Sales Budget Forecasting and Revision by Adaptive Network Fuzzy Base Inference System and Optimization Methods

Subject Areas : Journal of Computer & Robotics

Kaban  Koochakpour ¹ (Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran)
Mohammad Jafar  Tarokh ² (Associate Professor of IT Group, Industrial Engineering Department, K.N.Toosi University of Technology)

Received: 2015-10-05 Accepted : 2015-12-01 Published : 2016-01-01

Keywords: ANFIS, Sales Forecast, Time Series Analysis, PSO & BPN methods,

Abstract :

The sales proceeds are the most important factors for keeping alive profitable companies. So sales and budget sales are considered as important parameters influencing all other decision variables in an organization. Therefore, poor forecasting can lead to great loses in organization caused by inaccurate and non-comprehensive production and human resource planning. In this research a coherent solution has been proposed for forecasting sales besides refining and revising it continuously by ANFIS model with consideration of time series relations. The relevant data has been collected from the public and accessible annual financial reports being related to a famous Iranian company. Moreover, for more accuracy in forecasting, solution has been examined by Back Propagation neural Network (BPN) and Particle swarm Optimization (PSO). The comparison between prediction taken and real data shows that PSO can optimize some parts of prediction in contrast to the rest which is more coincident to the output of BPN analysis with more precise results relatively.