This article aims towards development of a Demand forecasting model for retail automobile inventory using soft computing Technique. The forecasting of the quantitative value of the dependent variable under the influence of independent variables is explained. One of the most important decisions a retailer in the automobile sector can build on information obtained by soft computing based on the predictive model. This paper proposes a data mining technique that may be used to forecast demand. The proposed soft computing-based forecasting model resulted in a reduction in inventory and an improvement in consumer service, resulting in enhanced retail automobile inventory performance. To increase inventory performance and operations profitability, the proposed model for retail demand forecasting required a variety of factors.

Soft computing Technique, Demand forecasting, Data Mining, Matlab