Responsive Production in Manufacturing: A Modular Architecture

[EN] This paper proposes an architecture aiming at promoting the convergence of the physical and digital worlds, through CPS and IoT technologies, to accommodate more customized and higher quality products following Industry 4.0 concepts. The architecture combines concepts such as cyber-physical systems, decentralization, modularity and scalability aiming at responsive production. Combining these aspects with virtualization, contextualization, modeling and simulation capabilities it will enable self-adaptation, situational awareness and decentralized decision-making to answer dynamic market demands and support the design and reconfiguration of the manufacturing enterprise.The research leading to these results has received funding from the European Union H2020 project C2 NET (FoF-01-2014) nr 636909.Marques, M.; Agostinho, C.; Zacharewicz, G.; Poler, R.; Jardim-Goncalves, R. (2018). Responsive Production in Manufacturing: A Modular Architecture. Studies in Systems, Decision and Control. 140:231-254. https://doi.org/10.1007/978-3-319-78437-3_10S231254140European Commission: For a European Industrial Renaissance, Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions (2014)Hartmann, B., King, W.P., Narayanan, S.: Digital manufacturing: the revolution will be virtualized. McKinsey & Company (2015)European Forum for Manufacture: Driving Innovation and Growth in European Manufacturing (2015)European Factories of the Future Research Association (EFFRA): Factories of the Future: Multi-annual Roadmap for the Contractual PPP under the Horizon 2020 (2013)European Commission: Horizon 2020—Work Programme 2016–2017: 17. Cross-cutting Activities (2016)Schlaepfer, R.C., Koch, M., Merkofer, P.: Industry 4.0 challenges and solutions for the digital transformation and use of exponential technologies. Deloitte AG (2015)7iD: Industry 4.0. https://www.7id.com/technology/industry-4-0/ (2016)European Commission: Horizon 2020—Work Program 2016-2017—Cross-cutting Activities, 25 July 2016EFFRA: Factories of the Future: Multi-annual Roadmap for the Contractual PPP under the Horizon 2020 (2013)FInES Research Roadmap Task Force (2012)Jacinto, J.: Smart manufacturing? Industry 4.0? What’s it all about? Siements Totally Integrated Automation, Automation World & Design World (2014)Monostori, L.: Cyber-physical production systems: roots, expectations and R&D challenges. Procedia CIRP 17, 9–13 (2014)Adolphs, P.: RAMI 4.0—An architectural Model for Industrie 4.0. Platform Industrie 4.0 (2015)Collins, M.: Why America has a shortage of skilled workers. Industry Week (2015)Forbes, J., Naujok, N., Geissbauer, R., Vedso, J., Schrauf, S.: Industry 4.0: building the digital enterprise. PWC (2016)World Economic Forum Industrial Internet Survey (2014)Chen, D., Vernadat, F.B.: Enterprise interoperability: a standardisation view. Enterprise Inter- and Intra-Organizational Integration, Volume 108 of the series IFIP—The International Federation for Information Processing, pp. 273–282 (2003)Yan, L., Li, Z., Yuan, X.: Study on method-of-robust-multidisciplinary-design-collaborative-decision for product design. Inf. Technol. J. 8(4), 441–452 (2009)Ruiz Dominguez, G. A.: Caractérisation de l’activité de conception collaborative à distance: étude des effets de synchronisation cognitive (2005)Jung, J.J.: Reusing ontology mappings for query routing in semantic peer-to-peer environment. Inf. Sci. (2010). https://doi.org/10.1016/j.ins.2010.04.018Ranjan, R., Zhao, L., Wu, X., Liu, A., Quiroz, A., Parashar, M.: Peer-to-Peer Cloud Provisioning: Service Discovery and Load-Balancing. https://doi.org/10.1007/978-1-84996-241-4_12Agostinho, C., Pinto, P., Jardim-goncalves, R.: Dynamic adaptors to support model-driven interoperability and enhance sensing enterprise networks. In: 19th World Congress of the International Federation of Automatic Control (IFAC’14), Cape Town, South Africa (2014)Chen, D., Doumeingts, G., Vernadat, F.: Architectures for enterprise integration and interoperability: past, present and future. Comput. Ind. 59, 647–659 (2008). https://doi.org/10.1016/j.compind.2007.12.016Ducq, Y., Chen, D., Alix, T.: Principles of servitization and definition of an architecture for model driven service system engineering. In: 4th International IFIP Working Conference on Enterprise Interoperability (IWEI 2012), Harbin, China, 2012. https://doi.org/10.1007/978-3-642-33068-17_12Elvesæter, B., Hahn, A., Berre, A., Neple, T.: Towards an interoperability framework for model-driven development of software systems. In: 1st International Conference on Interoperability Enterprise Software and Applications. Springer. http://www.springerlink.com/index/L10NU4306N054T6G.pdf (2005)OMG: MDA Guide Version 1.0.1 (omg/2003-06-01), Object Management Group. http://www.omg.org/cgibin/doc?omg/03-06-01.pdf (2003)Agostinho, C., Ducq, Y., Zacharewicz, G., Sarraipa, J., Lampathaki, F., Poler, R., Jardim-Goncalves, R.: Towards a sustainable interoperability in networked enterprise information systems: trends of knowledge and model-driven technology. Comput. Ind. (2015). https://doi.org/10.1016/j.compind.2015.07.001Santucci, G., Martinez, C., Vlad-câlcic, D.: The sensing enterprise. In: FInES Work. FIA 2012, Aalborg, Denmark. http://www.theinternetofthings.eu/sites/default/files/%5Buser-name%5D/Sensing-enterprise.pdf (2012)Sriram, R.: Smart networked systems and societies: what will the future look like? In: IEEE IT Professional Conference (IT Pro). IEEE Computer Society (2014)Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., et al.: Big data: the next frontier for innovation, competition, and productivity. http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation (2011)Zacharewicz, G., Diallo, S., Ducq, Y., Agostinho, C., Jardim-Goncalves, R., Bazoun, H., Wang, Z., Doumeingts, G.: Model-based approaches for interoperability of next generation enterprise information systems: state of the art and future challenges. Inf. Syst. e-Bus. Manag. (2016). https://doi.org/10.1007/s10257-016-0317-8Jardim-Goncalves, R., Agostinho, C., Steiger-Garcao, A.: A reference model for sustainable interoperability in networked enterprises: towards the foundation of EI science base. Int. J. Comput. Integr. Manuf. 25(10) (2012). (Special Issue on Collaborative Manufacturing and Supply Chains). https://doi.org/10.1080/0951192x.2011.653831Schatsky, D., Muraskin, C.: Blockchain is coming to disrupt your industry. Deloitte (2015)Shi, J., Wan, J., Yan, H., Suo, H.: A survey of cyber-physical systems. In: International Conference on Wireless Communications and Signal Processing, pp. 1–6 (2011)Rajkumar, R.: Workshop report on foundations for innovation in cyber-physical systems. NIST. http://www.nist.gov/el/upload/CPS-WorkshopReport-1-30-13-Final.pdf/ (2013)Lee, J., Lapira, E., Yang, S. Kao, H.-A.: Predictive manufacturing system trends of next generation production systems. In: 11th IFAC Workshop on Intelligent Manufacturing Systems, vol. 11, issue 1, pp. 150–156 (2013)IDC: The digital universe of opportunities: rich data and increasing value of the internet of things. EMC Digital Universe. emc.com/collateral/analyst-reports/idc-digital-universe-2014.pdf . (2014)Baheti, R., Gill, H.: Cyber-physical systems. Impact Control Technol. 1–6 (2011)Lee, J., Bagheri, B., Kao, H.-A.: A cyber physical systems architecture for Industry 4.0-based manufacturing system. Manuf. Lett. 2015, 3, 18–23 (2014). https://doi.org/10.1016/j.mfglet.2014.12.001Bagheri, B., Lee, J.: Big future for cyber-physical manufacturing systems. Design World. http://www.designworldonline.com/big-future-for-cyber-physical-manufacturing-systems/ (2015)Lucke, D., Constantinescu, C., Westkämper, E.: Smart factory-a step towards the next generation of manufacturing. Manufacturing Systems and Technologies for the New Frontier, pp. 115–118. Springer, London (2008)Weiser, M.: The Computer for the 21st Century. Scientific American, Special Issue on Communications. Comput. Netw. (1991)Westkämper, E., Jendoubi, L., Eissele, M., Ertl, T.: Smart factory—bridging the gap between digital planning and reality. Manuf. Syst. 35(4), 307–314 (2006)Goryachev, A., Kozhevnikov, S., Kolbova, E., Kuznetsov, O., Simonova, E., Skobelev, P., Tsarev, A., Shepilov, Y.: Smart factory: intelligent system for workshop resource allocation, scheduling, optimization and controlling in real time. Adv. Mater. Res. 630, 508–513 (2012)Agostinho, C., Marques-Lucena, C., Sesana, M., Felic, A., Fischer, K., Rubattino, C., Sarraipa, J.: Osmosis process development for innovative product design and validation. 2015 ASME IMECE, Houston, USA (2015)Ko, J., Lee, B., Lee, K., Hong, S.G., Kim, N., Paek, J.: Sensor virtualization module: virtualizing IoT devices on mobile smartphones for effective sensor data management. Int. J. Distrib. Sens. Netw. (2015). https://doi.org/10.1155/2015/730762Guo, T., Papaioannou, T.G., Aberer, K.: Efficient indexing and query processing of model-view sensor data in the cloud. J. Big Data Res. 1, 52–65 (2014)Kumra, S., Sharma, L., Khanna, Y., Chattri, A.: Analysing an industrial automation pyramid and providing service oriented architecture. Int. J. Eng. Trends Technol. 