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.

研究摘要：本研究旨在探究能否通过层次分析法（Analytic Hierarchy Process，AHP）调整时间序列模型的输出，以获得更精准可靠的预测。
创新性：本研究将需求预测视作复杂决策情境，探讨了将层次分析法作为传统预测方法补充工具的应用可行性。
研究方法：本研究属于应用研究范畴，核心研究流程采用文献综述与数学建模两种手段。
主要研究成果：本研究提出两款预测效果优异的模型：模型I的预测误差在1月降低16%、2月降低25%、3月降低37%、4月降低3%、5月降低7%；模型II的预测误差在1月降低17%、2月降低21%、3月降低29%、4月降低2%、5月降低5%。
理论与实践启示：本研究结论证实，层次分析法具备修正纺织行业时间序列预测结果的潜力，能够实现定量与定性变量的融合纳入。