Demand forecasting based on natural computing approaches applied to the foodstuff retail segment
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DOI: 10.1016/j.jretconser.2016.03.008
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Cited by:
- Tian, Xin & Wang, Haoqing & E, Erjiang, 2021. "Forecasting intermittent demand for inventory management by retailers: A new approach," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
- Wesley Marcos Almeida & Claudimar Pereira Veiga, 2023. "Does demand forecasting matter to retailing?," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(2), pages 219-232, June.
- Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2022. "Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1283-1318.
- Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
- Alceu Souza & Ariane Maria Machado de Oliveira & Dayla Karolina Fossile & Emmanuel Óguchi Ogu & Luciano Luiz Dalazen & Claudimar Pereira da Veiga, 2020. "Business Plan Analysis Using Multi-Index Methodology: Expectations of Return and Perceived Risks," SAGE Open, , vol. 10(1), pages 21582440199, January.
- Liu, Hsiu-Wen, 2024. "Mining spatial-temporal patterns from customer data to improve forecasting of customer flow across multiple sites," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
- Icaro Romolo Sousa Agostino & Wesley Vieira da Silva & Claudimar Pereira da Veiga & Adriano Mendonça Souza, 2020. "Forecasting models in the manufacturing processes and operations management: Systematic literature review," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1043-1056, November.
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Keywords
Takagi-Sugeno Fuzzy System; Wavelets neural network; Strategy; Foodstuff retail; Fill rate;All these keywords.
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