3(5), 586–594 (2012)Endsley, M.: Design and evaluation for situational awareness enhancement. In: Proceedings of the Human Factors Society 32nd Annual Meeting. HFES, Santa Monica, pp. 97–10 (1988)Stanton, N.A., Chambers, P.R., Piggott, J.: Situational awareness and safety. Saf. Sci. 39(3), 189–204 (2001)Endsley, M.: Toward a theory of situation awareness in dynamic systems. Hum. Factors (The Journal of the Human Factors and Ergonomics Society) 37, 32–64 (1995)Bedny, G., Meister, D.: Theory of activity and situation awareness. Int. J. Cogn. Ergon. 3(1), 63–72 (1999)Smith, K., Hancock, P.A.: Situation awareness is adaptive, externally directed consciousness. Hum. Factors (The Journal of the Human Factors and Ergonomics Society) 37(1), 137–148 (1995)Ranganathan, A., Campbell, R.H.: An infrastructure for context-awareness based on first order logic. Pers. Ubiquit. Comput. 7(6), 353–364 (2003)Ning, K., Scholze, S., Marques, M., Campos, A, Neves-Silva, R. O’Sullivan, D.: A service oriented platform for context aware knowledge enhancing. In: 5th IFAC Conference on Management and Control of Production and Logistics (2010)Marques, M., Sucic, B., Vuk, T.: Context-based decision support for sustainable optimization of energy consumption. KES Trans. Sustain. Des. Manuf. 1(1), 899–910 (2014)Schneeweiss, C.: Distributed decision making in supply chain management. Int. J. Product. Econ. 84, 71–83 (2003)Alemany, M.M.E., Alarcón, F., Lario, F.C., Boj, J.J.: An application to support the temporal and spatial distributed decision-making process in supply chain collaborative planning. Comput. Ind. 62(5), 519–540 (2011). https://doi.org/10.1016/j.compind.2011.02.002Hong, I.H., Ammons, J.C., Realff, M.J.: Centralized versus decentralized decision-making for recycled material flows. Environ. Sci. Technol. 42(4), 1172–1177 (2008)Pibernik, R., Sucky, E.: An approach to inter-domain master planning in supply chains. Int. J. Product. Econ. 108, 200–212 (2007). https://doi.org/10.1016/j.ijpe.2006.12.010Lee, H., Whang, S.: Decentralized multi-echelon supply chains: incentives and information. Manag. Sci. 45(5), 633–640 (1999)Jung, H., Chen, F., Jeong, B.: Decentralized supply chain planning framework for third party logistics partnership. Comput. Ind. Eng. 55(2), 348–364 (2008). https://doi.org/10.1016/j.cie.2007.12.017Wang, K.-J., Chen, M.-J.: Cooperative capacity planning and resource allocation by mutual outsourcing using ant algorithm in a decentralized supply chain. Expert Syst. Appl. 36(2), 2831–2842 (2009)Simon, H.A.: The Science of the Artificial, 1st edn. MIT Press, Cambridge, Mass, (1969). (3rd ed. in 1996, MIT Press)Mesarovic, M.D., Masko, D., Takahara, Y.: Theory of Hierarchical Multilevel Systems. Academic Press, New York and London (1970)Camarinha-Matos, L.M., Afsarmanesh, H.J.: Collaborative networks: a new scientific discipline. J. Intell. Manuf. 16(4), 439–452 (2005)Popplewell, K., Stojanovic, N., Abecker, A., Apostolou, D., Mentzas, G., Harding, J.: Supporting adaptive enterprise collaboration through semantic knowledge services. In: Enterprise Interoperability Iii: New Challenges and Industrial Approaches, pp. 381–393 (2008). http://doi.org/10.1007/978-1-84800-221-0_30Agostinho, C., Ducq, Y., Zacharewicz, G., Sarraipa, J., Lampathaki, F., Jardim-Goncalves, R., Poler, R.: Towards a sustainable interoperability in networked enterprise information systems: trends of knowledge and model-driven technology. Accepted for Publication at Computers in Industry. http://doi.org/10.1016/j.compind.2015.07.001Agostinho, C., Jardim-Gonçalves, R.: Sustaining interoperability of networked liquid-sensing enterprises: a complex systems perspective. Annu. Rev. Control 39, 128–143 (2015). https://doi.org/10.1016/j.arcontrol.2015.03.012Weichhart, G., Molina, A., Chen, D., Whitman, L. E., Vernadat, F.: Challenges and current developments for sensing, smart and sustainable enterprise systems. Computers in Industry (2015). http://doi.org/10.1016/j.compind.2015.07.002Weichhart, G.: Supporting Interoperability for Chaotic and Complex Adaptive Enterprise Systems. On the Move to Meaningful Internet Systems: OTM 2013 Workshops. Confederated International Workshops: OTM Academy, OTM Industry Case Studies Program, ACM, EI2N, ISDE, META4eS, ORM, SeDeS, SINCOM, SMS, and SOMOCO 2013. Proceedings: LNCS 8186, 86–92. (2013). http://doi.org/10.1007/978-3-642-41033-8_14Truex, D.P., Baskerville, R., Klein, H.: Growing systems in emergent organizations. Mag. Commun. ACM CACM Homepage Arch. 42(8), 117–123 (1999)Weiberg, S.: Facilitating collaborative decision-making in six steps. International Association of Facilitators Annual Meeting, pp. 14–15 (1999)Delbecq, A.L., VandeVen, A.H.: A group process model for problem identification and program planning. J. Appl. Behav. Sci. 7, 466–492 (1971). https://doi.org/10.1177/002188637100700404Saaty, T.L.: The Analytic Hierarchy Process. McGraw-Hill, New York, USA (1980

