Agent-Based Digital Networking in Furniture Manufacturing Enterprises

  • Anthony Karageorgos
  • Dimitra Avramouli
  • Christos Tjortjis
  • Georgios Ntalos
Part of the Communications in Computer and Information Science book series (CCIS, volume 88)


International competition and varying customer needs commonly cause small and medium furniture manufacturing enterprises to join dynamically- formed, ‘smart’ enterprise networks, established and operating using digital information technologies. In this paper, we propose a technological approach to support such enterprise networks which is primarily based on the use of software agents. First we outline the reasons motivating networking in furniture manufacturing enterprises and we briefly present core smart enterprise network concepts. Subsequently, we provide an overview of the main technologies currently used to support enterprise networks, and we make the case for utilising service-orientation and adaptive, (semi-) autonomous software components, such as software agents. Furthermore, we propose a four-tier software architectural framework based on software agents and web services, and we briefly describe the requirements, the architecture and main features of the e-Furn software system, which is based on that framework. Finally, we discuss the intelligent recommendation feature of e-Furn.


Smart Business Networks Enterprise Networks Software Agents Multi-Agent Systems Web Services Furniture Manufacturing Data Mining 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Databank: ICT and e-Business Impact in the Furniture Industry. Impact Study No 3/2008 (2008) Google Scholar
  2. 2.
    Camarinha-Matos, L.M.: Collaborative Networks In Industry Trends and Foundations. In: Cunha, P.F., Maropoulos, P.G. (eds.) Digital Enterprise Technology, pp. 45–56. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  3. 3.
    van Heck, E., Vervest, P.: Smart business networks: how the network wins. Communications of the ACM 50, 28–37 (2007)CrossRefGoogle Scholar
  4. 4.
    van Heck, E., Vervest, P.: Smart business networks: Concepts and empirical evidence Decision Support Systems 47, 275–276 (2009)Google Scholar
  5. 5.
    Vervest, P.H.M., van Liere, D.W., Dunn, A.: The Network Factor - How to Remain Competitive. In: Vervest, P.H.M., van Liere, D.W., Zheng, L. (eds.) The Network Experience, pp. 15–35. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Vervest, P.H.M., van Heck, E., Pau, L.F., Preiss, K.: Smart Business Networks. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Vervest, P.H.M., van Heck, E., Preiss, K.: Smart Business Networks: A New Business Paradigm. In: SBNi Discovery Session, p. 529Google Scholar
  8. 8.
    Xiao, L., Zheng, L.: Achieving Quick Connect with the Support of Semantic Web. In: SBNi Discovery Session 2006. Vanenburg Castle in Putten, The Netherlands (2006)Google Scholar
  9. 9.
    Werthner, H., Fodor, O., Herzog, M.: Web Information Extraction and Mediation as a Basis for Smart Business Networking. In: Vervest, P., van Heck, E., Pau, L.-F., Preiss, K. (eds.) Smart Business Networks, pp. 405–419. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    van Hillegersberg, J., Boeke, R., van de Heuvel, W.-J.: The Potential of Webservices to Enable Smart Business Networks. In: van Peter Vervest, E.H., Pau, L.-F., Preiss, K. (eds.) Smart Business Networks, vol. 4, pp. 349–362. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Busquets, J., Rodona, J., Warehama, J.: Adaptability in smart business networks: An exploratory case in the insurance industry Smart Business Networks. Concepts and Empirical Evidence 47, 287–296 (2009)Google Scholar
  12. 12.
    Rodon, J., Busquets, X., Christiaanse, E.: Orchestration in ICT-enabled Business Networks: A Case in the Repairs Industry. In: 18th Bled eConference eIntegration in Action. Bled, Slovenia (2005)Google Scholar
  13. 13.
    Pramatari, K., Doukidis, G.: Intelligent Integration of Supply Chain Processes based on Unique Product Identification and a Distributed Network Architecture. In: SBNi Discovery Session, p. 369Google Scholar
  14. 14.
