Logistics network optimization

• Analytics & Modeling - Predictive Analytics
• Functional Applications - Enterprise Resource Planning Systems (ERP)
• Functional Applications - Warehouse Management Systems (WMS)

• Telecommunications

• Logistics & Transportation
• Warehouse & Inventory Management

• Inventory Management
• Supply Chain Visibility
• Warehouse Automation

• Data Science Services
• System Integration

The Client, a major Russian telecom company, faced the challenge of optimizing its logistics network to match future demand forecasts and inventory optimization targets. The company operated a single-tier logistics network with around 150 warehouses, 2,500 points of sale, and about 20,000 operational sites holding inventory. The Client sought to develop an optimal logistics network model that would maintain a high level of logistics service at operational sites and points of sale while addressing the following key questions: the optimal hierarchy for the network, the costs and effects of implementing various inventory optimization policies, the required capacity and locations of warehouses considering future demand, and the optimal network transformation plan, including inventory relocation and timeline of warehouse closings.

The Client is a major Russian telecom company operating a countrywide transportation network. The company provides a wide range of telecom services, including B2C (business to customer), B2B (business to business), B2O (business to operator), and B2G (business to government) services. With a single-tier logistics network comprising around 150 warehouses, 2,500 points of sale, and about 20,000 operational sites holding inventory, the Client sought to optimize its logistics network to meet future demand forecasts and inventory optimization targets. The company aimed to maintain a high level of logistics service at operational sites and points of sale while addressing key questions related to network hierarchy, inventory optimization policies, warehouse capacity and locations, and the network transformation plan.

Deloitte CIS was chosen to develop an optimal logistics network model for the Client. The project involved answering key questions related to the network hierarchy, inventory optimization policies, warehouse capacity and locations, and the network transformation plan. The team defined three key optimization hypotheses: reducing the number of small warehouses, redirecting delivery to seven regional distribution centers (DCs), and consolidating inventory in regional DCs. Eight initiatives were developed to address these hypotheses, and a business case was calculated based on network optimization benchmarks. The team also developed an optimization concept and AS-IS network model, tuned up automated logistics data processing from the ERP system, and produced a current logistics network performance dashboard. Six TO-BE scenarios were designed with three network structures and two inventory policies for each structure. The team conducted TO-BE scenario modeling, defined annual logistics costs for six scenarios, and created visual dashboard reports for each scenario to validate results with stakeholders. The optimal scenario for each of the seven regions was selected based on cost, service level, and implementation risks, and a target scenario was composed based on the combination of optimal scenarios for the seven regions. A logistics network transition plan was developed to guide the transition from the current network to the target state.

• The project resulted in a step-by-step road map for transitioning from the current network to the target state, taking into account ongoing projects and other network-related opportunities.
• Recommendations were provided on inventory policies considering network optimization scenarios.
• The project involved a significant increase in logistics efficiency, including a reduction in integral logistics costs, the number of warehouses, and transportation costs.

• Reduction of integral logistics costs by 9%
• Reduction of the number of warehouses by 28%
• Transportation cost reduction by 5%