4P Factory research

The 4P revolution

Whereas these requirements and programs pave the way for focused research efforts, we claims that the ‘4th industrial revolution’ need not only change the way systems are designed and built, but also the very paradigm underlying there capabilities. We claim that the Future Factory will only become a reality if it becomes a 4P Factory: Participative, Predictive, Preventive, and Personalized, the same way medicine is becoming 4P [Aufray2010, Hood2012].

  • Participative: Top-down management is forced to its limits in a hyper-connected world. Emergent production as well as management practices, AI-based automation processes, product-driven lines strive for a bottom-up approach to manufacturing and industry
  • Predictive: Based on prod line monitoring and previous experience, new products can be designed, simulated and produced with great reliance and reduced costs. Quality as well as security issues are considered.
  • Preventive: Prediction enables anticipation of potential problems and control of risks. Quality as well as security issues are considered.
  • Personalized: Clients and suppliers become actors of the prod line through customer feedback and small loop logistics systems. Individual needs are served through high volume prod lines able to answer individualized orders.

The switch to the 4P Factory requires switching models, switch paradigms, and switch minds. It requires global short-timed logistic flows to merge with global immediate knowledge flows, and factories to dwelve into knowledge-driven production, which will be structured according to the law of knowledge:

Knowledge flow is proportional to dedicated attention and time, and by the 3 rules of knowledge [Aberkane2014]:

  • Rule #3: Knowledge gathering makes it more valuable, and is the condition for the Predictive Factory
  • Rule #2: Knowledge transfer needs time and flow, so that the Preventive Factory can become a reality
  • Rule #1: Knowledge exchange increases the resources of all exchange partners, and fosters the Participative and Personalized Factory.

Research axes

The work of the e-lab “4P Factory” will be articulated around 3 axes:

  • Making state of the art solutions available to a large public, by documenting the Core research questions of the domain
  • Providing practitioners with ready-to-use Tools
  • Structuring the 4P Factory as a research domain of its own through systematic investigation of Hot research questions.

Each of these axes will be enriched according to the experience of e-lab members.

Core research questions

Some of the Core research questions identified at the beginning of the e-lab are:

  • Rule #3: Knowledge gathering
    • How to capitalize knowledge [Sanin2009]? How to let it self-organize [Ramos2004]?
  • Rule #2: Knowledge transfer and embedding
    • To humans, to the machine
    • How to embed knowledge in the evolution of the 4P Factory? To predict changes[Clarkson2004][Eckert2004], to optimize processes and products[Clarkson2010]?
  • Rule #1: Knowledge exchange
    • How to master the complexity of the knowledge-based industry? Which (cognitive?) structure [Legrand2013]? Which analysis tools?


Some important tools identified at the beginning of the e-lab are:

  • Rule #3: Knowledge gathering
    • SOEKS [Sanin2009], Self-organizing maps [Ramos2004]
  • Rule #2: Knowledge transfer and embedding
    • Evolving objects [Keijzer2002], genetic algorithms [Arena2002]
    • Domain specific solutions: logistics [Zanjirani2009], prod chains [Deroussi2006], robotics [Shibata2001]
  • Rule #1: Knowledge exchange
    • Design Structure Matrix [Browning2001], game theory in distributed environments [Lisý2012]

Hot research questions

Some of the Hot research questions identified at the beginning of the e-lab are:

  • Rule #3: Knowledge gathering
    • How to enable decision [Shafiq2014]?
    • How to foster creativity [Rousselot2012]?
    • At the right place: automation vs. expertise balance
  • Rule #2: Knowledge transfer and embedding
    • How to foster communication between stakeholders [Rasoulifar2014]?
    • How to optimize the right entities (processes/products) [Isaksson2014]?
  • Rule #1: Knowledge exchange
    • How to embed flowing knowledge into actual products to enable users to provide their input and request for personalized goods?
    • How to perceive and interpret weak signals in complex systems and organizations [Lisý2012]?
    • How to model internal and external interdependencies [Wong2003][Dulac2007]?

This is why we strongly believe the 4P Factory can only be designed and implemented in the context of the UNESCO Unitwin Complex System – Digital Campus.

Research Tracks

The identified research tracks for the 4P Factory e-lab are:

  • Industrial engineering and the factory environment
    • Design and biomimetic (Claudia Eckert)
    • Architectures and processes for design and innovation
    • Simulation for product design
    • Dynamic product lines and logistic chains (Laurent Deroussi)
    • Sustainable development and resource management (Ricardo Palma; Paula Castesana)
  • Economical ecosystems
    • Socio-cultural aspects and ethics
    • Innovation for the 4P Industry
    • Complex systems for organizations
    • Risk management and crisis forecasting (Marija Jankovic and colleagues)
  • Emergent numerical solutions for the 4P industry
    • Intelligent Robots (Masanori Sugisaka)
    • Algorithms and architectures for artificial intelligence (Juan Julian Merelo)
    • Optimization, meta-heuristics and artificial intelligence for the industry
    • Secure Cyber-physical systems (Pierre Parrend)
    • Knowledge capitalization (Cecilia Zanni-Merk)
    • Artificial imaging and vision: fuzzy and stochastic learning for pattern recognition, graph and template-based approaches, predictors and multi-factor optimization. Applications are in particular in the domain of robot vision, biometry-based recognitions, drone vision

They will be completed according to the requirements and proposals of academic and industry partners.


