Ikhlef JEBBOR | Industrial engineering | Excellence in Research

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Dr. Ikhlef JEBBOR | Industrial engineering | Excellence in Research

ibn Tofail University at National School of Applied Sciences, Ibn Tofail University, Kenitra, Morocco

A Ph.D. candidate in Sustainable Optimization of Manufacturing and Supply Chain with extensive experience in both academia and industry. Focuses on lean manufacturing, production optimization, and AI for sustainable development. Currently an Engineering Project Leader at Sumitomo Electric Wiring Systems (SEWS-E), leading process improvements in the automotive industry. Expert in project management, operational research, and continuous improvement strategies such as Six Sigma and Kaizen. Has published numerous peer-reviewed articles and presented at international conferences on sustainability and advanced manufacturing techniques.

Professional Profile

Scopus Profile

Education:

  • Ph.D. in Sustainable Optimization of Manufacturing and Supply Chain (ENSA Kenitra, Ibn Tofail University, 2022 – Current)
  • State Engineer, Industrial Engineering (GI) (FST Errachidia, Moulay Ismail University, 2010 – 2013)
  • Physics & Chemistry Teacher Education Diploma (CPR Mohamed V SAFI, Morocco, 2009 – 2010)
  • Professional University Degree, Renewable Energies and Sustainable Development (ERDD) (Faculty of Sciences -Agadir, Ibn Zohr University, 2007 – 2009)
  • General University Degree, Physical Matter Sciences (SMP) (Faculty of Sciences -Agadir, Ibn Zohr University, 2005 – 2007)
  • BAC, Experimental Science (Salah Eddin Elayyoubi High School, Tinghir, 2005)

Professional Experience:

Sumitomo Electric Wiring Systems (SEWS-E)

  • Engineering Project Leader (Feb 2021 – Current)
  • Senior Project Engineer (Aug 2018 – Jan 2021)
  • Process Engineer (Sep 2015 – Aug 2018)
  • Work Study Engineer (May 2014 – Aug 2015)

Research Interests:

  • Facilities Design and Optimization
  • Lean Manufacturing
  • Production Planning and Scheduling
  • Supply Chain Management
  • Mathematical Modeling and Optimization

Awards and Honors:

  • Outstanding Project Leader Award
    Sumitomo Electric Wiring Systems (SEWS-E), 2022
    Recognized for leading key process improvements in automotive production, significantly enhancing efficiency and innovation.
  • Excellence in Lean Manufacturing Implementation
    Sumitomo Electric Wiring Systems (SEWS-E), 2020
    Awarded for implementing lean manufacturing strategies that resulted in significant cost savings and production efficiency.
  • Best Paper Award
    International Conference on Industrial Engineering and Applications (ICIEA), 2023
    Awarded for presenting the paper on “Improvement of an Assembly Line in the Automotive Industry: A Case Study in Wiring Harness Assembly Line.”
  • Research Excellence Award
    ENSA Kenitra, Ibn Tofail University, 2022
    For contributions to sustainable optimization of manufacturing and supply chain research, particularly in the automotive industry.
  • Innovation Award for Sustainable Practices
    Sustainability and Advanced Manufacturing Techniques Conference, 2023
    Honored for innovative research in applying AI and optimization techniques for sustainable manufacturing practices.

Conclusion:

With an extensive background in industrial engineering, lean manufacturing, and AI applications, I have consistently delivered impactful results in both academia and industry. My experience as an Engineering Project Leader at Sumitomo Electric Wiring Systems (SEWS-E) and my ongoing doctoral research on the sustainable optimization of manufacturing and supply chain systems equip me with a strong foundation for tackling complex industry challenges.

Through my research, publications, and practical experience, I aim to contribute to the development of more efficient, sustainable, and innovative manufacturing processes. I am committed to driving continuous improvement through the application of cutting-edge methodologies such as Six Sigma, Kaizen, and AI-driven optimizations. As I continue to advance in both academia and industry, I strive to shape the future of sustainable industrial engineering and contribute to global efforts for sustainable development.

