Optimizing order pattern insights using machine learning


World-wide leader in manufacturing & distribution on health, beauty & home products, via a direct selling with a multi-level marketing strategy.

They are facing enourmous pressure on their warehouse operations, in terms of both asset utilization and workforce capcity. This is caused by a high volatility and unpredictability in orders placed by their customers, which have to be delivered within a limited number of days.

Especially unexpected high peaks in demand are causing high stress on the warehouse and potentially a decreased service level.


By leveraging internal and external data, combined with LOP’s machine learning models, they have built a prediction model for their short term orders & installed an alert system which predicts upcoming peak days.

In this way they have a better visibility on what’s potentially coming, and anticipation actions can be taken. Moreover, they are gathering more and more information on what the ultimate drivers of orders are, and better

  • Create a virtual twin

  • Get end-to-end visibility

  • Set smart parameters

  • Optimize end-to-end financials

  • Monitor the control tower


Visibility on predicted peak days

Anticipation possibilities for operations

Identification of influencing factors

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