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Zero-defect manufacturing strategies towards on-line production management

Z-fact0r (PI): Zero-defect manufacturing strategies towards on-line production management for European factories

Manufacturing represents approximately 21 % of the EU’s GDP and 20 % of its employment, providing more than 30 million jobs in 230 000 enterprises, mostly SMEs. Moreover, each job in the industry is considered to be linked to two more in related services. European manufacturing is also a dominant element in international trade, leading the world in areas such as automotive, machinery and agricultural engineering. Already threatened by both the lower-wage economies and other high-tech rivals, the situation of EU companies was even made more difficult by the downturn.

The Z-Fact0r consortium has conducted extensive state-of-the-art research and realized that although a number of activities have been trying to address the need for zero-defect manufacturing, still there is a vast business opportunity for innovative, high-ROI (Return on Investment) solutions to ensure, better quality and higher productivity in the European manufacturing industries.

The Z-Fact0r solution comprises the introduction of five multi-stage production-based strategies targeting:

  • the early detection of the defect (Z-DETECT),
  • the prediction of the defect generation (Z-PREDICT),
  • the prevention of defect generation by recalibrating the production line (multi-stage), as well as defect propagation in later stages of the production (Z-PREVENT),
  • the reworking/remanufacturing of the product, if this is possible, using additive and subtractive manufacturing techniques (Z-REPAIR) and
  • the management of the aforementioned strategies through event modelling, KPI (key performance indicators) monitoring and real-time decision support (Z-MANAGE).

To do that we have brought together a total of thirteen EU-based partners, representing both industry and academia, having ample experience in cutting-edge technologies and active presence in the EU manufacturing.


  • Morad Danishvar, Alireza Mousavi, Sebelan Danishvar, Afsahin Mansouri (2020). Implementing Multi-Objective Batch Base Flow-Shop Scheduling Optimisation (MOBS-NET) using Fully Connected Deep Neural Network. Under review at International Journal of Production Research
  • Morad Danishvar, Alireza Mousavi, Sebelan Danishvar (2019). The Genomics of Industrial Process through the Qualia of Markovian Behaviour, under review at IEEE Transactions on Systems, Man and Cybernetics: Systems.
  • Foivos Psarommatis, Morad Danishvar, Ali Mousavi, Dimitris Kiritsis (2019). Cost-Based Optimisation of manufacturing Key Performance Indicators for Zero Defect Manufacturing, Under review at International Journal of Production Research.
  • Morad Danishvar, Alireza Mousavi, Peter Broomhead (2018). EventiC: A Real-Time Unbiased Event-Based Learning Technique for Complex Systems, 2018 IEEE Transactions on Systems, Man and Cybernetics: Systems.
  • Huang, Z., Li, M., Mousavi, A., Danishvar, M., & Wang, Z. (2018). EGEP: An Event Tracker Enhanced Gene Expression Programming for Data-Driven System Engineering Problems. IEEE Transactions on Emerging Topics in Computational Intelligence.

Meet the Principal Investigator(s) for the project

Dr Alireza Mousavi - Graduated as BEng with equivalent of first class from Tehran Azad University in Industrial Engineering, Planning and Analysis of Systems in 1994. I worked first as placement and then full time in Automotive Industry Management Consultancy from 1992-1996. In 1996 I joined the Postgraduate Research programme (PhD) of the well-known Department of Manufacturing and Engineering Systems of Brunel University with a scholarship from the University. I obtained my PhD in May 2000. In year October 1999, I was appointed as an RA on an EPSRC/MAFF project MEATRAC – where I developed a fully novel monitoring and control system using Sensors & Actuation, SCADA, PLC, RFID Technology, and Enterprise Data Management System for 100% Tracking and Traceability of Meat products. It was successfully delivered in mid-2002. From May 2002, I was appointed as a lecturer in the same department and to date have covered a wide range of teaching and supervising UG and PG projects in subject areas ranging from mathematics, software engineering, software development, systems modelling & probability theory, control, and embedded systems. The modules covered all undergraduate and postgraduate years, and taught in a highly international and diverse cohort of students. I contribute to a wide range of classical (e.g. mathematics, probability theory, queuing theory, discrete systems, software development) and modern subjects (e.g. Machine Learning, AI, Applied Control, and Cyberphyisical systems) at the Departments of Computer Science, Electronic and Copter Engineering as well as Department of Mechanical & Aerospace Engineering with the College of Engineering, Design and Physical Sciences. My current research activities are concentrated on digital transformation and smartification of Industrial Systems, especially within the Industry 4.0 context covering sensors-actuation, signal processing and feature extraction, machine learning, modelling, control and optimisation. For complete list of publications and other information please visit my website: http://www.brunel.ac.uk/~emstaam/ Systems Engineering REsearch Group (SERG) Website: http://www.brunel.ac.uk/~emstaam/SERG_BRUNEL/index.html University site about SERG (https://www.brunel.ac.uk/computer-science/research-and-phd-programmes/System-Engineering-Research-Group)  Special Announcement: SERG is Recruiting Research Assistants, Fellows and PhD students in the areas of Automation, Control, Sensors and Actuation, Mathematical Modelling and Optimisation, Machine Learning & AI, and Software Engineering (contact me for details)

Related Research Group(s)

Digital Manufacturing

Digital Manufacturing - Being at the forefront of solutions for building smart machines, we create an operational framework for the digital transformation to Industry 4.0.

Partnering with confidence

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Project last modified 21/07/2021