Out-of-stock situations and excess inventory exist at all levels of the supply chain and need to be reduced. Whip effects should be identified in the historical sales data and countermeasures implemented. Increasing product variety should be managed without the need for manual intervention.
The data silos are broken down by establishing an overarching data lake for centralised control of the entire supply chain. Automated sales forecasts for all stages of the supply chain are implemented with machine learning algorithms based on historical sales data. Forecasting unknown demand by modelling demand data from historical sales data.
A universal, automated forecasting environment is available that can be used flexibly at all stages of the supply chain. Whip effects are reduced through shorter response times and reliable forecasts at all stages.
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