Advances in spare parts demand forecasting on industrial applications: a systematic literature review

Autores

DOI:

https://doi.org/10.20397/2177-6652/2026.v26i1.3053

Palavras-chave:

Spare parts; forecasting; industry; systematic literature review; real-world applications.

Resumo

A previsão da demanda por peças de reposição é uma tarefa importante em muitas indústrias, já que os custos de estoque podem representar 60% dos custos totais. No entanto, a escolha, avaliação e aplicação real de uma abordagem de previsão apropriada ainda são desafiadoras. Isso ocorre principalmente porque a demanda por peças de reposição tem comportamento
intermitente, que é caracterizado pela ocorrência de demandas em intervalos de tempo muito espaçados e tamanhos de demanda altamente variáveis, portanto, a previsão da demanda por peças de reposição é uma tarefa complexa. Este artigo apresenta uma revisão sistemática da literatura do atual estado da arte das aplicações do mundo real relacionadas à previsão da demanda por peças de reposição na indústria. Artigos de alta qualidade são selecionados, e ferramentas de visualização e análise são usadas para inspecionar os trabalhos selecionados. Uma análise bibliométrica também é realizada para revelar tendências. Os resultados revelam os setores industriais predominantes, aplicações com itens intermitentes e abordagens não paramétricas. Além disso, as descobertas revelam lacunas em relação à avaliação de abordagens e perspectivas de previsão que podem ser adotadas na prática.

Biografia do Autor

Symone Gomes Soares Alcalá, Universidade Federal de Goiás

Symone Gomes Soares Alcalá possui graduação em Engenharia de Computação pela Pontifícia Universidade Católica de Goiás (2009) e doutorado em Engenharia Elétrica e da Computação pela Universidade de Coimbra, Portugal (2015). Desde 2016, é Professora da Universidade Federal de Goiás, Faculdade de Ciências e Tecnologia, atuando no curso de Engenharia de Produção e no Programa de Pós-Graduação em Engenharia de Produção (PPGEP). Liderou o grupo de trabalho em Predictive Real-Time Simulation and Optimization no projeto de pesquisa Europa/Brasil Flexible and Autonomous Manufacturing Systems for Custom-Designed Products (FASTEN) da chamada Horizon 2020 (H2020). Neste ano, ingressou na Universidade Pontifícia Comillas em estágio pós-doutoral como pesquisadora na área de Inteligência Artificial (IA) Generativa aplicada à Indústria, cujo principal objetivo é investigar os facilitadores para a adoção adequada da IA generativa (como ChatGPT) na Indústria. Seus interesses de pesquisa incluem inteligência artificial aplicada à indústria, forecasting, análise de big data e sistemas de visão.

Rui Alexandre de Matos Araújo, Universidade de Coimbra

Rui Araújo recebeu o grau de bacharel. (5 anos de "Licenciatura") em Engenharia Elétrica, o grau de mestre em Sistemas e Automação, o grau de doutor em Engenharia Elétrica e o grau de Habilitação em Engenharia Elétrica e Sistemas Inteligentes pela Universidade de Coimbra, Portugal, em 1991, 1994, 2000 e 2024, respectivamente. Ingressou no Departamento de Engenharia Elétrica e de Computadores da Universidade de Coimbra, onde atualmente é Professor Associado. É Membro Sênior do IEEE e membro fundador do Instituto Português de Sistemas e Robótica (ISR-Coimbra), onde é atualmente investigador sênior.

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2026-06-17

Como Citar

Galvão Bandeira, S., Gomes Soares Alcalá, S., & Alexandre de Matos Araújo, R. (2026). Advances in spare parts demand forecasting on industrial applications: a systematic literature review. Revista Gestão & Tecnologia, 26(1), 41–72. https://doi.org/10.20397/2177-6652/2026.v26i1.3053

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v. 26 n. 1 (2026)

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ARTIGO

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