Concept of data-driven supply chain management

With the help of sales forecasts and demand forecasting, the entire supply chain is to be optimised for a beverage company.
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Significant reduction of the whip effect, which increases as the level in the supply chain rises.
Reduction of out-of-stock situations despite simultaneous reduction of stocks
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Central calculation of order proposals enables transition to vendor-managed inventory: "Delivery is already waiting at the door when the order is actually only supposed to be placed".

Challenge

  • Out-of-stock situations and excess inventory exist at all levels of the supply chain and need to be reduced
  • Whip effects are to be identified in the historical sales data and countermeasures implemented
  • Increasing product diversity should be managed without the need for manual intervention

Solution

  • Breaking down the data silos of the various stages and establishing an overarching data lake for centralised control of the entire supply chain
  • Implement automated sales forecasts for all stages of the supply chain with machine learning algorithms based on historical sales data.
  • Forecasting the unknown demand by modelling the demand data from the historical sales data

Result

  • A universal, automated forecasting environment that can be flexibly deployed at all stages of the supply chain
  • Reduction of the whip effect through shorter response times and reliable forecasts at all levels

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