Redesigning a Regional Supply Chain Network
A multinational consumer goods manufacturer with multiple plants, dozens of distribution centers, and thousands of customer locations.
potential annual savings identified in the benchmark market
production sourcing, distribution flows, and inventory positioning optimized together
framework now being extended to additional markets in the region
The Challenge
The network had evolved incrementally over several years. Customers entered and exited the market, demand shifted across geographies, and the product portfolio expanded, but the manufacturing and distribution network was never redesigned at the same pace. Products were not always made, stored, and shipped through the most cost-efficient combination of facilities.
Three questions had to be answered together: which plants should produce each SKU, which plants should supply each distribution center, and which SKUs should be stocked at each location. None of them could be optimized in isolation: changing where an SKU is produced changes transportation, throughput, inventory placement, handling costs, and customer service.
On top of that sat the strategic questions: whether to open new distribution centers, close or consolidate existing ones, or relocate facilities closer to future demand. Without a unified model, these decisions relied on historical practice, local assumptions, and manually built scenarios.
What We Built
We built an integrated production, inventory-location, and distribution optimization engine that redesigns the network end to end: SKU allocation across plants, flows from plants to distribution centers, customer allocation, SKU placement, and facility opening, closure, consolidation, and relocation scenarios, all inside one model.
The engine works from the company’s real constraints: plant capacities and capabilities, SKU eligibility, distribution-center capacities, customer demand, and the full cost structure across materials, manufacturing, transportation, and handling. Production and distribution decisions minimize total supply chain cost; inventory-location decisions weigh the cost of goods sold and the financial impact of positioning each SKU at each location.
A scenario-analysis layer on top lets decision-makers test alternative network strategies against the existing network on a consistent cost and service basis: opening or closing a facility, relocating a distribution center, demand growth, capacity expansion, or a different sourcing footprint.
The Results
- Approximately $10 million in potential annual savings identified in the benchmark market
- Better SKU-to-plant sourcing, plant-to-distribution-center allocation, and customer-to-location assignment
- Optimized inventory positioning with lower transportation and handling costs
- Facility consolidation and relocation opportunities quantified instead of guessed
- Network design transformed from a periodic consulting study into a repeatable, data-driven process the team runs itself
What changed: Instead of a one-time study, the company now continuously evaluates how changes in demand, customers, products, and costs affect the optimal network, and is extending the same framework across the region.
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