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FUZZY RULE-BASED APPROACH FOR ENTERPRICE RESOURCE PLANNING (ERP) SOFTWARE EVALUATION

Year 2015, Volume 11, Issue 1, 34 - 52, 20.01.2016

Abstract

The integration of ERP systems is a primary issue for management and
operation of enterprises. An enterprise resource planning (ERP) system is
regarded a solution approach for any organization. Future operation and
profitability of the enterprise or organization usually depends on selection
most suitable ERP system. ERP is an information system and arrange
different tools for management. This paper focuses on the ERP software
selection procedure for any governmental organization applying fuzzy rule
based decision making. Fuzzy rule based system depends on a rule
depository and components for accessing and running the rules of proposed
model. A governmental organization may request different solution
approaches for its requirements. This research proposes an effective
process to exploit what issues should be considered for ERP software
selection in order to enhance enterprise competitive advantages.

References

  • Moore, M. H. (2000). Managing for value: Organizational strategy in for-profit, nonprofit, and governmental organizations. Nonprofit and Voluntary Sector Quarterly, 29(suppl 1), 183-208.
  • Umble, E. J., Haft, R. R., & Umble, M. M. (2003). Enterprise resource planning: Implementation procedures and critical success factors. European journal of operational research, 146(2), 241-257.
  • Zhang, L., Lee, M. K., Zhang, Z., & Banerjee, P. (2003, January).
  • Critical success factors of enterprise resource planning systems implementation success in China. In System Sciences, 2003. Proceedings of the 36th Annual Hawaii International Conference on (pp. 10-pp). IEEE.
  • Lim, E. T., Pan, S. L., & Tan, C. W. (2005). Managing user acceptance towards enterprise resource planning (ERP) systems–understanding the dissonance between user expectations and managerial policies. European
  • Journal of Information Systems, 14(2), 135-149. Haddara, M. (2014). ERP Selection: The SMART Way. Procedia Technology,16, 394-403.
  • Kilic, H. S., Zaim, S., & Delen, D. (2015). Selecting “The Best” ERP system for SMEs using a combination of ANP and PROMETHEE methods.
  • Expert Systems with Applications, 42(5), 2343-2352.
  • Jacobs, F. R. (2007). Enterprise resource planning (ERP)-A brief history.
  • Journal of Operations Management, 25(2), 357-363. Kumar, V., Maheshwari, B., & Kumar, U. (2003). An investigation of critical management issues in ERP implementation: Empirical evidence from Canadian organizations. Technovation, 23, 793–807.
  • Ross, J. W., & Vitale, M. R. (2000). The ERP revolution: surviving vs. thriving.Information systems frontiers, 2(2), 233-241.
  • Aloini, D., Dulmin, R., & Mininno, V. (2007). Risk management in
  • ERP project introduction: Review of the literature. Information & Management, 44, 547–567. Botta-Genoulaz, V., Millet, P. A., & Grabot, B. (2005). A survey on the recent research literature on ERP systems. Computers in Industry, 56(6), 522.
  • Forslund, H., & Jonsson, P. (2010). Selection, implementation and use of ERP systems for supply chain performance management. Industrial
  • Management & Data Systems, 110(8), 1159–1175.
  • Carter, L., & Belangar, F. (2005). The utilization of e-government services: citizen trust, innovation and acceptance factors. Information Systems Journal, 15(1), 5-25.
  • Ke, W., & Wei, K. (2004). Successful e-government in Singapore.
  • Communications of the ACM, 47(6), 95-99. Danziger, J. N., & Andersen, K. V. (2002). The Impacts of Information
  • Technology on Public Administration: an Analysis of Empirical Research from the “Golden Age” of Transformation [1]. International Journal of Public Administration, 25(5), 591-627. Layne, K., & Lee, J. (2001). Developing fully functional E- government: A four stage model. Government information quarterly, 18(2), 136.
  • Shao, Z., Feng, Y., & Liu, L. (2012). The mediating effect of organizational culture and knowledge sharing on transformational leadership and Enterprise Resource Planning systems success: An empirical study in China. Computers in Human Behavior, 28(6), 2400-2413.
  • Haddara, M. (2014). ERP Selection: The SMART Way. Procedia Technology, 16, 394-403.
  • Sun, H., Ni, W., & Lam, R. (2015). A step-by-step performance assessment and improvement method for ERP implementation: Action case studies in Chinese companies. Computers in Industry.
  • Wei, C.-C., Chien, C.-F., & Wang, M.-J. J. (2005). An AHP based approach to ERP system selection. International Journal of Production Economics, 96, 47–62.
  • Kilic, H. S., Zaim, S., & Delen, D. (2014). Development of a hybrid methodology for ERP system selection: The case of Turkish Airlines.
  • Decision Support Systems, 66, 82-92. Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338
  • Zeleznikow, J., & Nolan, J. R. (2001). Using soft computing to build real world intelligent decision support systems in uncertain domains.
  • Decision Support Systems, 31(2), 263-285. Jin, Y., & Sendhoff, B. (2003). Extracting interpretable fuzzy rules from RBF networks. Neural Processing Letters, 17(2), 149-164.
  • Słowiński, R. (Ed.). (1992). Intelligent decision support: handbook of applications and advances of the rough sets theory (Vol. 11). Springer
  • Science & Business Media. Klir, G., & Yuan, B. (1995). Fuzzy sets and fuzzy logic (Vol. 4). New
  • Jersey: Prentice Hall. Goztepe, K. (2012). Designing Fuzzy Rule Based Expert System for
  • Cyber Security. International Journal of Information Security Science, 1(1), 19. Tolias, Y. A., & Panas, S. M. (1998). On applying spatial constraints in fuzzy image clustering using a fuzzy rule-based system. Signal Processing Letters, IEEE, 5(10), 245-247.
  • Adina, U. T. A., Intorsureanu, I., & Mihalca, R. (2007). Criteria for the selection of ERP software. Informatica Economica, 11(2), 63-66.
  • Yazgan, H. R., Boran, S., & Goztepe, K. (2009). An ERP software selection process with using artificial neural network based on analytic network process approach. Expert Systems with Applications, 36(5), 9214
  • Tsai, W. H., Lee, P. L., Chen, S. P., & Hsu, W. (2009). A study of the selection criteria for enterprise resource planning systems. International
  • Journal of Business and Systems Research, 3(4), 456-480. louiscolumbus/ market-leadership/ market share, http://www.forbes.com/ sites/ /05/12/2013-erp-market-share-update-sap-solidifies- http://beyondplm.com/2014/11/26/why-all-plm-software-will-be-saas- soon/
  • Yager, R. R., & Filev, D. P. (1994). Essentials of fuzzy modeling and control. New York.
  • Fortemps, P., & Roubens, M. (1996). Ranking and defuzzification methods based on area compensation. Fuzzy sets and systems, 82(3), 319
  • Fuzzy rule based system, http://sci2s.ugr.es/gfs/frbs.php, access time, Appril, 2015.

