Case Study · Supply Chain & Manufacturing

AI-Driven Network Optimization — Consumer Durables

Supply Chain · Geospatial Analytics · Cost Modeling · Demand Forecasting · Optimization

The challenge

Costly, ad-hoc distribution.

Suboptimal routing & rising logistics costs

Unoptimized routes inflated freight spend and reduced delivery efficiency across distribution tiers.

Non-standard, ad-hoc supply transfers

Unplanned transfers bypassed supply routes, undermining cost controls and predictability.

Imbalanced warehouse utilization

Misallocated inventory across warehouse tiers caused bottlenecks and excess holding costs.

Limited network visibility & scalability

No unified view of cost drivers or metrics, blocking growth planning and performance benchmarking.

What we built

Geospatial cost-optimized network.

Geospatial route & catchment optimization

Re-mapped stores to optimal supply nodes using a real-world distance matrix and geospatial analytics.

Cost-minimization optimization model

Balances freight, processing, and warehouse costs against capacity to redesign distribution economically.

Simulation & scenario modeling

Built simulation capability for distribution scenarios — enabling what-if planning for network redesign.

Interactive intelligence dashboards

Real-time dashboards surfacing network diagnostics, anomalies, and optimization benchmarks for leadership.

Results

Quantified outcomes.

3 Tiers

Distribution tiers redesigned — central, regional & local.

6

Analytics capabilities — EDA, geospatial, optimization, forecasting, simulation, visualization.

0

Reliance on ad-hoc transfers — eliminated via optimized supply node mapping.

1

Unified cost-optimization model for nationwide distribution.

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