Supply Chain Optimization for A Major Retailer: Choosing the Right Location for Warehouses
公司规模
1,000+
地区
- Europe
国家
- Russia
产品
- anyLogistix
技术栈
- GIS
- Simulation Modeling
实施规模
- Enterprise-wide Deployment
影响指标
- Cost Savings
- Customer Satisfaction
- Productivity Improvements
技术
- 分析与建模 - 预测分析
- 功能应用 - 库存管理系统
- 功能应用 - 仓库管理系统 (WMS)
适用行业
- 零售
适用功能
- 物流运输
- 仓库和库存管理
用例
- 库存管理
- 供应链可见性(SCV)
- 仓库自动化
服务
- 软件设计与工程服务
- 系统集成
关于客户
Eldorado Company is a major electronics retailer in Russia, operating in 350 cities across the country. The company is known for its extensive network of retail stores and its commitment to providing a wide range of electronic products to its customers. With a large customer base and a significant number of stores, Eldorado faces the challenge of efficiently managing its supply chain to meet customer demand while minimizing costs. The company sought to optimize its warehouse locations and operations to improve delivery times, reduce storage expenses, and enhance overall customer satisfaction. By leveraging advanced supply chain optimization tools, Eldorado aimed to streamline its logistics and maintain its competitive edge in the market.
挑战
Eldorado Company, a huge electronics retailer in Russia, with stores in 350 cities, needed to determine the optimal number of warehouses, and where they should be situated, in order to better fulfill customer demand and minimize delivery and storage expenses. The analysis showed that the problem could be solved with introduction of the anyLogistix supply chain optimization system. Input data provided by the customer described potential warehousing points: rent cost, investments for building new or modernizing old warehouses, average level and cost of storage, overall costs for staffing and security, etc. In addition, the anyLogistix simulation model considered the warehouse and retail store GIS coordinates, and distances between cities.
解决方案
The introduced system allowed the client to simulate, in detail, several kinds of activities: • Daily basis (model time): goods are sold in stores, and losses from the shortage of demanded goods are counted. • Weekly basis: inventory is supplemented to target level, transportation costs are counted, and deferred payments to suppliers are planned. • Monthly basis: warehouse levels are renewed according to monthly sales levels of stores, transportation routes from warehouses to stores are generated, and franchisee shipments are planned. Monthly sales numbers conform to average sales numbers, while daily sales are generated stochastically. Users can carry out several experiments with the model: • In a simple simulation experiment, a user manually chooses warehouses from the list to test the desired scenario, and launches the model in order to receive statistics for this specific network configuration. • Parameter variation experiment checks all possible scenarios of warehouse positioning, taking into account “fixed” warehouses and their maximum number. The result of this experiment is the best combination of warehouses that cost the least amount of money. • Based on this configuration of the supply chain, optimization experiment can calculate in-store warehouses’ floor space.
运营影响
数量效益
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