PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2016 | 8 | nr 1 | 21--34
Tytuł artykułu

Computational Intelligence for Estimating Cost of New Product Development

Autorzy
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper is concerned with estimating cost of various new product development phases with the use of computational intelligence techniques such as neural networks and fuzzy neural system. Companies tend to develop many new products simultaneously and a limited project budget imposes the selection of the most promising new product development projects. The evaluation of new product projects requires cost estimation. The model of cost estimation contains product design, prototype manufacturing and testing, and it is specified in terms of a constraint satisfaction problem. The illustrative example presents comparative analysis of estimating product development cost using computational intelligence techniques and multiple regression model. (original abstract)
Rocznik
Tom
8
Numer
Strony
21--34
Opis fizyczny
Twórcy
  • University of Zielona Gora, Poland
Bibliografia
  • Cooper R., Edgett S., 2008. Maximizing productivity in product innovation. Research Technology Management, 51(2), pp.47-58.
  • Chan S.L., Ip W.H., 2011. A dynamic decision support system to predict the value of customer for new product development. Decision Support Systems, Vol. 52, pp.178-188.
  • Spalek S., 2013. Improving industrial engineering performance through a successful project management office. Engineering Economics, 24(2), pp.88-98.
  • Relich M., Bzdyra K., 2014. Estimating new product success with the use of intelligent systems. Foundations of Management, 6(2), pp.7-20.
  • Trott P., 2005. Innovation Management and New Product Development. Essex: Prentice Hall.
  • Relich M., Bzdyra K., 2015. Knowledge discovery in enterprise databases for forecasting new product success. Intelligent Data Engineering and Automated Learning (ed. K. Jackowski et al.). Lecture Notes in Computer Science, Vol. 9375, Springer, pp.121-129.
  • Dieter G.E., 2000. Engineering Design: A Material and Processing Approach. Boston: McGraw-Hill.
  • Nepal B., Monplaisir L., Singh N., 2005. Integrated fuzzy logic-based model for product modularization during concept development phase. International Journal of Production Economics, Vol. 96, pp.157-174.
  • Ulrich K.T., Eppinger S.D., 2011. Product Design and Development. Boston: McGraw-Hill.
  • Relich M., 2015. Identifying relationships between eco-innovation and product success. Technology Management for Sustainable Production and Logistics (ed. P. Golinska, A. Kawa). Berlin Heidelberg: Springer, pp.173-192.
  • Anderson D.M., 2001. Design for Manufacturability: Optimizing Cost, Quality and Timeto-Market. Cambria: CIM Press.
  • Sharma A., 2005. Collaborative product innovation: integrating elements of CPI via PLM framework. Computer-Aided Design, Vol. 37, pp.1425-1434.
  • Danesi F., Gardan N., Gardan Y. Reimeringer M., 2008. P4LM: A methodology for product lifecycle management. Computers in Industry, Vol. 59, pp.304-317.
  • Alemanni M., Destefanis F., Vezzetti E., 2011. Model-based definition design in the product lifecycle management scenario. International Journal of Advanced Manufacturing Technology, Vol. 52, pp.1-14.
  • Gola A., Relich M., Kłosowski G., Świć A., 2015. Mathematical models for manufacturing systems capacity planning and expansion - an overview. Applied Mechanics and Materials, Vol. 791, pp.125-131.
  • Ernst H., 2002. Success factors of new product development: a review of the empirical literature. International Journal of Management Reviews, Vol. 4, pp.1-40.
  • Kormancova G., 2012. Project success and failure. Theory of Management 6: The Selected Problems for the Development Support of Management Knowledge Base, Zilina, pp.117-119.
  • Bhuiyan N., 2011. A framework for successful new product development. Journal of Industrial Engineering and Management, Vol. 4, pp.746-770.
  • McCarthy I.P., Tsinopoulos C., Allen P., Rose-Anderssen C., 2006. New product development as a complex adaptive system of decisions. Journal of Product Innovation Management, Vol. 23, pp.437-456.
  • Kahraman C., Buyukozkan G., Ates N.Y., 2007. A two phase multi-attribute decisionmaking approach for new product introduction. Information Sciences, Vol. 177, pp.1567-1582.
  • Sun H., Wing W., 2005. Critical success factors for new product development in the Hong Kong toy industry. Technovation, Vol. 25, pp.293-303.
  • Relich M., 2016. A knowledge-based system for new product portfolio selection. New Frontiers in Information and Production Systems Modelling and Analysis (ed. P. Rozewski et al.). Intelligent Systems Reference Library, Vol. 98, Springer, pp.169-187.
  • Cooper R., Slagmulder R., 1999. Develop profitable new products with target costing. Sloan Management Review, Vol. 40, pp.23-33.
  • Ben-Arieh D., Qian L., 2003. Activity-based cost management for design and development stage. International Journal of Production Economics, Vol. 83, pp.169-183.
  • Niazi A., Dai J., Balabani S., Seneviratne L., 2005. Product cost estimation: technique classification and methodology review. Journal of Manufacturing Science and Engineering, Vol. 128, pp.563-575.
