into intelligent platforms within the context of digital transformation in the food and beverage industry. It describes the critical role of IMES in enabling innovative, sustainable, and flexible manufacturing environments. It also addresses several implementation challenges, including system integration, technology complexity, and organizational readiness. The findings support the strategic positioning of IMES as a key enabler of next-generation manufacturing systems aligned with Industry 4.0 objectives. Description: Master : Programme Complémentaire d'Ingéniorat: Génie Industriel : Alger: Ecole Nationale Supérieure des Technologie Avancées: 2025 2025-01-01T00:00:00Z