Case-Based Reasoning Model for International Market Selection

To increase the quality of decision making in construction companies, organizational memory should be enhanced and utilized effectively. Experiences of other companies should constitute an important part of organizational memory so that a construction company may have the chance to learn from the experiences of others and modify its behavior in the light of acquired knowledge. Within this study, a case-based reasoning (CBR) decision support tool is constructed to demonstrate how experiences of competitors in international markets may be used by contractors, to support international market selection decisions. 215 cases from the Turkish construction industry have been used to build the model, namely CBR-INT. The reliability of CBR-INT in predicting potential profitability of international projects and the level of competitiveness of Turkish contractors is around 90%. Moreover, CBR-INT has proven to be an effective tool for practitioners due to its high explanation capability and ease of use. Using the conceptual framework of CBR-INT, different CBR models can also be constructed for other countries.

[1]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[2]  David Leake,et al.  Case-Based Reasoning: Experiences, Lessons and Future Directions , 1996 .

[3]  Makarand Hastak,et al.  ICRAM-1: Model for International Construction Risk Assessment , 2000 .

[4]  Irem Dikmen,et al.  Neural Network Model to Support International Market Entry Decisions , 2004 .

[5]  Janet L. Kolodner,et al.  An introduction to case-based reasoning , 1992, Artificial Intelligence Review.

[6]  Srinath Perera,et al.  Collaborative case-based estimating and design , 1998 .

[7]  Nie-Jia Yau,et al.  Case‐Based Reasoning in Construction Management , 1998 .

[8]  Weng Tat Chan,et al.  Case-based reasoning approach in bid decision making , 2001 .

[9]  Heng Li,et al.  Selecting KBES development techniques for applications in the construction industry , 1996 .

[10]  Beliz Ozorhon,et al.  Organizational memory formation and its use in construction , 2005 .

[11]  Seung Heon Han,et al.  Making a risk-based bid decision for overseas construction projects , 2001 .

[12]  S. Thomas Ng,et al.  EQUAL: a case-based contractor prequalifier , 2001 .

[13]  Joseph H. M. Tah,et al.  Information modellng for case-based construction planning of highway bridge projects , 1999 .

[14]  S. Thomas Ng,et al.  A case-based procurement advisory system for construction , 2003 .

[15]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[16]  Phillip Burrell,et al.  Case-Based Reasoning System and Artificial Neural Networks: A Review , 2001, Neural Computing & Applications.

[17]  Onur Behzat Tokdemir,et al.  Comparison of Case-Based Reasoning and Artificial Neural Networks , 1999 .

[18]  Sung Hoon An,et al.  Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning , 2004 .

[19]  Iris D. Tommelein,et al.  Boiler Erection Scheduling Using Product Models and Case-Based Reasoning , 1997 .

[20]  Özlem Öz,et al.  Sources of competitive advantage of Turkish construction companies in international markets , 2001 .