SCENERY:
In the machining sector there are 50 state-of-the-art machines, capable of machining 200 production orders per month. The constrain is that only few tooling kits for the same product model are available, that means that you can only use 5 or 3 machines simultaneously to produce the same parts. How to distribute these production orders to optimize production and obtain the shortest possible delay in the production of machined parts?
Planejamento SEM o Optimus APS | Planejamento COM o Optimus APS | |
---|---|---|
Tempo Gasto em Planejamento | 8 horas | 1 MINUTO |
Horas de Atraso na Produção | 653 horas | 528 HORAS |
% de Melhora | - | 19% |
Planejamento SEM o Optimus APS | Planejamento COM o Optimus APS | |
---|---|---|
Tempo Gasto em Planejamento | 4 horas | 10 MINUTOS |
Tempo de set-up | 1.034 horas | 885 HORAS |
% de Melhora | - | 19% |
WIP | R$ 2,687 Milhões | R$ 1,996 Milhões |
% de Melhora | - | 19% |
SCENERY:
In the plastic injection department there are 10 injection machines. Approximately 2,000 production orders of a variety of product models and quantities of parts must be produced every month. The number of molds is restricted and there is a wide variety of colors. It is necessary to consider the change over of both, molds and/or colors, as the high frequency of changes leads to a long time of machine downtime.
How to increase productivity and reduce delivery delays considering all these variables?
Planejamento Optimus OPS + Optimizer | |
---|---|
Tempo de Otimização | 1 MINUTO |
Ordens de Produção em Dia | AUMENTO DE 300% |
Ordens de Produção Atrasadas | REDUÇÃO DE 50% |
Redução de Set-up | 25% |
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At 4Cast, we believe that predictability and efficient planning are crucial requirements to ensure long-term sustainability for any company. That’s why we use the most powerful algorithms and resources of machine learning to help the production planners to find the best scheduling option for its shopfloor, maximizing gains and reducing costs.
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