Supply chain · Production

Blue Yonder AI-powered demand and supply planning helps Martin Brower achieve 95%+ forecast accuracy and reduce waste by up to 30%

The problem

Martin Brower faced fragmented data across its supply chain, making it difficult to generate reliable forecasts, avoid excess inventory, and align supply with real-time demand fluctuations across diverse markets and recycling programs.

Workflow diagram · grounded in source
1
Fragmented data integration
integration
“Blue Yonder's demand and supply planning and fulfillment solutions address the company's fragmented data, helping to generate more reliable forecasts and avoid excess inventory”
2
ML demand classification
ai_action
“The demand classification capability has sharpened the company's ability to identify trends, leading to an improvement of up to 2.5% in forecast accuracy while optimizing stock levels and reducing excess inventory”
3
Dynamic demand response
ai_action
“Powered by Blue Yonder's demand response capability, these adjustments help Martin Brower react dynamically to changing demand signals, contributing to improved accuracy and efficiency”
4
AI promotions planning
ai_action
“with Blue Yonder's promotions planning and AI-driven predictions, Martin Brower can adapt more effectively to market shifts, offering alignment with demand trends while improving resource allocation and customer engagement”
5
Waste volume prediction
ai_action
“Leveraging Blue Yonder tools, Martin Brower has created data systems and restaurant engagement strategies to predict the amount of waste each restaurant should generate and track recycling rates”
6
Fulfillment replenishment output
output
“Martin Brower's expert teams, together with Blue Yonder Fulfillment, efficiently replenish our distribution centers and restaurant customers across France, the UK, Canada, Australia, New Zealand, and the U.S.”
Reported outcome

Martin Brower achieved forecast accuracy exceeding 95% in certain markets, reduced manual workloads by up to 25–40%, cut processing time by an average of 30%, reduced product wastage by up to 30%, and reduced costs by up to 20%.

Reported metrics
Forecast accuracy improvementup to 2.5%
Forecast accuracy level in certain marketsexceeding 95%
Manual workload reductionup to 25–40%
Processing time reduction30% average
Show all 6 reported metrics
forecast accuracy improvementup to 2.5%
forecast accuracy level in certain marketsexceeding 95%
manual workload reductionup to 25–40%
processing time reduction30% average
product wastage reductionup to 30%
cost reductionup to 20%
Reported stack
Blue YonderBlue Yonder Fulfillment
Source
https://blueyonder.com/customers/martin-brower
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Martin Brower achieved forecast accuracy exceeding 95% in certain markets, reduced manual workloads by up to 25–40%, cut processing time by an average of 30%, reduced product wastage by up to 30%, and reduced costs by…

What tools did this team use?

Blue Yonder, Blue Yonder Fulfillment.

What results were reported?

Forecast accuracy improvement: up to 2.5%; Forecast accuracy level in certain markets: exceeding 95%; Manual workload reduction: up to 25–40%; Processing time reduction: 30% average (source-reported, not independently verified).

How is this supply chain AI workflow structured?

Fragmented data integration → ML demand classification → Dynamic demand response → AI promotions planning → Waste volume prediction → Fulfillment replenishment output.

Related supply chain cases