Knowledge discovery from an ERP database in the context of new product development

• Authors: Marcin Relich University of Zielona Gora
• Languages of publication: EN

Abstracts

EN

This paper is aimed at using an ERP database to identify the variables that have a significant influence on the duration of a project phase. In the paper, some methodologies of the knowledge discovery process are compared and a model of knowledge discovery from an ERP database is proposed. The presented approach is dedicated for the industrial enterprises that use an ERP system to plan and control the development of new products. The example contains four stages of the knowledge discovery process, such as data selection, data transformation, data mining, and the interpretation of patterns. Among data mining techniques, a fuzzy neural system is chosen to seek relationships between data from completed projects and other data stored in an ERP system.

Keywords

EN

knowledge management; project management; data selection; data mining

• Publisher: Uniwersytet Ekonomiczny we Wrocławiu. Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
• Journal: Informatyka Ekonomiczna
• Year: 2013
• Issue: 2(28)
• Pages: 100-111

Contributors

author

Marcin Relich University of Zielona Gora

• University of Zielona Gora

References

• Advanced product quality planning and control plan. Reference Model, Carwin Continuous Ltd., Essex 1994.
• Azar A.T., Adaptive neuro-fuzzy systems, [in:] Fuzzy systems, InTech 2010.
• Bagiński J., Rostański M., The modeling of business impact analysis for the loss of integrity, confidentiality and availability in business processes and data, “Theoretical and Applied Informatics” 2011, vol. 23, no. 1, pp. 73-82.
• Cabena P., Hadjinian P., Stadler R., Verhees J., Zanasi A., Discovering Data Mining: From Concepts to Implementation, Prentice Hall, 1998.
• Cheng M.Y., Tsai H.C., Sudjono E., Evolutionary fuzzy hybrid neural network for project cash flow control, “Engineering Applications of Artificial Intelligence” 2010, vol. 23, pp. 604-613.
• Chien S.C., Wang T.Y., Lin S.L., Application of neuro-fuzzy networks to forecast innovation performance, “Expert Systems with Applications” 2010, vol. 37, pp. 1086-1095.
• Colmenares G.L., Perez R., A data reduction method to train, test, and validate neural networks, Proceedings of IEEE, Southeastcon 1998, pp. 277-280.
• Davenport T., Putting the enterprise into the enterprise system, “Harvard Business Review”, July-August 1998, pp. 121-131.
• Fayyad U., Piatetsky-Shapiro G., Smith P., From data mining to knowledge discovery in databases, “American Association for Artificial Intelligence”, Fall 1996, pp. 37-54.
• Generowanie wiedzy dla przedsiębiorstwa: metody i techniki, red. M. Nycz, Wydawnictwo Akademii Ekonomicznej we Wrocławiu, Wrocław 2004.
• Han J., Kamber M., Data Mining. Concepts and Techniques, 2nd edition, Morgan Kaufmann Publishers, San Francisco 2006.
• Imtiaz A., Kibria M.G., Modules to optimize the performance of an ERP based integrated information system, IEEE International Conference on Informatics, Electronics & Vision, 2012, pp. 598-601.
• 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, Vienna 2009.
• May J., Dhillon G., Caldeira M., Defining value-based objectives for ERP systems planning, “Decision Support Systems” 2013, vol. 55, pp. 98-109.
• Relich M., A decision support system for alternative project choice based on fuzzy neural networks, “Management and Production Engineering Review” 2010, vol. 1, no. 4, pp. 46-54.
• Relich M., Project prototyping with application of CP-based approach, “Management”, 2011, vol. 15, no. 2, pp. 364-377.
• Zeng J., An M., Smith N.J., Application of a fuzzy based decision making methodology to construction project risk assessment, “International Journal of Project Management” 2007, vol. 25, pp. 589-600.