Applying Case-Based Reasoning in Supporting Strategy Decisions
Globalization and therefore increasing tight competition among companies, have resulted to increase the importance of making well-timed decision. Devising and employing effective strategies, that are flexible and adaptive to changing market, stand a greater chance of being effective in the long-term. In other side, a clear focus on managing the entire product lifecycle has emerged as critical areas for investment. Therefore, applying wellorganized tools to employ past experience in new case, helps to make proper and managerial decisions. Case based reasoning (CBR) is based on a means of solving a new problem by using or adapting solutions to old problems. In this paper, an adapted CBR model with k-nearest neighbor (K-NN) is employed to provide suggestions for better decision making which are adopted for a given product in the middle of life phase. The set of solutions are weighted by CBR in the principle of group decision making. Wrapper approach of genetic algorithm is employed to generate optimal feature subsets. The dataset of the department store, including various products which are collected among two years, have been used. K-fold approach is used to evaluate the classification accuracy rate. Empirical results are compared with classical case based reasoning algorithm which has no special process for feature selection, CBR-PCA algorithm based on filter approach feature selection, and Artificial Neural Network. The results indicate that the predictive performance of the model, compare with two CBR algorithms, in specific case is more effective.
[1] D. Kiritsis, A. Bufardi. A, P. Xirouchakis, "Research issues on product
lifecycle management and information tracking using smart embedded
systems," Advanced Engineering Informatics, vol.17, PP.189-202,
(2003).
[2] R. Fornasiero, A.Zangiacomi, "Management of residual life cycle costs
for sustainability in middle of life phase," Int. J. Product Lifecycle
Management, Vol. 4, pp.114-128, 2009.
[3] S. Terzi, M. Garetti, and D. Kiritsis, "A New Point of View on Product
Lifecycle Management," 2007.
[4] J. Surma, "Case Based Reasoning for Supporting Strategy Decision
Making in Small and Medium Enterprises," Springer-Verlag Berlin
Heidelberg , 2010, ch4.
[5] M. Roberto, " Making Difficult Decisions in Turbulent Times," Ivey
Business Journal, 2002.
[6] G. Gavetti, , D. Levinthal, J. Rivkin, , " Strategy Making in Novel and
Complex Worlds: The Power of Analogy." Strategic Management
Journal, 26, 2005.
[7] K. Kim, I. Han, "Maintaining case based reasoning systems using
agenetic algorithms approach," Expert Systems with Applications,
vol.21, pp. 139-145, 2001.
[8] J. Kolodner, "Case Based Reasoning. Morgan Kaufmann," San
Francisco, 1993.
[9] P. Humphreys, R. McIvor, F. Chan, "Using case-based reasoning to
evaluate supplier environmental management performance," Expert
Systems with Applications , vol.25, pp.141-153, 2003.
[10] I. Watson, "Applying Case-based Reasoning: Techniques for Enterprise
Systems," Morgan Kaufmann Publishers, San Francisco, CA, 1997.
[11] D. W. Aha, " The omnipresence of case-based reasoning in science and
application," Knowledge-Based Systems, vol.11, pp.261-273, 1998.
[12] Aamodt, K. D Althoff, R. Magaldi, and R. Milne, "Case-Based
Reasoning: A New Force In Advanced Systems Development," Unicom
Seminars & AI Intelligence,1995.
[13] R.D. Deters, "CBR for maintenance of telecommunication networks,"
Proceedings Second European Workshop on Case-Based Reasoning,
pp.23-32, 1994.
[14] A. Varma, N. Roddy, " ICARUS: a case-based system for locomotive
diagnostics," Engineering Applications of Artificial Intelligence,
vol.12(6), pp.681-690, 1999.
[15] F. Alexandrini, D. Krechel, K. Maximini, AV. Wangenheim, "
Integrating CBR into the Health Care Organization," 16th IEEE
Symposium on Computer- Based Medical Systems, 2003.
[16] W. Cheetham, "Global Grade Selector: A Recommender System for
Supporting the Sale of Plastic Resin," Berlin: Springer, pp.96-106,
2003.
[17] T.L. Acorn, S. H. Walden, "support management cultivated reasoning
technology for Compaq customer service," Innovative Applications of
Artificial Intelligence, vol. 4, Cambridge, 1992.
[18] M. BAIG, "Case Based Reasoning-An Effective Paradigm for Providing
Diagnostic Support Stroke Patients," Queen-s University, Kingston,
Canada, 2008.