Publication Top Notes

  1. 📊 Article: Forecasting supply chain disruptions in the textile industry using machine learning: A case study
    • Authors: Jebbor, I., Benmamoun, Z., Hachimi, H.
    • Journal: Ain Shams Engineering Journal, 2024, 15(12), 103116
    • Citations: 1
  2. 🌍 Conference Paper: Optimization of Carbon Emissions in Asphalt Pavement Construction
    • Authors: Benmamoun, Z., Elkhechafi, M., Abdo, A.A., Jebbor, I.
    • Conference: 10th Edition of the International Conference on Optimization and Applications, ICOA 2024 – Proceedings
    • Citations: 0
  3. 🧠 Conference Paper: Comparison of Generative AI Models in Supply Chain Management: Benefits, Applications and Challenges
    • Authors: Khlie, K., Benmamoun, Z., Jebbor, I., Hachimi, H.
    • Conference: 10th Edition of the International Conference on Optimization and Applications, ICOA 2024 – Proceedings
    • Citations: 0
  4. 🤖 Article: Generative AI for enhanced operations and supply chain management
    • Authors: Khlie, K., Benmamoun, Z., Jebbor, I., Serrou, D.
    • Journal: Journal of Infrastructure, Policy and Development, 2024, 8(10), 6637
    • Citations: 1
  5. 🌱 Article: Revolutionizing cleaner production: The role of artificial intelligence in enhancing sustainability across industries
    • Authors: Jebbor, I., Benmamoun, Z., Hachmi, H.
    • Journal: Journal of Infrastructure, Policy and Development, 2024, 8(10), 7455
    • Citations: 1
  6. 📉 Article: Leveraging variational autoencoders and recurrent neural networks for demand forecasting in supply chain management: A case study
    • Authors: Khlie, K., Benmamoun, Z., Fethallah, W., Jebbor, I.
    • Journal: Journal of Infrastructure, Policy and Development, 2024, 8(8), 6639
    • Citations: 5
  7. 📚 Conference Paper: Application of Fuzzy Logic for Evaluating Student Learning Outcomes in E-Learning
    • Authors: Mousse, M.A., Almufti, S.M., García, D.S., Aljarbouh, A., Tsarev, R.
    • Conference: Lecture Notes in Networks and Systems, 2024, 935 LNNS, pp. 175–183
    • Citations: 2
  8. 🚗 Conference Paper: Application of Manufacturing Cycle Efficiency to Increase Production Efficiency: Application in Automotive Industry
    • Authors: Jebbor, I., Benmamoun, Z., Hachimi, H.
    • Conference: 2024 4th International Conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2024
    • Citations: 3
  9. 🔧 Conference Paper: Process Improvement of Taping for an Assembly Electrical Wiring Harness
    • Authors: Jebbor, I., Raouf, Y., Benmamoun, Z., Hachimi, H.
    • Conference: Lecture Notes in Business Information Processing, 2024, 507 LNBIP, pp. 35–48
    • Citations: 3
  10. ⚙️ Article: Optimizing Manufacturing Cycles to Improve Production: Application in the Traditional Shipyard Industry
  • Authors: Jebbor, I., Benmamoun, Z., Hachimi, H.
  • Journal: Processes, 2023, 11(11), 3136
  • Citations: 10

 

 

 

Manas Ranjan Sethi | ECE | Best Researcher Award

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Mr. Manas Ranjan Sethi | ECE | Best Researcher Award

Research Scholar at NIT Silchar, India

Manas Ranjan Sethi is a dedicated academic professional currently pursuing a Ph.D. in Electronics and Instrumentation Engineering at NIT Silchar, with a strong focus on machine learning applications in fault diagnosis and energy systems. He holds an M.Tech in Electronics & Telecommunication from BPUT, Odisha and has over 12 years of teaching experience, having worked as an Assistant Professor at Gandhi Institute for Technology (GIFT) and as a Lecturer at Koustuv Institute of Self Domain, Bhubaneswar. His research interests include machine learning, signal processing, and sustainable energy systems, particularly in wind turbine diagnostics and emotion recognition using EEG signals. Manas has contributed to numerous journals, conferences, and book chapters, and he has earned distinctions such as qualifying CBSE-UGC NET and GATE. He is also skilled in technical tools, enjoys singing, and has a passion for reading.

