Secure collaborative supply-chain management

The SecureSCM project demonstrates the practical applicability of secure multiparty computation to online business collaboration. A prototype supply-chain management system protects the confidentiality of private data while rapidly adapting to changing business needs

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

Patient-reported outcomes in a trial of exenatide and insulin glargine for the treatment of type 2 diabetes

BACKGROUND: Patient-reported measures can be used to examine whether drug differences other than clinical efficacy have an impact on outcomes that may be important to patients. Although exenatide and insulin glargine appear to have similar efficacy for treatment of type 2 diabetes, there are several differences between the two treatments that could influence outcomes from the patient's perspective. The purpose of the current study was to examine whether the two drugs were comparable as assessed by patient-reported outcomes using data from a clinical trial in which these injectable medications were added to pre-existing oral treatment regimens. METHODS: Patients were randomized to either twice daily exenatide or once daily insulin glargine during a 26-week international trial. At baseline and endpoint, five patient-reported outcome measures were administered: the Vitality Scale of the SF-36, The Diabetes Symptom Checklist – Revised (DSC-R), the EuroQol EQ-5D, the Treatment Flexibility Scale (TFS), and the Diabetes Treatment Satisfaction Questionnaire (DTSQ). Change from baseline to endpoint was analyzed within each treatment group. Group differences were examined with General linear models (GLMs), controlling for country and baseline scores. RESULTS: A total of 549 patients with type 2 diabetes were enrolled in the trial, and current analyses were conducted with data from the 455 per protocol patients (228 exenatide and 227 insulin glargine). The sample was primarily Caucasian (79.6%), with slightly more men (55.2%) than women, and with a mean age of 58.5 years. Paired t-tests found that both treatment groups demonstrated statistically significant baseline to endpoint change on several of the health outcomes instruments including the DSC-R, DTSQ, and the SF-36 Vitality subscale. GLMs found no statistically significant differences between groups in change on the health outcomes instruments. CONCLUSION: This analysis found that both exenatide and insulin glargine were associated with significant improvements in patient-reported outcomes when added to oral medications among patients with type 2 diabetes. Despite an additional daily injection and a higher rate of gastrointestinal adverse events, treatment satisfaction in the exenatide group was comparable to that of the glargine group, possibly because of weight reduction observed in patients treated with exenatide