Abstract
Quick and accurate adapting selling can boost business results. This study developed an adapting selling solution by combining case-based reasoning (CBR) and rough set theory for industrial proposal generation in business-to-business (B2B) context. In this regard, CBR was developed to find the right proposal by considering users’ needs. Rough set theory is then developed to find the proper attribute weights for CBR retrieval phase. Further, when cases have same problem features or they have same similarity values, it is not possible to select one case among the retrieved cases. For this purpose, this study presented a new method to discriminate equally retrieved cases. Sales data of a steel manufacturing company are used and inputted into the CBR system. Practical application of the proposed system illustrated efficacy of the procedures and algorithms.
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