KURUMSAL KAYNAK PLANLAMA (KKP) YAZILIMI DEĞERLENDİRMESİNDE BULANIK KURAL TABANLI YAKLAŞIM

Year 2015, Volume 11, Issue 1, 34 - 52, 20.01.2016

Abstract

-

References

  • Moore, M. H. (2000). Managing for value: Organizational strategy in for-profit, nonprofit, and governmental organizations. Nonprofit and Voluntary Sector Quarterly, 29(suppl 1), 183-208.
  • Umble, E. J., Haft, R. R., & Umble, M. M. (2003). Enterprise resource planning: Implementation procedures and critical success factors. European journal of operational research, 146(2), 241-257.
  • Zhang, L., Lee, M. K., Zhang, Z., & Banerjee, P. (2003, January).
  • Critical success factors of enterprise resource planning systems implementation success in China. In System Sciences, 2003. Proceedings of the 36th Annual Hawaii International Conference on (pp. 10-pp). IEEE.
  • Lim, E. T., Pan, S. L., & Tan, C. W. (2005). Managing user acceptance towards enterprise resource planning (ERP) systems–understanding the dissonance between user expectations and managerial policies. European
  • Journal of Information Systems, 14(2), 135-149. Haddara, M. (2014). ERP Selection: The SMART Way. Procedia Technology,16, 394-403.
  • Kilic, H. S., Zaim, S., & Delen, D. (2015). Selecting “The Best” ERP system for SMEs using a combination of ANP and PROMETHEE methods.
  • Expert Systems with Applications, 42(5), 2343-2352.
  • Jacobs, F. R. (2007). Enterprise resource planning (ERP)-A brief history.
  • Journal of Operations Management, 25(2), 357-363. Kumar, V., Maheshwari, B., & Kumar, U. (2003). An investigation of critical management issues in ERP implementation: Empirical evidence from Canadian organizations. Technovation, 23, 793–807.
  • Ross, J. W., & Vitale, M. R. (2000). The ERP revolution: surviving vs. thriving.Information systems frontiers, 2(2), 233-241.
  • Aloini, D., Dulmin, R., & Mininno, V. (2007). Risk management in
  • ERP project introduction: Review of the literature. Information & Management, 44, 547–567. Botta-Genoulaz, V., Millet, P. A., & Grabot, B. (2005). A survey on the recent research literature on ERP systems. Computers in Industry, 56(6), 522.
  • Forslund, H., & Jonsson, P. (2010). Selection, implementation and use of ERP systems for supply chain performance management. Industrial
  • Management & Data Systems, 110(8), 1159–1175.
  • Carter, L., & Belangar, F. (2005). The utilization of e-government services: citizen trust, innovation and acceptance factors. Information Systems Journal, 15(1), 5-25.
  • Ke, W., & Wei, K. (2004). Successful e-government in Singapore.
  • Communications of the ACM, 47(6), 95-99. Danziger, J. N., & Andersen, K. V. (2002). The Impacts of Information
  • Technology on Public Administration: an Analysis of Empirical Research from the “Golden Age” of Transformation [1]. International Journal of Public Administration, 25(5), 591-627. Layne, K., & Lee, J. (2001). Developing fully functional E- government: A four stage model. Government information quarterly, 18(2), 136.
  • Shao, Z., Feng, Y., & Liu, L. (2012). The mediating effect of organizational culture and knowledge sharing on transformational leadership and Enterprise Resource Planning systems success: An empirical study in China. Computers in Human Behavior, 28(6), 2400-2413.
