Consolidate Distribution Centers
Network Optimization Use Case
Distribution center consolidation reduces total network costs by 20-35% while maintaining service levels. This use case demonstrates how a mid-market company reduced 8 distribution centers to 5 strategic locations, achieving $3.6M in annual savings with a 1.5-month payback period. The strategy balances fixed costs, variable costs, and shipping distances to find the optimal network configuration.
Multiple facilities below 60-70% capacity. Overlapping service areas. Facility costs dominate network expenses. Demand patterns shifted. M&A created redundancy. Growth capital constrained by underutilized assets.
Service levels can't be maintained. Facilities serve distinct markets. All locations near capacity. Regulatory or tax benefits require multiple sites. Risk mitigation needs geographic diversity.
Note: All cost figures, percentages, and metrics in this section are theoretical and illustrative. Actual results vary based on specific network characteristics, market conditions, and implementation approach.
Most mid-market companies operate too many distribution centers. Growth, acquisitions, and shifting demand create redundant facilities with overlapping coverage and low utilization. This drives excess fixed costs, higher transportation, duplicated inventory, and management overhead. Research indicates that suboptimal network configurations can cost companies 20-35% more than necessary for equivalent service levels.
The total logistics cost structure follows a well-established theoretical framework where total cost equals the sum of fixed, variable, and shipping costs. This relationship creates a U-shaped total cost curve as the number of distribution centers changes.
Total Cost = Fixed + Variable + Shipping. Optimal point minimizes total cost.
Academic and industry research provides several key insights into distribution network optimization:
Sources: Integrated optimization research (arXiv:2505.01808); Holistic cost management (London Journal of Business and Economics); Demand uncertainty frameworks (MIT Center for Transportation & Logistics); Resilience tradeoff analysis (Journal of Civil Engineering and Management).
Recent economic trends amplify the importance of network optimization:
Based on academic research and industry case studies, typical consolidation impacts fall within these theoretical ranges:
These ranges are theoretical and based on aggregated research findings. Actual results depend on specific network characteristics, market conditions, demand patterns, and implementation approach.
$150M consumer goods distributor. 8 DCs across 6 states serving 2,500 retail locations. $12M annual network cost.
| DC Location | Annual Cost | Utilization | Primary Service Area |
|---|---|---|---|
| Chicago, IL | $2.1M | 85% | Midwest |
| Atlanta, GA | $1.8M | 75% | Southeast |
| Dallas, TX | $1.6M | 70% | Southwest |
| Philadelphia, PA | $1.4M | 65% | Northeast |
| Denver, CO | $1.2M | 55% | Mountain West |
| Kansas City, MO | $1.1M | 50% | Central |
| Memphis, TN | $1.0M | 45% | South Central |
| Indianapolis, IN | $0.8M | 40% | Midwest |
Chicago and Indianapolis overlap in Midwest coverage. Four facilities run below 60% capacity. Memphis-Dallas proximity drives unnecessary transport costs. Redundant coverage across multiple regions.
Evaluated four scenarios: consolidate to 4, 5, or 6 DCs, or optimize assignments across all 8. Analysis weighed service levels, transport costs, utilization, and implementation complexity.
| DC Location | Action | New Capacity | Service Coverage |
|---|---|---|---|
| Chicago, IL | Expand (absorb Indianapolis) | 120% of current | Entire Midwest + Central |
| Atlanta, GA | Maintain | 100% | Southeast |
| Dallas, TX | Expand (absorb Memphis) | 115% of current | Southwest + South Central |
| Philadelphia, PA | Maintain | 100% | Northeast |
| Denver, CO | Maintain | 100% | Mountain West |
| Kansas City, MO | CLOSE | — | Covered by Chicago |
| Memphis, TN | CLOSE | — | Covered by Dallas |
| Indianapolis, IN | CLOSE | — | Covered by Chicago |
| Current Annual Network Cost: | $12,000,000 |
| Optimized Annual Network Cost: | $8,400,000 |
| Annual Savings: | $3,600,000 (30% reduction) |
| One-Time Implementation Cost: | $450,000 |
| Payback Period: | 1.5 months |
| 3-Year NPV (10% discount): | $9,400,000 |
| Cost Category | Before | After | Savings |
|---|---|---|---|
| Facility Operating Costs | $6,000,000 | $3,750,000 | $2,250,000 (37.5%) |
| Transportation Costs | $4,500,000 | $3,900,000 | $600,000 (13.3%) |
| Inventory Carrying Costs | $1,200,000 | $600,000 | $600,000 (50%) |
| Management & Overhead | $300,000 | $150,000 | $150,000 (50%) |
Service levels hold at 98% on-time delivery. Transit time increases from 1.9 to 2.1 days—acceptable. Utilization jumps from 58% to 82%. Inventory drops 25%. Management overhead down 37.5%.
Our network optimization software enables data-driven consolidation decisions through a simple three-step process: analyze your current baseline, configure optimization parameters, and compare optimized scenarios.
Visualize your current network with all 8 distribution centers, flow volumes, and service coverage areas. Identify overlapping service areas and underutilized facilities to understand consolidation opportunities.
Configure optimization parameters to test consolidation scenarios. Set facility capacity constraints, minimum facility requirements, and cost parameters to model different consolidation strategies.
Compare the optimized network (5 DCs) against your baseline. See cost savings breakdown, facility utilization improvements, and service level impacts to make data-driven consolidation decisions.
8-month phased approach minimizes risk while maintaining service levels.
Complete analysis, validate service requirements, develop transition plan, secure approval.
Expand Chicago and Dallas to absorb closing facility volume. Upgrade systems for increased capacity.
Gradually shift volume to expanded locations. Update customer assignments, transfer inventory, reassign staff.
Close Indianapolis, Memphis, and Kansas City. Optimize routes, tune inventory, monitor service levels.
