Forecasting Preliminary Order Cost to Increase Order Management Performance

Tüzin Günsari, Aysegül Kaya, Yeliz Ekinci

OAI: oai:igi-global.com:298015 • DOI: 10.4018/IJBAN.298015

In this study, the cost estimation to be used in the optimization of proposed order price offer is made by artificial neural network (ANN) method. A case study is performed by the real data of a company, and the forecast results of the traditional arithmetic model used by the company and the proposed ANN based method are compared and it is seen that the proposed method results outperform the other. The biggest contribution of this study to companies is to increase the company’s order management performance by helping the company to make more accurate pricing due to more accurate cost estimation. Moreover, to the best of our knowledge, this is the first study on forecasting preliminary order cost in the apparel industry and fills an important gap in the literature.

Business Intelligence Business and Management Business Information Systems