Supply chain · Production

Blue Yonder AI-driven demand and supply planning cuts waste 15% and planning time 86% at Charoen Pokphand Foods

The problem

Charoen Pokphand Foods required end-to-end supply chain visibility and faster cutting optimization calculations; existing manual Excel-based processes took one week per cycle, too slow for highly perishable meat products.

First attempt

Excel-based cutting optimization workflows took one week to compute and were explicitly described as not effective for managing highly perishable meat products.

Workflow diagram · grounded in source
1
Multi-source data lake ingestion
integration
“This data lake takes automatic feeds from Blue Yonder, IoT (internet-of-things) devices and cameras used in hatcheries and farms to monitor the growth and welfare of animals. At the other end of the supply chain, POS (point-of-sale) data…”
2
AI demand prediction
ai_action
“CPF incorporates down-stream demand data from retailers' POS systems into the planning process and leverages artificial intelligence (AI) to improve demand predictions”
3
Cutting optimization calculation
ai_action
“Using Excel, the time to calculate the cutting optimization process previously took one week— which was not effective when dealing with highly perishable meat products where time and freshness is of the essence. Now, these calculations c…”
4
Animal feeding optimization
ai_action
“With insights from Blue Yonder, the process can be optimized to balance the exact demand requirements with the timing and sizing of animals”
5
Sustainability KPI capture
output
“The data lake is used to capture sustainability numbers based on actual versus planned key performance indicator (KPI) reports to measure food wastage, and energy consumption. This is essential disclosure for CPF's exports to its Europea…”
Reported outcome

Blue Yonder reduced residual waste from meat-cutting operations by 15%, cut optimization calculation time by 86% (from one week to around one day), and enabled carbon emissions tracking required for CPF's European export compliance.

Reported metrics
Residual waste from meat-cutting operations15%
Cutting optimization calculation time86%
Cutting optimization calculation time (previous)one week
Cutting optimization calculation time (current)around one day
Reported stack
Blue YonderIoT
Source
https://blueyonder.com/customers/charoen-pokphand-foods
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Blue Yonder reduced residual waste from meat-cutting operations by 15%, cut optimization calculation time by 86% (from one week to around one day), and enabled carbon emissions tracking required for CPF's European exp…

What tools did this team use?

Blue Yonder, IoT.

What results were reported?

Residual waste from meat-cutting operations: 15%; Cutting optimization calculation time: 86%; Cutting optimization calculation time (previous): one week; Cutting optimization calculation time (current): around one day (source-reported, not independently verified).

What failed first in this deployment?

Excel-based cutting optimization workflows took one week to compute and were explicitly described as not effective for managing highly perishable meat products.

How is this supply chain AI workflow structured?

Multi-source data lake ingestion → AI demand prediction → Cutting optimization calculation → Animal feeding optimization → Sustainability KPI capture.

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