    Pramatari, K., Doukidis, G.I., Kourouthanassis, P.: Towards Smarter Supply and Demand Chain Collaboration Practices Enabled by RFID Technology. In: Vervest, P.H.M., van Heck, E., Pau, L.-F., Preiss, K. (eds.) Smart Business Networks, vol. Section 2, pp. 197–210. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    van Oosterhout, M., Koenen, E., van Heck, E.: Empirical Evidence from a Business Experiment with Small and Medium Enterprises in the Netherlands The Adoption of Grid Technology and Its Perceived Impact on Agility. In: Vervest, P.H.M., Liere, D.W., Zheng, L. (eds.) The Network Experience, pp. 285–299 (2009)Google Scholar
  16. 16.
    Boden, T.: The Grid Enterprise — Structuring the Agile Business of the Future. BT Technology Journal 22, 107–117 (2004)CrossRefGoogle Scholar
  17. 17.
    Chen, X., Duan, G., Sun, Y., Gu, J.: Research on Key Technologies for Grid-Based Network Collaborative Design. In: Fourth International Conference on Networked Computing and Advanced Information Management, pp. 639–644. IEEE, Los Alamitos (2008)CrossRefGoogle Scholar
  18. 18.
    Collins, J., Ketter, W., Gini, M.: Flexible Decision Support in a DynamicBusiness Network. In: The Network Experience, Part 4, pp. 233–248. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  19. 19.
    Douma, A., Moonen, H., van Hillegersberg, J., van de Rakt, B., Schutten, M.: Designing an Agent-Based Inter-Organizational Coordination System for Planning and Control of Container Barges in the Port of Rotterdam. In: SBNi Discovery Session 2006. Vanenburg Castle in Putten, The Netherlands (2006)Google Scholar
  20. 20.
    Ketter, W., Collins, J., Gini, M., Gupta, A., Schrater, A.P.: Strategic Sales Management Guided By Economic Regimes. In: Smart Business Networks A new Business Paradigm (2008)Google Scholar
  21. 21.
    McAfee, A.: Will Web Services Really Transform Collaboration? MIT Sloan Management Review 46, 78–84 (2005)Google Scholar
  22. 22.
    Schroth, C., Janner, T.: Web 2.0 and SOA: Converging Concepts Enabling the Internet of Services 9, 36–41 (2007)Google Scholar
  23. 23.
    Laurent, W.: The Importance of SOA Governance. DM Review 17, 38–38 (2007)Google Scholar
  24. 24.
    Dominic, G., Margaret, L., Ashok, M., Hiroki, S.: The IEEE FIPA approach to integrating software agents and web services. In: Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems. ACM, Honolulu (2007)Google Scholar
  25. 25.
    Greenwood, D., Calisti, M.: Engineering web service-agent integration. In: Proceedings of the International Conference on Systems, Man and Cybernetics (SMC 2004), The Hague, The Netherlands, pp. 1918–1925 (2004)Google Scholar
  26. 26.
    Nguyen, X., Kowalczyk, R., Chhetri, M., Grant, A.: WS2JADE: A Tool for Run-time Deployment and Control of Web Services as JADE Agent Services. In: Software Agent-Based Applications, Platforms and Development Kits, pp. 223–251 (2005)Google Scholar
  27. 27.
    Ramírez, E., Brena, R.: Integrating agent technologies into enterprice systems Using Web Services. Enterprise Information Systems VII, 223–227 (2006)Google Scholar
  28. 28.
    Blacoe, I., Portabella, D.: Guidelines for the integration of agent-based services and web-based services (2005)Google Scholar
  29. 29.
    Hendler, J.: Agents and the Semantic Web. IEEE Intelligent Systems 16, 30–37 (2001)CrossRefGoogle Scholar
  30. 30.
