Logistics & Supply Chain Solutions

Demand Sensing & Forecasting

Supply chain planners rely on statistical forecast models that use 12-24 months of historical shipment data, missing the real-time POS signals, promotional lift events, and new product introduction (NPI) patterns that actually drive near-term demand variability. The resulting forecast error cascades into either excess safety stock that ties up working capital or stockouts that trigger expedited replenishment at premium freight cost, often LTL spot rates 40-60% above contracted FTL lane rates.

Built For

Demand Planning Manager responsible for a 15,000 SKU forecast across 8 distribution regions with a 12-week rolling planning horizon

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Lumi Supply

Demand Planner

Integrates POS signals, promotional calendars, and seasonal decomposition into a consensus demand forecast with daily deviation alerts.

POS Signal Integration
Promotional Lift Modeling
Stockout Risk Scoring
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Lumi Warehouse

Inventory Planner

Translates demand forecast updates into safety stock adjustments and replenishment order recommendations by DC.

Safety Stock Recalculation
Replenishment Triggers
DC Inventory Balancing
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Lumi Route

Transportation Planner

Connects demand surge signals to inbound freight mode decisions, switching from LTL to FTL or expedited when lead time compression is needed.

Inbound Mode Selection
Expedite Decision Support
Lead Time Compression
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How It Works

Multi-signal demand sensing that integrates POS data, promotional calendars, seasonal decomposition, and NPI ramp curves into a consensus forecast that updates daily and flags deviation alerts.

Instant Analysis

Drag and drop your CSVs. No complex pipelines required.

Natural Language

Ask questions in plain English, get instant answers.

Lumina Analyst
Which lanes have the highest deadhead percentage this month, and what is the revenue impact?

Analyzing TMS data... Three lanes account for 68% of empty miles this month: Chicago-Detroit (31% deadhead), Dallas-Houston (24%), and LA-Phoenix (13%). Combined revenue loss from unloaded miles is estimated at $187,000. Backhaul matching opportunities exist on the Chicago-Detroit corridor based on shipper demand signals.

Deadhead Rate by Lane vs Revenue Impact
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Related Capabilities

demand sensing softwaresupply chain forecastingPOS data integrationpromotional lift forecastingnew product forecast model