Designing sales budget forecasting and revision system by using optimisation methods. Issue 2 (2017)

• Record Type: Journal Article
• Authors: Koochakpour, K.; Tarokh, M.J.
• Is Part Of: International journal of knowledge engineering and data mining. Volume 4:Issue 2(2017)
• Page Start: 93
• Page End: 113
• Publication Date: 2017
• Subjects: sales forecast -- adaptive neuro fuzzy inference system -- ANFIS -- time series analysis -- PSO and BPN methods -- hybrid method; Knowledge representation (Information theory) -- Periodicals; Data mining -- Periodicals; 006.305
• Languages: English
• ISSNs: 1755-2087

Abstract

The sales procedures are the most important factors for keeping companies alive and profitable. So sales and budget sales are considered as important parameters influencing all other decision variables in an organisation. Therefore, poor sales forecasting can lead to great loses in an organisation caused by inaccurate and non-comprehensive production and human resource planning. Hence, in this research, a coherent solution has been proposed for forecasting sales besides refining and revising it continuously by adaptive neuro fuzzy inference system (ANFIS) model with consideration of time series relations. Data has been collected from the public and accessible annual financial reports related to a famous Iranian company. Moreover, for more accuracy in forecasting, the solution has been examined by back propagation neural network (BPN) and particle swarm optimisation (PSO) as optimisation methods. The comparison between taken prediction and the real data shows that PSO method can optimise some parts of prediction in contrast to the rest which is more coincident to the output of BPN analysis. As a consequence, a hybrid integrated system including them both, has been designed. This system uses them depending on their abilities to optimise each part, so it can produce more precise results relatively.