Schneider Electric

Schneider Electric operates one of the most complex logistics networks in industrial electronics, spanning 240 manufacturing facilities and 110 distribution centers worldwide. Coordinating raw material acquisition and finished goods delivery across this footprint requires managing hundreds of thousands of transportation lanes, freight rates, policies, and flow constraints simultaneously — a scale that makes manual analysis unreliable and incomplete. Without a systematic optimisation framework, routing decisions were made without full visibility into the interdependencies across the network, leaving cost inefficiencies and underutilised container capacity embedded and largely invisible. The data complexity alone — over 200,000 transportation policy data points and 130,000 routing constraints — made the status quo unsustainable at enterprise scale.

Schneider Electric developed a machine learning model to analyse its entire global transportation network, ingesting 200,000 transportation policy data points and 130,000 flow and routing constraints. To make the problem computationally tractable, the team first consolidated 300,000 individual SKUs into 1,800 product groups by clustering on attributes including origin, stocking type, and product family. This 99.4% dimensionality reduction was a deliberate prerequisite — not an afterthought — that allowed the model to evaluate more than 150 distinct routing scenarios across the full network. The ML model assessed existing lanes, freight rates, capacity constraints, and policy rules simultaneously, identifying optimal product flow paths for both inbound raw material sourcing and outbound distribution. No third-party vendor is identified in the public record; the initiative appears to have been led internally by Schneider Electric's supply chain analytics function.

The optimisation programme delivered €8 million in transportation cost savings, the primary financial outcome of rationalising routing decisions across a 350-facility global network. Alongside cost reduction, the initiative achieved measurable improvement in container utilisation across international freight lanes, reducing wasted capacity that compounds costs at scale. The SKU consolidation also simplified ongoing supply chain planning, establishing a leaner product taxonomy for future modelling. Key metrics:

• €8 million in transportation cost savings
• 300,000 → 1,800 product groups (99.4% SKU reduction)
• 200,000+ transportation policy data points modelled
• 130,000 flow and routing constraints incorporated
• 150+ scenarios evaluated to identify optimal product flows

• Dimensionality reduction is a prerequisite, not an optimisation. Consolidating 300,000 SKUs into 1,800 product groups was what made the ML model tractable — without this step, the solution space is too large to evaluate meaningfully.
• Mature supply chains still contain hidden cost. The assumption that a large, established network is already optimised rarely holds; ML can surface savings that manual analysis misses by systematically exploring the full constraint space.
• Joint modelling of policies and physical constraints outperforms siloed optimisation. Treating transportation policy rules and flow constraints as a unified problem captures trade-offs that optimising each dimension separately would obscure.
• Scenario breadth matters. Evaluating 150+ initial scenarios provides confidence that the selected solution is globally rather than locally optimal.