Método de previsão de vendas e estimativa de reposição de itens no varejo da moda

• Ano de defesa: 2018
• Autor(a) principal: Santos, Graziele Marques Mazuco dos
• Orientador(a): Ruiz, Duncan Dubugras Alcoba
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Pontifícia Universidade Católica do Rio Grande do Sul
• Programa de Pós-Graduação: Programa de Pós-Graduação em Ciência da Computação
• Departamento: Escola Politécnica
• País: Brasil
• Palavras-chave em Português: Mineração de Dados; Séries Temporais; Previsão de Vendas; Indústria da Moda
• Palavras-chave em Inglês: Dynamic Time Warping; Data Mining; Time Series; Sales Forecast; Fashion Industry
• Área do conhecimento CNPq: CIENCIA DA COMPUTACAO::TEORIA DA COMPUTACAO
• Link de acesso: http://tede2.pucrs.br/tede2/handle/tede/8171

Demand forecasting is one of the most essential components of supply chain management. Forecasts are used both for long-term and for short-term. Long-term forecasts are important because it is difficult in terms of production to face the demand deviation in a short time, so the anticipation of prediction helps to increase the responsiveness of the supply chain. Short term forecasts are important for the demand monitoring aiming to keep healthy inventory levels. In the fashion industry, the high change of products, the short life cycle and the lack of historical data makes difficult accurate predictions. To deal with this problem, the literature presents three approaches: statistical, artificial intelligence and hybrid that combines statistical and artificial intelligence. This research presents a two-phased method: (1) long-term prediction, identifies the different life cycles in the products, allowing the identification of sales prototypes for each cluster and (2) short-term prediction, classifies new products in the clusters labeled in the long-term phase and adjusts the sales curve considering optimistic and pessimist factors. As a differential, the method is based in dynamic time warping, distance measure for time series. The method is tested in a real dataset with real data from fashion retailers that demonstrates the quality of the contribution.