Analysis of lot-sizing methods’ suitability for different manufacturing application scenarios oriented to MRP and JIT/Kanban environments

• Autor(a) principal: Florim, Wilson
• Data de Publicação: 2019
• Outros Autores: Dias, Paulo, Santos, Andre S., Varela, M.L.R., Madureira, Ana M., Putnik, Goran D.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://hdl.handle.net/1822/66314
• topic: Lot-sizing methods; suitability analysis; MRP-based and JIT/Kanban environments Wilson; MRP-based and JIT/Kanban environments; Science & Technology

Goal: The main goal of this research is to analyse the behaviour of a set of ten lot-sizing methods applied to different application scenarios, within the context of more traditional MRP-based manufacturing environments and on JIT/ Kanbans oriented ones.Design/Methodology/Approach: After an extended literature review, a quantitative research method is used to provide a comparative analysis on the performance of the lot-sizing methods under different simulated application scenarios, with variations in demand and peaks of seasonality. Moreover, a final summary provides the error deviations for lot-sizing methods regarding increases in demand variations and seasonality indexes.Results: The study analyses lot-sizing methods and discusses benefits and risks associated to its use in application scenarios marked by a considerable variation in demand or peaks in seasonality.Limitations of the investigation: As the application scenarios did not explore variations in the ordering and stock holding costs, further analysis including these kinds of variations is encouraged.Practical implications: The findings of this research enable the enhancement of the conscience of industrial practitioners, regarding the selection of best suited lot-sizing methods for being applied on each kind of manufacturing scenario, regarding MRP or JIT/Kanban environments.Originality/Value: Given the diversity of the existing lot-sizing methods, for instance, the heuristic ones, authors can find it quite difficult to select appropriate methods for solving their problems for each kind of application scenario. Therefore, the present study can provide useful knowledge to better support decision making in the lot-sizing domain.