- Logistics
Port Logistics
Every changeover of containers costs money. Precise forecasts of their transport routes and times reduce unnecessary container movements. In particular, the correct sequence when stacking containers holds significant savings potential. Self-learning algorithms react immediately to changes in the market and replace less flexible sets of rules.
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Save time -
Reduce costs
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Plan more precisely
- Logistics
Optimized picking
Limited capacities of personnel or machines make it necessary to pick orders from customers as early as possible while avoiding costly and time-consuming multiple deliveries.
Precise forecasts help to improve service levels and at the same time relieve staff - especially during periods of high workload.
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Relieve employees
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Increase service level
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Save shipping costs
- Logistics
Demand forecast
Defects cost money and damage the customer relationship. To avoid defects, companies need precise demand forecasts at item level. Machine learning can take into account all factors influencing demand and therefore improves the precision of the forecast compared to conventional methods.
