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

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Replenishment Optimization

Increase profits and ensure optimal stock availability for every store

  • 10x

    increased availability

  • 80%

    out-of-stock rate reductions

  • 50x

    fewer manual interventions

  • Delivering waste reduction
  • Increasing efficiency gains
  • Enabling operational stability
  • Alignment with business strategy

Why Replenishment Optimization?

Blue Yonder Replenishment Optimization is a machine learning solution that allows automated store replenishment, reducing out-of-stock rates by up to 80% without increasing waste or inventory. The solution is based on accurate and granular order forecasts, enabling a weighted optimization of waste levels and product availability, while reducing unnecessary manual interventions.

Whether your products are perishable, subject to a fast turnover, or even have a long shelf-life, Blue Yonder Replenishment Optimization determines the optimal amount to order for every product and every store.

Replenishment Optimization translates your business strategy into thousands of daily replenishment decisions and is highly scalable across thousands of products and hundreds of locations. The solution delivers order proposals weeks in advance, freeing up your time to deliver your brand promise and meet changing customer expectations.

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Fresh Food Replenishment

Challenge

Fresh is the most difficult replenishment category to solve. Fresh items are perishable and overestimating their demand can rapidly generate waste as well as impact profitability.

Solution
  • Accurate predictions with a rich set of influencing factors, including weather
  • Knowing the uncertainties to select the optimal trade-off
  • Daily delivery of optimized replenishment decisions
Results
  • Less waste cost and higher profitability
  • Improved customer experience through better product availability in store
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Promotion Prediction

Challenge

Promotions influence sales — the challenge in retail is to accurately forecast their influence on sales.

Solution
  • Demand predictions integrate information on promotions
  • No need for a separate process for predicting promotional sales
  • Order strategy takes current promotions into account
Results
  • Increased automation and better predictions for promoted items
  • Product availability for promoted items is ensured
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Ambient Replenishment

Challenge

Inefficient store replenishment and an inconsistent product presentation can lead to empty shelves, poor customer experience, multiple manual efforts and lower sales figures.

Solution
  • Optimized and automated central replenishment process
  • Shelf space in store is included into replenishment optimization decisions
Results
  • Lower handling costs due to fewer manual interventions in store
  • Higher product availability with consistent presentation levels

How Does Replenishment Optimization Work?

Blue Yonder Replenishment Optimization for Retail

Highlights of Our Solution

  • Scientific optimization based on machine learning instead of a a rule-based system
  • Daily optimized replenishment decisions, essential for the fresh and perishable food category
  • Automated forecasting without daily adjustments of individual parameters or maintenance of static rules

Customers Using Replenishment Optimization

Some book

Supermarket Kaufland automates its central ordering with Replenishment Optimization

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Blue Yonder’s AI technology dramatically improves demand planning at Morrisons

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Natsu masters its demand planning with Replenishment Optimization

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Brochures

Replenishment Optimization: Achieve higher profits and optimal stock availability for every store

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Data sheets

Replenishment Optimization

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Studies

McKinsey: The Secret to Smarter Fresh Food Replenishment? Machine Learning

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Customer Experience in Grocery Retail: the Fresh Opportunity

A global survey of 4,000 consumers shines light on customer experience in fresh and product availability

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