Stout supported a Tier-One global automotive supplier in a large-scale manufacturing footprint optimization and restructuring initiative designed to improve profitability, resilience, and decision-making speed across its global operations. The engagement focused on optimizing a complex network of 50+ manufacturing plants spanning multiple geographies and product families under conditions of demand volatility, labor cost pressure, and evolving trade and tariff dynamics.

Using a supply-chain network optimization engine combined with scenario and sensitivity analysis, Stout evaluated alternative footprint configurations and product-family allocations across the plant network. The model incorporated plant-level economics, capacity constraints, logistics costs, labor profiles, and demand assumptions to identify value-maximizing strategies under a wide range of operating conditions.

Key Outcomes

$250M+ in Potential Savings Over Five Years

The optimization identified more than $250 million in cumulative five-year savings through data-driven plant rationalization, product-family transfers, and network rebalancing across 50+ global manufacturing sites.

Radically Accelerated Decision Making

What previously required two full-time resources working for approximately five weeks was reduced to minutes of optimizer runtime, enabling management to evaluate complex footprint decisions rapidly and consistently.

Expansion from 3 to 100+ Scenarios

The optimizer increased scenario throughput from 2–3 manually constructed cases to over 100 dynamically generated and evaluated scenarios, allowing leadership to assess resilience across a much broader range of demand, cost, and trade policy outcomes.

The analysis produced clear, defensible recommendations on which facilities to retain, consolidate, repurpose, or exit, supported by quantified EBITDA, cash flow, and operational impact under each scenario. Outputs were designed to support executive decision making, lender communication, and long-term strategic planning, providing a transparent and repeatable framework for managing uncertainty at scale.