Portal:Complex Systems Digital Campus/E-Laboratory on the Factory of the Future

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Challenges of the 4P Factory[edit]

A new industrial revolution has started: the Factory of the Future is about to bring individual orders to high volume product lines, by supporting constant re-organization and product-driven manufacturing. In addition to the transfer of goods, it is conditioned by the efficient sharing, transfer and use of knowledge throughout the production line.

Top teams worldwide are performing focused and interdisciplinary research, gathering precious experience, producing disruptive innovation to enable the rapid embodiment of this revolution in the heterogeneous, rapidly evolving world of tomorrow’s Factory. It is therefore of critical importance that a structure being able to foster cross-disciplinary research AND innovation for the 4P factory supports and structure the current efforts of the community. The complex system approach provides a deep analytical framework to understand behaviors and shape solutions for the Industry of the Future.

elab objectives[edit]

The objective of this e-lab is to bring the power of complex systems to the factory floor: solutions for industrial engineering, economical ecosystems, and numerical tools will be gathered and referenced in a single place. Software, Data, Pedagogical resources and platforms will enable researchers, but also the industry, to get immediate advantage of the last advanced of complex systems.

The Future Factory is built by the interconnection of following elements:

  • The cyber-physical system, ie. computer-driven manufacturing robots and machines
  • The Internet of Things: each material and product becomes addressable
  • The Internet of Services: the factory becomes integrated in a top-down way (from the production line to management) and in a transversal way (from suppliers to customers)
  • The Internet of Humans: From to customers to designers, every stakeholder is integrated in the control loops of the factory

The current research frameworks are defined:

  • By the H2020 program and EFFRA association: focus is on the development of advanced processes, new materials, tight interconnection between enterprises, simulation tools, and training knowledge workers
  • By national or private organisms like the BMBF in Germany or the Gimelec in France. Key blockers identified by the BMBF are: security, lack of norms, missing competencies, insufficient network infrastructure, and cost. Core stakes listed by the Gimelec are: sensors, control/command systems, connected robots, suitable software libraries, logistics, cybersecurity, energetic cost.

The 4P revolution[edit]

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[edit]

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[edit]

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?

Tools[edit]

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 [Palma2012], prod chains [Deroussi2006], robotics [Shibata2001]
  • Rule #1: Knowledge exchange
    • Design Structure Matrix [Browning2001], game theory in distributed environments [Lisý2012]

Hot research questions[edit]

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[edit]

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.

Operational Objectives[edit]

The operational objectives of this e-lab are:

  • Creating contacts between academic partners for enabling publications and join projects in the domain of complex systems for the industry
  • Creating contacts between academic and industry partners for enabling join projects
  • Publishing research, technical reports and technical recommendations on the subject of the e-lab
  • Defining a training curriculum for Complex Systems for the Industry, and gathering open resources for making this curriculum available to the broad public
  • Setting up a gathering of software libraries for the Factory of the Future
  • Creating a who’s who of researchers and practitioners active in the field of the 4P Factory, available for members.

A monthly online meeting, as well as 2 workshops per year will support the implementation of this program.

The particularity of this e-lab is:

  • that is goal is to foster research AND innovation by creating suitable conditions for technology transfer between the academia and the industry, and between enterprises
  • that participation is open to industrial partners with membership agreement.

Members[edit]

The members of the e-laboratory are:

  • E-lab leader: Pierre Parrend, ECAM Strasbourg-Europe
  • Involved scientific teams
    • Europe
      • Center for complexity and design, Open University: Jeffrey Johnson, Claudia Eckert
      • ICube Laboratory, Strasbourg: Aline Deruyver, Cecilia Zanni-Merk
      • ECAM Strasbourg-Europe: Michel Risser, Pierre Parrend
      • INSA-Lyon: Véronique Legrand
      • Université de Clermont-Ferrand: Laurent Deroussi
      • University Granada (Spain): Juan Julian Merelo
    • South-America
      • University of Chile (Chile): Juan Velasquez
      • National University of Cuyo (UNCUYO), Mendoza, (Argentina): Ricardo Palma; Gustavo Masera
      • Institute of Environmental Research and Engineering, National University of San Martín (3iA-UNSAM), Buenos Aires (Argentina): Paula Castesana
    • Asia
      • Alife-Robotics (Japon): Masanori Sugisaka
    • Africa
      • Université Cheickh Antar Diop, Dakar (Senegal) : Ismaila Diouf
  • Scientific comitee:
    • Jeffrey Johnson (Open University)
    • Masanori Sugisaka (Alife Robotics)

References[edit]

  • [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).
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  • [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.
  • [Palma2012] Palma, R. R. (2012). Supply chain and logistics in national, international and governmental environment, concept and models. Journal of Computer Science & Technology, 12.
  • [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
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  • [Aberkane2014] L'économie de la connaissance est notre nouvelle renaissance, 04/06/2014, Hufftington Post