Publication Profile : 

Scopus

 

🎓 Educational Background :

Manas Ranjan Sethi is currently pursuing a Ph.D. in Electronics and Instrumentation Engineering at NIT Silchar (since March 2020). He holds an M.Tech in Electronics & Telecommunication (Specialization in Communication Engineering) from BPUT, Odisha (2012), with a CGPA of 8.20. He completed his B.E. in Electronics & Telecommunication from BPUT, Odisha, in 2006, securing a 62.19%. Additionally, he completed his +2 Science and Matriculation from MP Board, Madhya Pradesh.

💼 Professional Experience :

Manas has a rich academic career spanning over 12 years. He served as an Assistant Professor in the Electronics & Communication Engineering Department at Gandhi Institute for Technology (GIFT), Bhubaneswar, from November 2013 to March 2020. Prior to that, he worked as a Lecturer in the Electronics & Telecommunication Engineering Department at Koustuv Institute of Self Domain, Bhubaneswar, from July 2007 to November 2013. He has a strong foundation in teaching and mentoring students in the field of Electronics and Communication Engineering.

📚 Research Interests : 

Manas’s research interests lie in the domains of Machine Learning, Signal Processing, and Fault Diagnosis. His work focuses on vibration signal-based diagnostics and energy extraction using wind turbines. He is passionate about leveraging machine learning techniques for predictive maintenance and condition monitoring. His recent research includes the application of meta-classifiers for diagnosing wind turbine blade faults and exploring emotion recognition through EEG signals.

🏆Achievements & Certifications :

Manas has earned several academic distinctions, including qualifying the CBSE-UGC NET (Electronic Science) in July 2018, and securing GATE scores of 260 (2016) and 218 (2011) in Electronics and Communication. He has also attended and contributed to various seminars, workshops, and short-term courses in fields such as VLSI Design, Microwave Filters, and Adaptive Signal Processing.

📝 Publication Top Notes :

  • Sethi, M. R., Subba, A. B., Faisal, M., Sahoo, S., & Koteswara Raju, D. (2024). Fault diagnosis of wind turbine blades with continuous wavelet transform based deep learning model using vibration signal. Engineering Applications of Artificial Intelligence, 138, 109372.
  • Sethi, M. R., Sahoo, S., Dhanraj, J. A., & Sugumaran, V. (2023). Vibration Signal-Based Diagnosis of Wind Turbine Blade Conditions for Improving Energy Extraction Using Machine Learning Approach. Smart and Sustainable Manufacturing Systems, 7(1), 14–40.
  • Chatterjee, S., Sethi, M. R., & Asad, M. W. A. (2016). Production phase and ultimate pit limit design under commodity price uncertainty. European Journal of Operational Research, 248(2), 658–667.
  • Sethi, M. R., Parhi, S. S., Sahoo, S., Sugumaran, V., & Mohanty, S. R. (2023). Fault Diagnosis of Wind Turbine Blades Through Vibration Signal Using Filtered Cultivation Data: A Comparative Study. Proceedings of the 2023 IEEE Region 10 Symposium, TENSYMP 2023.
  • Kar, P., Hazarika, J., & Sethi, M. R. (2023). A Comparative Study between Supervised and Unsupervised Techniques for Two Class Emotion Recognition using EEG. Proceedings of the 2023 IEEE 8th International Conference for Convergence in Technology, I2CT 2023.
  • Banala, H. S., Sahoo, S., Sethi, M. R., & Sharma, A. K. (2023). Fault Diagnosis in Wind Turbine Blades Using Machine Learning Techniques. In R. Doriya, B. Soni, A. Shukla, & X. Z. Gao (Eds.), Machine Learning, Image Processing, Network Security and Data Sciences (Lecture Notes in Electrical Engineering, Vol. 946), 401–411. Springer, Singapore.
  • Sethi, M. R., Sahoo, S., Kanoongo, S., & Hemasudheer, B. (2022). A Comparative Study on Diagnosing Wind Turbine Blade Fault Conditions using Rule Classifier. Proceedings of the 2022 2nd International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET 2022), 1–6. doi: 10.1109/ICEFEET51821.2022.9848401.
  • Sethi, M. R., Hemasudheer, B., Sahoo, S., & Kanoongo, S. (2022). A Comparative Study on Diagnosing Wind Turbine Blade Fault Conditions using Vibration Data through META Classifiers. Proceedings of the 2022 4th International Conference on Energy, Power, and Environment (ICEPE 2022), 1–5. doi: 10.1109/ICEPE55035.2022.9798026.