[19] H. Li, H. Huang, J. Sun, Ch. Lin, "On sensitivity of case-based
reasoning to optimal feature subsets in business failure prediction,"
Expert Systems with Applications, 2010.
[20] A. Aamodt, E. Plaza, "Case-Based Reasoning; Foundational Issues,
Methodological Variations, and System Approaches," AI
Communications. IOS Press, Vol. 7:1, pp. 39 -59, 1994.
[21] H. Li, J. Sun, "Predicting business failure using multiple case-based
reasoning combined with support vector machine," Expert Systems with
Applications, vol. 36, pp. 10085-1009, 2009.
[22] H. Ahn, K. Kim, "Bankruptcy prediction modeling with hybrid casebased
reasoning and genetic algorithms approach," Applied Soft
Computing, vol.9, pp. 599-607, 2009.
[23] S. W. Changchien, M. C. Lin, "Design and implementation of casebased
reasoning system for marketing plans," Expert Systems with
Applications, vol. 28, pp. 43-53, 2005.
[24] B. U. Haque, R. A. Belecheanu, R. J. Barson, , and K. S. Pawar,
"Toward the application of case based reasoning to decision-making in
concurrent product development ," Knowledge- Based Systems, vol. 13,
pp. 101-112.
[25] Sh. W. Lin, K. Ch. Ying , Sh. Ch. Chen , Z. J. Lee, "Particle swarm
optimization for parameter determination and feature selection of
support vector machines," Expert Systems with Applications , vol. 35,
pp. 1817-1824, 2008.
[26] H. Liu, H. Motoda, " Feature Selection for knowledge discovery and
data mining," Boston: Kluwer Academic, 1998.
[27] R. Kohavi, G. H. John, "Wrappers for feature subset selection,"
Artificial Intelligence, vol. 97, pp. 273-324, 1997.
[28] P. N. Tan, V. Kumar, "Introduction to Data Mining," Michigan State
University, ch. 5, 2006.
[29] Ch. L. Huang, M. C. Chen, C. J. Wang ,"Credit scoring with a data
mining approach based on support vector machines," Expert Systems
with Applications, 2006
[1] D. Kiritsis, A. Bufardi. A, P. Xirouchakis, "Research issues on product
lifecycle management and information tracking using smart embedded
systems," Advanced Engineering Informatics, vol.17, PP.189-202,
(2003).
[2] R. Fornasiero, A.Zangiacomi, "Management of residual life cycle costs
for sustainability in middle of life phase," Int. J. Product Lifecycle
Management, Vol. 4, pp.114-128, 2009.
[3] S. Terzi, M. Garetti, and D. Kiritsis, "A New Point of View on Product
Lifecycle Management," 2007.
[4] J. Surma, "Case Based Reasoning for Supporting Strategy Decision
Making in Small and Medium Enterprises," Springer-Verlag Berlin
Heidelberg , 2010, ch4.
[5] M. Roberto, " Making Difficult Decisions in Turbulent Times," Ivey
Business Journal, 2002.
[6] G. Gavetti, , D. Levinthal, J. Rivkin, , " Strategy Making in Novel and
Complex Worlds: The Power of Analogy." Strategic Management
Journal, 26, 2005.
[7] K. Kim, I. Han, "Maintaining case based reasoning systems using
agenetic algorithms approach," Expert Systems with Applications,
vol.21, pp. 139-145, 2001.
[8] J. Kolodner, "Case Based Reasoning. Morgan Kaufmann," San
Francisco, 1993.
[9] P. Humphreys, R. McIvor, F. Chan, "Using case-based reasoning to
evaluate supplier environmental management performance," Expert
Systems with Applications , vol.25, pp.141-153, 2003.
[10] I. Watson, "Applying Case-based Reasoning: Techniques for Enterprise
Systems," Morgan Kaufmann Publishers, San Francisco, CA, 1997.
[11] D. W. Aha, " The omnipresence of case-based reasoning in science and
application," Knowledge-Based Systems, vol.11, pp.261-273, 1998.
[12] Aamodt, K. D Althoff, R. Magaldi, and R. Milne, "Case-Based
Reasoning: A New Force In Advanced Systems Development," Unicom
Seminars & AI Intelligence,1995.
[13] R.D. Deters, "CBR for maintenance of telecommunication networks,"
Proceedings Second European Workshop on Case-Based Reasoning,
pp.23-32, 1994.
