A Knowledge-Based Approach to Product Concept Screening

  • Marcin RelichEmail author
  • Antoni Śwíc
  • Arkadiusz Gola
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 373)


This paper is concerned with developing a knowledge-based approach for selecting portfolio of product concepts for development. The critical success factors for new product development are identified on the basis of information acquired from an enterprise system, including the fields of sales and marketing, project management, and production. The model of new product screening consists of enterprise functional domains and business information systems. The model has been described in terms of a constraint satisfaction problem (CSP) that contains a set of decision variables, their domains, and the constraints. Knowledge base is specified according to CSP framework and it reflects the company’s resources and relationships identified. The illustrative example presents the use of fuzzy neural network to estimating the success of new products and constraint programming to product concept screening in the context of the different search strategies.


project management new product development concept selection decision support system constraint satisfaction problem constraint programming 


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  1. 1.
    Sun, H., Wing, W.: Critical Success Factors for New Product Development in the Hong Kong Toy Industry. Technovation 25, 293–303 (2005)CrossRefGoogle Scholar
  2. 2.
    Spalek, S.: Does Investment in Project Management Pay Off? Industrial Management & Data Systems 114(5), 832–856 (2014)CrossRefGoogle Scholar
  3. 3.
    Chan, S.L., Ip, W.H.: A Dynamic Decision Support System to Predict the Value of Customer for New Product Development. Decision Support Systems 52, 178–188 (2011)CrossRefGoogle Scholar
  4. 4.
    Doskocil, R., Smolikova, L.: Knowledge Management as a Support of Project Management. In: International Scientific Conference on Knowledge for Market Use, pp. 40–48 (2012)Google Scholar
  5. 5.
    Relich, M.: Using ERP Database for Knowledge Acquisition: A Project Management Perspective. In: International Scientific Conference on Knowledge for Market Use, pp. 263–269 (2013)Google Scholar
  6. 6.
    Rossi, F., van Beek, P., Walsh, T.: Handbook of Constraint Programming. Elsevier Science (2006)Google Scholar
  7. 7.
    Bocewicz, G.: Robustness of Multimodal Transportation Networks. Eksploatacja i Niezawodnosc–Maintenance and Reliability 16(2), 259–269 (2014)Google Scholar
  8. 8.
    Li, T., Ruan, D.: An Extended Process Model of Knowledge Discovery in Database. Journal of Enterprise Information Management 20(2), 169–177 (2007)zbMATHGoogle Scholar
  9. 9.
    Woolliscroft, P., Relich, M., Caganova, D., Cambal, M., Sujanova, J., Makraiova, J.: The Implication of Tacit Knowledge Utilisation Within Project Management Risk Assessment. In: 10th International Conference of Intellectual Capital, Knowledge Management and Organisational Learning (ICICKM 2013), Washington, DC, pp. 645–652 (2013)Google Scholar
  10. 10.
    Do, N.A.D., Nielsen, I.E., Chen, G., Nielsen, P.: A Simulation-Based Genetic Algorithm Approach for Reducing Emissions from Import Container Pick-up Operation at Container Terminal. Annals of Operations Research (2014) (article in press)Google Scholar
  11. 11.
    Banaszak, Z., Zaremba, M., Muszynski, W.: Constraint Programming for Project-Driven Manufacturing. International Journal of Production Economics 120, 463–475 (2009)CrossRefGoogle Scholar
  12. 12.
    Sitek, P., Wikarek, J.: A Hybrid Approach to Supply Chain Modeling and Optimization. In: Federated Conference on Computer Science and Information Systems, pp. 1223–1230 (2013)Google Scholar
  13. 13.
    Van Roy, P., Haridi, S.: Concepts, Techniques and Models of Computer Programming. Massachusetts Institute of Technology (2004)Google Scholar
  14. 14.
    Grzybowska, K., Kovács, G.: Logistics Process Modelling in Supply Chain – Algorithm of Coordination in the Supply Chain – Contracting. In: de la Puerta, J.G., et al. (eds.) International Joint Conference SOCO’14-CISIS’14-ICEUTE’14. AISC, vol. 299, pp. 311–320. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  15. 15.
    Grzybowska, K., Awasthi, A., Hussain, M.: Modeling Enablers for Sustainable Logistics Collaboration Integrating Canadian and Polish Perspectives. In: The Federated Conference on Computer Science and Information Systems, pp. 1311–1319 (2014)Google Scholar
  16. 16.
    Sitek, P., Wikarek, J.: Hybrid Solution Framework for Supply Chain Problems. In: Omatu, S., Bersini, H., Corchado Rodríguez, J.M., González, S.R., Pawlewski, P., Bucciarelli, E. (eds.) Distributed Computing and Artificial Intelligence 11th International Conference. AISC, vol. 290, pp. 11–18. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  17. 17.
    Baptiste, P., Le Pape, C., Nuijten, W.: Constraint-Based Scheduling: Applying Constraint Programming to Scheduling Problems. Kluwer Academic Publishers, Norwell, Massachusetts (2001)CrossRefGoogle Scholar
  18. 18.
    Relich, M.: Identifying Relationships Between Eco-innovation and Product Success. In: Golinska, P., Kawa, A. (eds.) Technology Management for Sustainable Production and Logistics, pp. 173–192. Springer, Heidelberg (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of Economics and ManagementUniversity of Zielona GoraZielona GoraPoland
  2. 2.Faculty of Mechanical EngineeringLublin University of TechnologyLublinPoland
  3. 3.Faculty of ManagementLublin University of TechnologyLublinPoland

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