Knowledge Discovery in Enterprise Databases for Forecasting New Product Success

  • Marcin RelichEmail author
  • Krzysztof Bzdyra
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9375)


This paper presents the knowledge discovery process that aims to improve the forecast quality of the success of new product development projects. 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, research and development, production, and project management. The proposed knowledge discovery process consists of stages such as data selection from enterprise databases, data preprocessing, data mining, and the use of the discovered patterns for forecasting new product success. The illustrative example presents the use of fuzzy neural networks for forecasting net profit from new products.


Knowledge retrieval Data mining Fuzzy neural systems New product development Rule-based systems 


  1. 1.
    Cios, K.J., Kurgan, L.A.: Trends in data mining and knowledge discovery. In: Pal, N., Jain, L. (eds.) Advanced Techniques in Knowledge Discovery, pp. 1–26. Springer, London (2005)Google Scholar
  2. 2.
    Li, T., Ruan, D.: An extended process model of knowledge discovery in database. J. Enterp. Inf. Manage. 20(2), 169–177 (2007)CrossRefGoogle Scholar
  3. 3.
    Trott, P.: Innovation Management and New Product Development. Prentice Hall, Essex (2005)Google Scholar
  4. 4.
    Spalek, S.: Does investment in project management pay off? Ind. Manage. Data Syst. 114(5), 832–856 (2014)CrossRefGoogle Scholar
  5. 5.
    Chan, S.L., Ip, W.H.: A dynamic decision support system to predict the value of customer for new product development. Decis. Support Syst. 52, 178–188 (2011)CrossRefGoogle Scholar
  6. 6.
    Mishra, S., Kim, D., Lee, D.: Factors affecting new product success: cross-country comparisons. J. Prod. Innov. Manage 13(6), 530–550 (1996)CrossRefGoogle Scholar
  7. 7.
    Lynn, G., Schnaars, S., Skov, R.: A survey of new product forecasting practices in industrial high technology and low technology businesses. Ind. Mark. Manage. 28(6), 565–571 (1999)CrossRefGoogle Scholar
  8. 8.
    Ernst, H.: Success factors of new product development: a review of the empirical literature. Int. J. Manage. Rev. 4(1), 1–40 (2002)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Relich, M.: Knowledge acquisition for new product development with the use of an ERP database. In: Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 1285–1290 (2013)Google Scholar
  10. 10.
    Hardie, B., Fader, P., Wisniewski, M.: An empirical comparison of new product trial forecasting models. J. Forecast. 17, 209–229 (1998)CrossRefGoogle Scholar
  11. 11.
    Kahn, K.: An exploratory investigation of new product forecasting practices. J. Prod. Innov. Manage 19, 133–143 (2002)CrossRefGoogle Scholar
  12. 12.
    Fayyad, U., Piatetsky-Shapiro, G., Smith, P.: From data mining to knowledge discovery in databases. Am. Assoc. Artif. Intell. 37–54 (1996). FallGoogle Scholar
  13. 13.
    Cabena, P., Hadjinian, P., Stadler, R., Verhees, J., Zanasi, A.: Discovering Data Mining: From Concepts to Implementation. Prentice Hall, Saddle River (1998)Google Scholar
  14. 14.
    Marban, O., Mariscal, G., Segovia, J.: A data mining & knowledge discovery process model. In: Data Mining and Knowledge Discovery in Real Life Applications. I-Tech (2009)Google Scholar
  15. 15.
    Han, J., Kamber, M.: Data Mining. Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2006)zbMATHGoogle Scholar
  16. 16.
    Hudec, M., Vujosevic, M.: Integration of data selection and classification by fuzzy logic. Expert Syst. Appl. 39, 8817–8823 (2012)CrossRefGoogle Scholar
  17. 17.
    Relich, M., Muszynski, W.: The use of intelligent systems for planning and scheduling of product development projects. Procedia Comput. Sci. 35, 1586–1595 (2014)CrossRefGoogle Scholar
  18. 18.
    Gola, A., Świć, A.: Computer-aided machine tool selection for focused flexibility manufacturing systems using economical criteria. Actual Probl. Econ. 10(124), 383–389 (2011)Google Scholar
  19. 19.
    Relich, M., Pawlewski, P.: A multi-agent system for selecting portfolio of new product development projects. In: Bajo, J., Hallenborg, K., Pawlewski, P., Botti, V., Sánchez-Pi, N., Duque Méndez, N.D., Lopes, F., Vicente, J. (eds.) PAAMS 2015 Workshops. CCIS, vol. 524, pp. 102–114. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  20. 20.
    Sitek, P.: A hybrid CP/MP approach to supply chain modelling, optimization and analysis. In: Federated Conference on Computer Science and Information Systems, pp. 1345–1352 (2014)Google Scholar
  21. 21.
    Van Roy, P., Haridi, S.: Concepts, Techniques and Models of Computer Programming. Massachusetts Institute of Technology, Cambridge (2004)Google Scholar
  22. 22.
    Grzybowska, K., Kovács, G.: Logistics process modelling in supply chain – algorithm of coordination in the supply chain – contracting. In: de la Puerta, J.G., Ferreira, I.G., Bringas, P.G., Klett, F., Abraham, A., de Carvalho, A.C.P.L.F., Herrero, Á., Baruque, B., Quintián, H., Corchado, E. (eds.) International Joint Conference SOCO’14-CISIS’14-ICEUTE’14. AISC, vol. 299, pp. 311–320. Springer, Heidelberg (2014)Google Scholar
  23. 23.
    Grzybowska, K.: Selected activity coordination mechanisms in complex systems. In: Bajo, J., Hallenborg, K., Pawlewski, P., Botti, V., Sánchez-Pi, N., Duque Méndez, N.D., Lopes, F., Vicente, J. (eds.) PAAMS 2015 Workshops. CCIS, vol. 524, pp. 69–79. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  24. 24.
    Bzdyra, K., Banaszak, Z., Bocewicz, G.: Multiple project portfolio scheduling subject to mass customized service. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds.) Progress in Automation, Robotics and Measuring Techniques. AISC, vol. 350, pp. 11–22. Springer, Heidelberg (2015)Google 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 Electronic and Computer EngineeringKoszalin University of TechnologyKoszalinPoland

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