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How a Dairy Company Optimized Production Capacity by Adjusting Their Portfolio Mix in Asia

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-35%

decline in units over 4 weeks, while maintaining shelf space*

-29%

decline in units over 4 weeks, while maintaining shelf space**

+600 Basis Points

Time saved on RGM analytics in a dynamic market environment

*Impact predicted by Buynomics **Real Impact measured
Future-proofing RGM with AI

Background

PPA  |    iconscasestudyanon03  Food & Beverage   |   iconscasestudyanon01 Global

The multinational food and beverage company is a leading provider of dairy and plant-based products, bottled water, and nutritional items.

With a well-established presence in Europe and North America and expanding reach worldwide, their mission emphasizes initiatives that promote nutrition and wellness.

The company faced production capacity limits in Asia when offering both low-end and high-end yogurt brands simultaneously. They sought a solution to optimize their product portfolio .

Challenges

The manufacturer sought to explore a range of outcomes by challenging the assumption that fewer SKUs will lead to less shelf space.


Production capacity limitations

The manufacturer was facing production capacity limits, as they
were selling a cheaper product at a high volume. 

 


Transition from a low-end to a high-end product mix

The manufacturer wanted to transition from their low-end product to a high-end bio product.

 


Shelf space allocations

The shelf space allocated to fewer non-premium SKUs was uncertain,
as retailers and consumers highly demanded the low-end product.

 

Solution

By partnering with Buynomics, the team was able to model the delisting of their least profitable but still popular SKUs from
supermarkets and convenience stores.

 

Main strategies tested

  • Delisting 2 low-end SKUs with and without maintaining shelf space.
  • Delisting 3 low-end SKUs with and without maintaining shelf space (while raising prices on remaining SKUs).

 

Results

Optimal Scenario: The optimal scenario identified was that delisting three low-end SKUs would lead to decline in unit sales of -35% while maintaining a shelf space and raising prices.

Negative Scenario: It was identified that removing just two SKUs would have minimal effect on the necessary factory capacity.

 

Results

By leveraging the Buynomics tool, the team was able to quickly and accurately understand how portfolio changes would impact their revenue.

 

Impact predicted by Buynomics

-35% 

Decline in units over 4 weeks, while maintaining shelf space.

Real impact measured

-29%

Decline in units over 4 weeks, while maintaining shelf space.

Basis points

+600

Difference of Buynomics’ prediction in units sold vs. actual units sold.

 

 

Make better RGM decisions, faster!

Run agent-based simulations with Buynomics’ Virtual Shoppers AI to optimize all revenue levers, capturing cross-effects, cannibalization, and competition.

2-4%

Profit impact*

95%

Predictive Accuracy*

80%

Faster Decision-making

*Depending on data quality and completeness