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Supply Chain

AI-Driven Inventory & Logistics

A $400M regional distribution company (8 warehouses, 3,000+ retail locations)

$3.2M annual savings through demand forecasting and route optimization

KEY RESULTS

$3.2M
Annual savings: $1.4M inventory reduction, $800K transportation, $600K labor productivity, $400K avoided stockout losses
91%
Forecast accuracy at 30-day horizon, up from 62%. Model outperforms analysts on 94% of SKUs.
97.5%
Customer fill rate, up from 89%. Stockout rate dropped from 8.5% to 2.1%.
67 picks/hr
Warehouse productivity, up from 45 picks/hour. Same headcount, 49% more throughput.

Before & After

✗ Before

  • •Forecast accuracy: 62% (30-day horizon)
  • •Stockout rate: 8.5% of SKUs weekly
  • •Excess inventory: $4.2M write-off annually
  • •Warehouse picks per hour: 45
  • •Customer fill rate: 89%
  • •Transportation cost/unit: $2.40

✓ After

  • •Forecast accuracy: 91% (30-day horizon)
  • •Stockout rate: 2.1% of SKUs weekly
  • •Excess inventory: $1.8M write-off annually
  • •Warehouse picks per hour: 67 (49% increase)
  • •Customer fill rate: 97.5%
  • •Transportation cost/unit: $1.92 (20% reduction)

The Challenge

This distributor serves 3,000+ retail locations from 8 regional warehouses. Legacy forecasting used 12-week moving averages—useless for seasonal products, promotions, or demand volatility. The result: chronic stockouts on hot items, warehouses stuffed with slow movers, and transportation inefficiency from rushed emergency shipments.

PAIN POINTS

  • ×Forecasting was a spreadsheet exercise: 3 analysts spent full-time maintaining models that were 38% wrong
  • ×No visibility into demand signals: POS data from retailers arrived 2 weeks late, if at all
  • ×Inventory positioning was static: products sat in wrong warehouses while other regions faced stockouts
  • ×Warehouse routing was first-in-first-out, not optimized for pick efficiency
  • ×Transportation planning happened 12 hours before departure—no time to consolidate shipments

Technology Stack

  • →Demand sensing model incorporating POS data, weather, events, economic indicators
  • →LSTM neural network for time-series forecasting at SKU-location level
  • →Inventory optimization solver for multi-echelon positioning
  • →Dynamic pick-path optimization using real-time bin locations
  • →Route optimization engine with traffic, weather, and delivery window constraints

Implementation Approach

1

Phase 1: Demand Sensing Infrastructure (Weeks 1-4)

Built data pipeline to ingest POS data from top 200 retail accounts (65% of volume). Integrated external signals: weather forecasts, local events calendar, economic indicators. Reduced data latency from 2 weeks to same-day for participating retailers.

2

Phase 2: Forecasting Model Development (Weeks 5-10)

Trained LSTM model on 3 years of historical data across 12,000 SKUs and 8 warehouse locations. Model generates daily forecasts at SKU-location level with confidence intervals. Incorporated promotional calendar—promotions now improve forecast accuracy instead of destroying it. Achieved 91% accuracy on 30-day forecast horizon.

3

Phase 3: Inventory Optimization (Weeks 11-14)

Implemented multi-echelon inventory optimization. System recommends: safety stock levels by SKU-location, reorder points incorporating lead time variability, inter-warehouse transfers to rebalance inventory. Runs nightly; recommendations surface in morning planning meetings.

4

Phase 4: Warehouse & Transportation (Weeks 15-17)

Deployed dynamic pick-path optimization—routes pickers based on current order batch and real-time bin locations. Picks per hour increased 49%. For transportation: consolidated shipments, optimized routes accounting for traffic patterns and delivery windows. Reduced empty miles by 23%.

Results Breakdown

$3.2M

Annual savings: $1.4M inventory reduction, $800K transportation, $600K labor productivity, $400K avoided stockout losses

91%

Forecast accuracy at 30-day horizon, up from 62%. Model outperforms analysts on 94% of SKUs.

97.5%

Customer fill rate, up from 89%. Stockout rate dropped from 8.5% to 2.1%.

67 picks/hr

Warehouse productivity, up from 45 picks/hour. Same headcount, 49% more throughput.

Key Learnings

  • 1.Data quality is the bottleneck, not algorithms. We spent 40% of the project cleaning and validating data. Worth it.
  • 2.Demand sensing beats demand forecasting. Same-day POS data is worth more than a sophisticated model on stale data.
  • 3.Start with A-items (top 20% of SKUs driving 80% of revenue). Prove value there before expanding to the long tail.
  • 4.Inventory optimization is a system problem. Optimizing one warehouse while ignoring others creates whack-a-mole.
  • 5.Transportation savings compound. Better forecasting → fewer emergency shipments → better route consolidation → lower cost/unit.

Industry Context

Amazon's AI-driven supply chain is the industry benchmark: anticipatory shipping, dynamic inventory positioning, and sub-day delivery in major metros. Early AI adopters in distribution see 15% logistics cost reduction and 35% inventory improvement. McKinsey estimates AI can reduce forecasting errors by 50% and supply chain costs by 5-10% across industries.

TIMELINE

120 days for full implementation

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