supply_chain · manufacturing · workflow
Blue Yonder AI-driven demand and supply planning cuts waste 15% and planning time 86% at Charoen Pokphand Foods
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.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Multi-source data lake ingestion
The data lake takes automatic feeds from Blue Yonder, IoT devices and cameras on farms, and POS data from retailers.
Tools used
Blue YonderIoT
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.
What failed first
Excel-based cutting optimization workflows took one week to compute and were explicitly described as not effective for managing highly perishable meat products.
Results
Time saved86%
Volume15%
Grounding & classification
Source type: vendor customer story
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forecastingpredictive analyticsfailure mode describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedagriculturemanufacturingcost reductioncycle time reductiontime savedvendor customer storylogistics opssupply chaindata sync enrichmentmonitor detect alert