Quantitative demand forecasting adjustment based on qualitative factors: case study at a fast food restaurant

• Autor(a) principal: Meneghini, Mateus
• Data de Publicação: 2018
• Outros Autores: Anzanello, Michel, Kahmann, Alessandro, Luz Tortorella, Guilherme
• Tipo de documento: Artigo
• Idioma: por; eng
• Título da fonte: Sistemas & Gestão
• Texto Completo: https://www.revistasg.uff.br/sg/article/view/1188
• topic: Forecast of Demand; Time Series; Quantitative Models; Qualitative Adjustment; Fast Food; Previsão de Demanda; Séries Temporais; Modelos Quantitativos; Ajuste Qualitativo; Fast Food

This paper proposes a method of forecasting demand that integrates quantitative models with qualitative contextual factors. The proposed method selects the mathematical (quantitative) model that best fits the historical data, based on the determination coefficient R² and the mean absolute percentage error (MAPE). Next, the forecasts generated by the selected model are adjusted based on expert opinion on contextual factors (judgemental adjustment), such as events and renovations, for example, not included in the historical data. The proposed method was applied at a fast food restaurant to forecast the demand of meat. The adjusted method yielded an average error of 10% in the worst scenario when compared to the real demand of the period, whereas the quantitative model, with no judgemental adjustment, led to an average error of 38%.