[14] A. Varma, N. Roddy, " ICARUS: a case-based system for locomotive
diagnostics," Engineering Applications of Artificial Intelligence,
vol.12(6), pp.681-690, 1999.
[15] F. Alexandrini, D. Krechel, K. Maximini, AV. Wangenheim, "
Integrating CBR into the Health Care Organization," 16th IEEE
Symposium on Computer- Based Medical Systems, 2003.
[16] W. Cheetham, "Global Grade Selector: A Recommender System for
Supporting the Sale of Plastic Resin," Berlin: Springer, pp.96-106,
2003.
[17] T.L. Acorn, S. H. Walden, "support management cultivated reasoning
technology for Compaq customer service," Innovative Applications of
Artificial Intelligence, vol. 4, Cambridge, 1992.
[18] M. BAIG, "Case Based Reasoning-An Effective Paradigm for Providing
Diagnostic Support Stroke Patients," Queen-s University, Kingston,
Canada, 2008.
[19] H. Li, H. Huang, J. Sun, Ch. Lin, "On sensitivity of case-based
reasoning to optimal feature subsets in business failure prediction,"
Expert Systems with Applications, 2010.
[20] A. Aamodt, E. Plaza, "Case-Based Reasoning; Foundational Issues,
Methodological Variations, and System Approaches," AI
Communications. IOS Press, Vol. 7:1, pp. 39 -59, 1994.
[21] H. Li, J. Sun, "Predicting business failure using multiple case-based
reasoning combined with support vector machine," Expert Systems with
Applications, vol. 36, pp. 10085-1009, 2009.
[22] H. Ahn, K. Kim, "Bankruptcy prediction modeling with hybrid casebased
reasoning and genetic algorithms approach," Applied Soft
Computing, vol.9, pp. 599-607, 2009.
[23] S. W. Changchien, M. C. Lin, "Design and implementation of casebased
reasoning system for marketing plans," Expert Systems with
Applications, vol. 28, pp. 43-53, 2005.
[24] B. U. Haque, R. A. Belecheanu, R. J. Barson, , and K. S. Pawar,
"Toward the application of case based reasoning to decision-making in
concurrent product development ," Knowledge- Based Systems, vol. 13,
pp. 101-112.
[25] Sh. W. Lin, K. Ch. Ying , Sh. Ch. Chen , Z. J. Lee, "Particle swarm
optimization for parameter determination and feature selection of
support vector machines," Expert Systems with Applications , vol. 35,
pp. 1817-1824, 2008.
[26] H. Liu, H. Motoda, " Feature Selection for knowledge discovery and
data mining," Boston: Kluwer Academic, 1998.
[27] R. Kohavi, G. H. John, "Wrappers for feature subset selection,"
Artificial Intelligence, vol. 97, pp. 273-324, 1997.
[28] P. N. Tan, V. Kumar, "Introduction to Data Mining," Michigan State
University, ch. 5, 2006.
[29] Ch. L. Huang, M. C. Chen, C. J. Wang ,"Credit scoring with a data
mining approach based on support vector machines," Expert Systems
with Applications, 2006
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:62447", author = "S. M. Seyedhosseini and A. Makui and M. Ghadami", title = "Applying Case-Based Reasoning in Supporting Strategy Decisions", abstract = "Globalization and therefore increasing tight competition among companies, have resulted to increase the importance of making well-timed decision. Devising and employing effective strategies, that are flexible and adaptive to changing market, stand a greater chance of being effective in the long-term. In other side, a clear focus on managing the entire product lifecycle has emerged as critical areas for investment. Therefore, applying wellorganized tools to employ past experience in new case, helps to make proper and managerial decisions. Case based reasoning (CBR) is based on a means of solving a new problem by using or adapting solutions to old problems. In this paper, an adapted CBR model with k-nearest neighbor (K-NN) is employed to provide suggestions for better decision making which are adopted for a given product in the middle of life phase. The set of solutions are weighted by CBR in the principle of group decision making. Wrapper approach of genetic algorithm is employed to generate optimal feature subsets. The dataset of the department store, including various products which are collected among two years, have been used. K-fold approach is used to evaluate the classification accuracy rate. Empirical results are compared with classical case based reasoning algorithm which has no special process for feature selection, CBR-PCA algorithm based on filter approach feature selection, and Artificial Neural Network. The results indicate that the predictive performance of the model, compare with two CBR algorithms, in specific case is more effective.
", keywords = "Case based reasoning, Genetic algorithm, Groupdecision making, Product management.", volume = "5", number = "2", pages = "469-6", }