  • Weustink I.F., ten Brinke E., Streppel A.H., Kals H.J., 2000. A generic framework for cost estimation and cost control in product design. Journal of Materials Processing Technology, Vol. 103, pp.141-148.
  • Foussier P., 2006. From Product Description to Cost: A Practical Approach. London: Springer.
  • Mislick G., Nussbaum D., 2015. Cost Estimation: Methods and Tools. New Jersay: Wiley & Sons.
  • Kusiak A., 2000. Computational Intelligence in Design and Manufacturing. New York: John Wiley & Sons.
  • Medsker L., 1995. Hybrid Intelligent Systems. New York: Springer Science.
  • Cavalieri S., Maccarrone P., Pinto R., 2004. Parametric vs. neural network models for the estimation of production costs: a case study in the automotive industry. International Journal of Production Economics, Vol. 91, pp.165-177.
  • Relich M., 2010. Assessment of task duration in investment projects. Management, 14(2), pp.136-147.
  • Lolas S., Olatunbosun O., 2008. Prediction of vehicle reliability performance using artificial neural networks. Expert Systems with Applications, Vol. 34, pp.2360-2369.
  • Seo K., Park J., Jang D., Wallace D., 2002. Approximate estimation of the product life cycle cost using artificial neural networks in conceptual design. International Journal of Advanced Manufacturing Technology, Vol. 19, pp.461-471.
  • Aydin R., Kwong C., Ji P., Law H., 2014. Market demand estimation for new product development by using fuzzy modelling and discrete choice analysis. Neurocomputing, Vol. 142, 2014, pp.136-146.
  • Yadav O., Singh N., Chinnam R., Goel P., 2003. A fuzzy logic based approach to reliability improvement estimation during product development. Reliability Engineering & System Safety, Vol. 80, pp.63-74.
  • Carrera D., Mayorga R., 2008. Supply chain management: a modular fuzzy inference system approach in supplier selection for new product development. Journal of Intelligent Manufacturing, Vol. 19, pp.1-12.
  • Doskocil R., 2016. An evaluation of total project risk based on fuzzy logic. Business: Theory and Practice, 17(1), pp.23-31.
  • Doskocil R., 2015. Fuzzy logic: an instrument for the evaluation of project status. Revista de Metodos Cuantitativos para la Economia y la Empresa, Vol. 19, pp.5-23.
  • Relich M., 2013. Fuzzy project scheduling using constraint programming. Applied Computer Science, 9(1), pp.3-16.
  • Chang D., Chen C., Lee K., 2014. A crowdsourcing development approach based on a neuro-fuzzy network for creating innovative product concepts. Neurocomputing, Vol. 142, pp.60-72.
  • Kwong C., Wong T., Chan K., 2009. A methodology of generating customer satisfaction models for new product development using a neuro-fuzzy approach. Expert Systems with Applications, Vol. 36, pp.11262-11270.
  • Relich M., 2015. A computational intelligence approach to predicting new product success. Proceedings of the 11th International Conference on Strategic Management and its Support by Information Systems, Uherske Hradiste, pp.142-150.
  • Rossi F., van Beek P., Walsh T., 2006. Handbook of Constraint Programming. Elsevier.
  • Bocewicz G., Nielsen I., Banaszak Z., 2014. Iterative multimodal processes scheduling. Annual Reviews in Control, 38(1), pp.113-132.
  • Relich M., Swic A., Gola A., 2015. A knowledge-based approach to product concept screening. Distributed Computing & Artificial Intelligence (ed. S. Omatu et al.). Advances in Intelligent Systems and Computing, Vol. 373, Springer, pp.341-348.
  • Relich M., Banaszak Z., 2011. Reference model of project prototyping problem. Foundations of Management, 3(1), pp.33-46.
  • Han J., Kamber M., 2006. Data Mining. Concepts and Techniques. San Francisco: Morgan Kaufmann Publishers.
  • Cios K.J., Pedrycz W., Swiniarski R.W., Kurgan L.A., 2007. Data Mining: A Knowledge Discovery Approach. New York: Springer.
  • Relich M., Pawlewski P., 2015. A multi-agent system for selecting portfolio of new product development project. PAAMS 2015 Workshops (ed. J. Bajo et al.). Communications in Computer and Information Science, Vol. 524, Springer, pp.102-114.
  • Fayyad U., Piatetsky-Shapiro G., Smith P., 1996. From data mining to knowledge discovery in databases. American Association for Artificial Intelligence, Fall, pp.37-54.
  • Relich M., 2013. Project parameter estimation on the basis of an ERP database. Foundations of Management, 5(2), pp.49-58.
  • Van Roy P., Haridi S., 2004. Concepts, Techniques and Models of Computer Programming. Massachusetts Institute of Technology.
  • Baptiste P., Le Pape C., Nuijten W., 2001. Constraint-Based Scheduling: Applying Constraint Programming to Scheduling Problems. Norwell, Massachusetts: Kluwer Academic Publishers.
  • Relich M., 2011. CP-based decision support for scheduling. Applied Computer Science, 7(1), pp.7-17.
  • Banaszak Z., Zaremba M., Muszyński W., 2009. Constraint programming for project driven manufacturing. International Journal of Production Economics, Vol. 120, pp. 463-475.
  • Relich M., 2014. A constraint programming approach for scheduling in a multi-project environment. International Journal of Advanced Computer Science and Information Technology, 3(2), pp.156-171.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171508454

Zgłoszenie zostało wysłane

Zgłoszenie zostało wysłane

Musisz być zalogowany aby pisać komentarze.
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.