    García-Sánchez, F., Alvarez Sabucedo, L., Martínez-Béjar, R., Anido Rifón, L., Valencia-García, R., Gómez, J.: A Knowledge Technologies-Based Multi-agent System for eGovernment Environments. In: Service-Oriented Computing: Agents, Semantics and Engineering, pp. 15–30 (2008)Google Scholar
  31. 31.
    Karageorgos, A., Avramouli, D., Ntalos, G., Tjortjis, C., Vasilopoulou, K.: Towards Agent-based ‘Smart’ Collaboration in Enterprise Networks. In: 8th Int’l Workshop on Agent-based Computing for Enterprise Collaboration (ACEC) at WETICE 2010, Larissa, Greece. IEEE Computer Society Press, Los Alamitos (2010)Google Scholar
  32. 32.
    Carrascosa, C., Giret, A., Julian, V., Rebollo, M., Argente, E., Botti, V.: Service Oriented MAS: An open architecture. In: Proc. of 8th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS), pp. 1291–1292. IEEE Press, Los Alamitos (2009)Google Scholar
  33. 33.
    Giret, A., Julian, V., Rebollo, M., Argente, E., Carrascosa, C., Botti, V.: An Open Architecture for Service-Oriented Virtual Organizations. In: Seventh International Workshopon Programming Multi-Agent Systems, PROMAS 2009, pp. 23–33. Springer, Budapest (2009)Google Scholar
  34. 34.
    Karageorgos, A., Mehandjiev, N., Thompson, S.: RAMASD: a semi-automatic method for designing agent organisations. The Knowledge Engineering Review 17, 331–358 (2002)CrossRefGoogle Scholar
  35. 35.
    Stuit, M., Szirbik, N.B.: Towards Agent-Based Modeling and Verification of Collaborative Business Processes: An Approach Centered on Interactions and Behaviors. International Journal of Cooperative Information Systems 18, 423–479 (2009)CrossRefGoogle Scholar
  36. 36.
    Dong, L., Tjortjis, C.: Experiences of Using a Quantitative Approach for Mining Association Rules. In: Liu, J., Cheung, Y.-m., Yin, H. (eds.) IDEAL 2003. LNCS, vol. 2690, pp. 693–700. Springer, Heidelberg (2003)Google Scholar
  37. 37.
    Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 2nd edn., November 3, 2005. Morgan Kaufmann, San Fransisco (2006)Google Scholar
  38. 38.
    Wang, C., Tjortjis, C.: PRICES: An Efficient Algorithm for Mining Association Rules. In: Yang, Z.R., Yin, H., Everson, R.M. (eds.) IDEAL 2004. LNCS, vol. 3177, pp. 352–358. Springer, Heidelberg (2004)Google Scholar
  39. 39.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar
  40. 40.
    Rokach, L., Maimon, O.: Top-down induction of decision trees classifiers - a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 35, 476–487 (2005)CrossRefGoogle Scholar
  41. 41.
    Tjortjis, C., Keane, J.: T3: A Classification Algorithm for Data Mining. In: Yin, H., Allinson, N.M., Freeman, R., Keane, J.A., Hubbard, S. (eds.) IDEAL 2002. LNCS, vol. 2412, pp. 50–55. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  42. 42.
    Cumb, C., Fano, A., Ghani, R., Krema, M.: Predicting customer shopping lists from point-of-sale purchase data. In: Proc. of the 10th ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining, Seattle, WA, USA, August 22-25 (2004)Google Scholar
  43. 43.
    Adomavicius, G., Tuzhilin, A.: Using Data Mining Methods to Build Customer Profiles. Computer 34(2), 74–82 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Anthony Karageorgos
    • 1
  • Dimitra Avramouli
    • 1
  • Christos Tjortjis
    • 2
    • 3
  • Georgios Ntalos
    • 1
  1. 1.TEI of Larissa, Karditsa BranchKarditsaGreece
  2. 2.University of IoanninaIoanninaGreece
  3. 3.University of Western MacedoniaKozaniGreece

Personalised recommendations