  • Haddara, M. (2014). ERP Selection: The SMART Way. Procedia Technology, 16, 394-403.
  • Sun, H., Ni, W., & Lam, R. (2015). A step-by-step performance assessment and improvement method for ERP implementation: Action case studies in Chinese companies. Computers in Industry.
  • Wei, C.-C., Chien, C.-F., & Wang, M.-J. J. (2005). An AHP based approach to ERP system selection. International Journal of Production Economics, 96, 47–62.
  • Kilic, H. S., Zaim, S., & Delen, D. (2014). Development of a hybrid methodology for ERP system selection: The case of Turkish Airlines.
  • Decision Support Systems, 66, 82-92. Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338
  • Zeleznikow, J., & Nolan, J. R. (2001). Using soft computing to build real world intelligent decision support systems in uncertain domains.
  • Decision Support Systems, 31(2), 263-285. Jin, Y., & Sendhoff, B. (2003). Extracting interpretable fuzzy rules from RBF networks. Neural Processing Letters, 17(2), 149-164.
  • Słowiński, R. (Ed.). (1992). Intelligent decision support: handbook of applications and advances of the rough sets theory (Vol. 11). Springer
  • Science & Business Media. Klir, G., & Yuan, B. (1995). Fuzzy sets and fuzzy logic (Vol. 4). New
  • Jersey: Prentice Hall. Goztepe, K. (2012). Designing Fuzzy Rule Based Expert System for
  • Cyber Security. International Journal of Information Security Science, 1(1), 19. Tolias, Y. A., & Panas, S. M. (1998). On applying spatial constraints in fuzzy image clustering using a fuzzy rule-based system. Signal Processing Letters, IEEE, 5(10), 245-247.
  • Adina, U. T. A., Intorsureanu, I., & Mihalca, R. (2007). Criteria for the selection of ERP software. Informatica Economica, 11(2), 63-66.
  • Yazgan, H. R., Boran, S., & Goztepe, K. (2009). An ERP software selection process with using artificial neural network based on analytic network process approach. Expert Systems with Applications, 36(5), 9214
  • Tsai, W. H., Lee, P. L., Chen, S. P., & Hsu, W. (2009). A study of the selection criteria for enterprise resource planning systems. International
  • Journal of Business and Systems Research, 3(4), 456-480. louiscolumbus/ market-leadership/ market share, http://www.forbes.com/ sites/ /05/12/2013-erp-market-share-update-sap-solidifies- http://beyondplm.com/2014/11/26/why-all-plm-software-will-be-saas- soon/
  • Yager, R. R., & Filev, D. P. (1994). Essentials of fuzzy modeling and control. New York.
  • Fortemps, P., & Roubens, M. (1996). Ranking and defuzzification methods based on area compensation. Fuzzy sets and systems, 82(3), 319
  • Fuzzy rule based system, http://sci2s.ugr.es/gfs/frbs.php, access time, Appril, 2015.

Details

Primary Language English
Journal Section Articles
Authors

Kerim GÖZTEPE>


Muammer KARAMAN This is me


Hayrettin ÇATALKAYA This is me

Publication Date January 20, 2016
Published in Issue Year 2015, Volume 11, Issue 1

Cite

APA Göztepe, K. , Karaman, M. & Çatalkaya, H. (2016). FUZZY RULE-BASED APPROACH FOR ENTERPRICE RESOURCE PLANNING (ERP) SOFTWARE EVALUATION . Journal of Naval Sciences and Engineering , 11 (1) , 34-52 . Retrieved from https://dergipark.org.tr/en/pub/jnse/issue/10002/123535