[Auffray2010] Auffray, C., Charron, D., & Hood, L. (2010). Predictive, preventive, personalized and participatory medicine: back to the future. Genome Med, 2(8), 57.
[Hood2012] Hood, L., & Flores, M. (2012). A personal view on systems medicine and the emergence of proactive P4 medicine: predictive, preventive, personalized and participatory. New biotechnology, 29(6), 613-624.
[Clarkson2004] Clarkson, P. J., Simons, C., & Eckert, C. (2004). Predicting change propagation in complex design. Journal of Mechanical Design, 126(5), 788-797.
[Eckert2004] Eckert, C., Clarkson, P. J., & Zanker, W. (2004). Change and customisation in complex engineering domains. Research in Engineering Design, 15(1), 1-21.
[Clarkson2010] Clarkson, J., & Eckert, C. (2010). Design process improvement: a review of current practice. Springer.
[Keijzer2002] Keijzer, M., Merelo, J. J., Romero, G., & Schoenauer, M. (2002, January). Evolving objects: A general purpose evolutionary computation library. InArtificial Evolution (pp. 231-242). Springer Berlin Heidelberg.
[Arena2002] Arenas, M. G., Collet, P., Eiben, A. E., Jelasity, M., Merelo, J. J., Paechter, B., … & Schoenauer, M. (2002). A framework for distributed evolutionary algorithms. In Parallel Problem Solving from Nature—PPSN VII (pp. 665-675). Springer Berlin Heidelberg.
[Ramo2004] Ramos, V., & Merelo, J. J. (2004). Self-organized stigmergic document maps: Environment as a mechanism for context learning. arXiv preprint cs/0412075.
[Browning2001] Browning, T. R. (2001). Applying the design structure matrix to system decomposition and integration problems: a review and new directions.Engineering Management, IEEE Transactions on, 48(3), 292-306.
[Legrand2013] Legrand, V. (2013). Confiance et risque pour engager un échange en milieu hostile (Doctoral dissertation, Lyon, INSA).
[Sanin2009] Sanin, C., & Szczerbicki, E. (2009). Experience-based knowledge representation: SOEKS. Cybernetics and Systems: An International Journal,40(2), 99-122.
[Renaud2010] Renaud, D., Bouché, P., Gartiser, N., Zanni-Merk, C., & Michaud, H. P. (2010). Knowledge Transfer for Supporting the Organizational Evolution of SMEs: A Case Study. In Innovation through Knowledge Transfer (pp. 293-302). Springer Berlin Heidelberg.
[Zanjirani2009] Reza Zanjirani Farahani, Nasrin Asgari, Hoda Davarzani (2009). Supply chain and logistics in national, international and governmental environment, concept and models. Springer.
[Derouss2006] Deroussi, L., Gourgand, M., & Tchernev, N. (2006, October). Combining optimization methods and discrete event simulation: A case study in flexible manufacturing systems. In Service Systems and Service Management, 2006 International Conference on (Vol. 1, pp. 495-500). IEEE.
[Shibata2001] Shibata, K., Sugisaka, M., & Ito, K. (2001). Fast and stable learning in direct-vision-based reinforcement learning. target, 1, 5.
[Rousselot2012] Rousselot, F., Zanni-Merk, C., & Cavallucci, D. (2012). Towards a formal definition of contradiction in inventive design. Computers in Industry, 63(3), 231-242.
[Shafiq2014] SI Shafiq, C Sanin, E Szczerbicki, C Toro. Decisional DNA Based Framework for Representing Virtual Engineering Objects – Intelligent Information and Database Systems, 2014
[Isaksson2014] Isaksson, O., Lindroth, P., & Eckert, C. M. (2014). OPTIMISATION OF PRODUCTS VERSUS OPTIMISATION OF PRODUCT PLATFORMS: AN ENGINEERING CHANGE MARGIN PERSPECTIVE. In DS 77: Proceedings of the DESIGN 2014 13th International Design Conference.
[Rasoulifar2014] Rasoulifar, G., Eckert, C., & Prudhomme, G. (2014). Supporting communication between product designers and engineering designers in the design process of branded products: a comparison of three approaches. CoDesign, 10(2), 135-152.
[Wong2003] Wong, A. K. T. (2003). Before and Beyond Systems: An Empirical Modelling Approach (Doctoral dissertation, University of Warwick).
[Dulac2007] N. Dulac (2007). A framework for dynamic safety and risk management modeling in complex engineering systems (Doctoral dissertation, Massachusetts Institute of Technology).
[Lisý2012] Lisý, V., Píbil, R., Stiborek, J., Bosanský, B., & Pechoucek, M. (2012). Game-theoretic Approach to Adversarial Plan Recognition. In ECAI (pp. 546-551).
[Jaeger2014] Green growth in a complex world, ECCS’2014
[Adamic2014] The strange paths that information takes, ECCS’2014
[Anufriev2013] Anufriev, M., Hommes, C. H., & Philipse, R. H. (2013). Evolutionary selection of expectations in positive and negative feedback markets. Journal of Evolutionary Economics, 23(3), 663-688.
[Aberkane2014] L’économie de la connaissance est notre nouvelle renaissance, 04/06/2014, Hufftington Post


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