Returned Product Acquisition Pricing by Adaptive Neuro Fuzzy Inference System

In recent years, reverse logistics have become increasingly important for the firms as a both environmental and economical approach. By collecting the returned products, firms realize to recover after kind of activities. In return products collection, due to the fact that each returned products have different functionality, determining the acquisition price of the used products is an important problem. For this reason, a pricing approach that can be used for collecting returned products is proposed in this study. Since the different product models can be exist and the acquisition price can be affected by the new product price, the acquisition price is predicted by the ratio of the new product price to acquisition price. In this study, the acquisition price ratio to new product price is modeled by the adaptive neuro fuzzy inference system and a case study is conducted for the used cell phones collection. Four phone models that have different release dates take into consideration with general appearance and functionality parameters. When the results are examined, the proposed method prediction’s is pretty close to the expert view.