Insights from computational modeling in inflammation and acute rejection in limb transplantation

Acute skin rejection in vascularized composite allotransplantation (VCA) is the major obstacle for wider adoption in clinical practice. This study utilized computational modeling to identify biomarkers for diagnosis and targets for treatment of skin rejection. Protein levels of 14 inflammatory mediators in skin and muscle biopsies from syngeneic grafts [n = 10], allogeneic transplants without immunosuppression [n = 10] and allografts treated with tacrolimus [n = 10] were assessed by multiplexed analysis technology. Hierarchical Clustering Analysis, Principal Component Analysis, Random Forest Classification and Multinomial Logistic Regression models were used to segregate experimental groups. Based on Random Forest Classification, Multinomial Logistic Regression and Hierarchical Clustering Analysis models, IL-4, TNF-α and IL-12p70 were the best predictors of skin rejection and identified rejection well in advance of histopathological alterations. TNF-α and IL-12p70 were the best predictors of muscle rejection and also preceded histopathological alterations. Principal Component Analysis identified IL-1α, IL-18, IL-1β, and IL-4 as principal drivers of transplant rejection. Thus, inflammatory patterns associated with rejection are specific for the individual tissue and may be superior for early detection and targeted treatment of rejection. © 2014 Wolfram et al

So what do we really mean when we say that systems biology is holistic?

Background: An old debate has undergone a resurgence in systems biology: that of reductionism versus holism. At least 35 articles in the systems biology literature since 2003 have touched on this issue. The histories of holism and reductionism in the philosophy of biology are reviewed, and the current debate in systems biology is placed in context. Results: Inter-theoretic reductionism in the strict sense envisaged by its creators from the 1930s to the 1960s is largely impractical in biology, and was effectively abandoned by the early 1970s in favour of a more piecemeal approach using individual reductive explanations. Classical holism was a stillborn theory of the 1920s, but the term survived in several fields as a loose umbrella designation for various kinds of anti-reductionism which often differ markedly. Several of these different anti-reductionisms are on display in the holistic rhetoric of the recent systems biology literature. This debate also coincides with a time when interesting arguments are being proposed within the philosophy of biology for a new kind of reductionism. Conclusions: Engaging more deeply with these issues should sharpen our ideas concerning the philosophy of systems biology and its future best methodology. As with previous decisive moments in the history of biology, only those theories that immediately suggest relatively easy experiments will be winners

A computational framework for complex disease stratification from multiple large-scale datasets.

BACKGROUND: Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. METHODS: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. RESULTS: We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. CONCLUSIONS: This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine