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From Source to Shelf, a Round Up of Retail AI Innovation

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Kaiser’s Tengelmann Automates Replenishment

“For us, introducing machine learning algorithms into core retail and replenishment processes is an important investment in the future of our merchandise operations and supply chains.”

Dr. Henrik Haenecke, Managing Director,
Kaiser’s Tengelmann

Demand Forecast & Replenishment

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The Challenge

Kaiser’s Tengelmann needed to improve the operational efficiency and cost of store-based replenishment. Historically, sales forecasting at Kaiser’s Tengelmann was mostly done manually by employees. With Blue Yonder’s solution, they wanted to optimize and scale processes to deliver profitable store replenishment.

The Solution

The company opted for Replenishment Optimization because the solution could easily be adapted to the various stores’ different requirements. Additionally, it flexibly incorporates external factors into its analyses, which is reflected in the resulting forecasts.

The Results
  • Out-of-stock situations reduced from 6% to 0.5%
  • Significant profit gains
  • Reduced workload in demand planning
  • Better demand forecasts and fewer write-offs

Interview with Mark Michaelis, Kaiser’s Tengelmann

Mark Michaelis describes why Kaiser’s Tengelmann chose Replenishment Optimization and how the solution benefits the business.


Demand Forecast & Replenishment

Reduce out-of-stock rates by up to 80% without increasing waste or inventory

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  • 10x

    increased product availability

  • 80%

    out-of-stock rate reductions

  • 50x

    fewer manual interventions

Businesses Using Replenishment Optimization

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Kaufland Optimizes Its Replenishment Process

Read the success story

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OTTO Uses Replenishment Optimization to Impress Its Customers and Increase Profits

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Some book

Successful Demand Forecasting at dm

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Some book

Natsu Optimizes Its Sales Planning and Reduces Leftover Stock by 20%

Read the success story