Inventory Optimization

🎯 AI-Driven Inventory Optimization

Neural networks active • 2,847 SKUs analyzed • Next optimization: 2 hours

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OPTIMIZATION

Capital Release Opportunity

Machine learning algorithms identified $2.3M in excess inventory across 247 slow-moving SKUs. AI recommends gradual reduction over 90 days to maintain service levels.

Capital release: $2.3M Service level: Maintained at 99.1% Risk level: Low (12%)

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STOCKOUT RISK

Safety Stock Adjustment Required

Deep learning models predict 34% stockout probability for 18 critical SKUs within 14 days. Demand volatility increased 23% due to seasonal patterns.

At-risk SKUs: 18 Stockout probability: 34% Buffer increase needed: +15%

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TURNOVER

Inventory Velocity Optimization

AI-powered velocity analysis suggests rebalancing 156 SKUs to achieve 18% improvement in inventory turnover while reducing carrying costs by $420K annually.

Turnover improvement: +18% Cost reduction: $420K/year SKUs affected: 156

🧠 AI Optimization Performance

Models trained and active

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97.2%

Forecast Accuracy

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$3.2M

Cost Savings YTD

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247

Auto-optimizations

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7.2x

Avg Turnover

AI Model Performance

Optimization Impact

AI-Recommended Inventory Adjustments

SKU Code	Description	Current Stock	AI Recommended	Adjustment	Impact	Confidence	Priority
SC-4521	Steel Component Grade A	142 units	285 units	+143 units	$12K savings	98.7%	Critical
PM-7832	Polymer Material Blue	2,450 kg	1,850 kg	-600 kg	$45K release	95.3%	Medium
EL-9911	Electronic Module X1	856 units	1,100 units	+244 units	$8K cost avoidance	87.1%	High
PK-5634	Packaging Material A4	15,000 units	8,500 units	-6,500 units	$78K release	92.8%	Low
RT-2389	Raw Textile Material	3,200 meters	4,200 meters	+1,000 meters	$15K cost avoidance	94.5%	Medium