Multi-criteria approach to adjust demand forecast for products: application of analytic hierarchy process

Abstract

Paper aims: Investigate whether the results of time series models can be adjusted with the AHP method towards a more assertive forecast. Originality: Considering demand forecasting as a complex decision-making situation, this research investigated the use of the AHP as a complement to traditional forecasting methods. Research method: This applied research employed, as main procedures, literature review and mathematical modeling. Main findings: Two models were proposed that presented satisfactory results: model I reduced the forecast error by 16% in January, 25% in February, 37% in March, 3% in April, and 7% in May; model II reduced it by 17% in January, 21% in February, 29% in March, 2% in April, and 5% in May. Implications for theory and practice: We conclude that the AHP has the potential to correct the results of time series in the textile industry by allowing the incorporation of quantitative and qualitative variables.