The demand forecast is the most influential input into enterprise activities planning thus creating a challenging issue for Demand Planning experts in model selection. Models including quantitative, qualitative and hybrid forecasting methods have been developed and are widely used. The literature reveals the use of several case-dependent error measures to evaluate forecasting accuracy, however, these performance indicators may at times differentiate in results making it more difficult in determining the most appropriate forecasting model for the users’ needs. This paper presents the development of two hybrid multi-criteria decision making approaches, AHP-TOPSIS and ANP-TOPSIS, to evaluate and rank the relative performances based on error measures of alternate forecasting models. Validation is provided through an industrial application using empirical data from a plastic bag manufacturer based on five models; three regression forecast models and two hybrid demand forecast models using expert judgement. Results illustrate that subjective adjustment by experts of mathematical forecasts consistently gives a higher ranking due to proximity to the ideal solution, and that collaborative adjustment limits the risk of outliers due to forecasting errors that could be done by a single decision maker.