In a complete supply chain network, the warehouse network is a core component, directly impacting efficiency, cost control, customer satisfaction, and market responsiveness. But what exactly is network design? What are its key points? How should it be carried out? To answer these questions, it's essential to first understand the structure of a supply chain network.
Products move between two states in the supply chain: static (at sites like factories, warehouses, distribution centers, etc.) and dynamic (during transportation). Static sites handle product processing, repackaging, sorting, and storage to prepare them for downstream movement or to buffer against future demand changes and supply disruptions. Transportation lines connect these sites, ensuring continuous product flow within the network. This interconnected network ensures efficient, accurate, and sustainable product delivery to end customers.
Warehouse network design involves constructing and adjusting the layout of points and lines within a supply chain network to enhance efficiency, reduce costs, and better serve customer needs. For instance, in the automotive industry, where manufacturing processes are complex and require numerous raw materials, effective factory location and raw material supply network setup ensure timely delivery of parts to the production line, reducing downtime and avoiding significant losses. For companies producing perishable food, placing warehouses near major consumer markets shortens transportation time and increases product freshness. In the case of large machinery manufacturing, where products are bulky and transportation costs are high, locating the warehouse network near major transport routes and markets can significantly reduce transportation and distribution costs. These examples highlight the importance of scientific and effective warehouse network design within the entire supply chain system.
Warehouse network design can range from rough to detailed work. In practice, decisions made solely based on experience and intuition can maintain the basic operation of the supply chain network, but this approach may lead to unnecessary resource waste. More importantly, once the warehouse network layout is determined, any subsequent modifications or adjustments may involve high costs. Therefore, data-driven and optimization algorithm-based warehouse network design, though requiring some upfront time and effort, is highly valuable in the long run. This method provides more accurate and scientific network design recommendations through thorough data analysis, aligned with business realities and considering future developments. These results are then adjusted and confirmed by combining the rich experience and judgment of professionals. Such a comprehensive strategy enables more scientific and rational warehouse network layouts, effectively improving logistics efficiency, reducing logistics costs, and providing a sustainable competitive advantage for enterprises.
Next, two case studies will illustrate how to make more scientific and effective warehouse network design decisions.
In network design, a common challenge is determining the optimal location for warehouses, factories, or stores—often referred to as the facility location problem in decision-making algorithms.
In one case, a rapidly growing retail company (referred to as Z Company) faced this issue. The company planned to add a distribution warehouse in either city A or city B to better serve hundreds of stores in these cities. After using the Convect Flow platform for analysis, the results showed that choosing the A location could reduce transportation costs by up to 70%.
Subsequently, the Z Company team input information from several potential sites they had considered during their initial research into the Convect Flow network design application. The optimization engine, taking into account factors like location, construction costs, and storage capacity, as well as store locations, demand, and transportation costs, identified the most cost-effective option that met their needs. This decision further reduced costs by 20% compared to the initial B site.
However, the decision-making process didn't stop there. The Z Company wondered if the optimization engine could directly recommend new warehouse locations without relying on their predefined options. They also considered whether having multiple warehouses might be more cost-effective. By removing constraints on the number of warehouses and potential sites, the optimization engine ultimately recommended two new warehouses and provided the corresponding service areas for each. This solution maintained the same total cost as the previous one while improving service levels by 2%.
The underlying business principles and algorithmic logic are straightforward. In a supply chain network, customer or demand points, such as retail stores or distribution points, often exhibit geographical clustering. Z Company's stores displayed this clustering, but it would have been difficult for a human to assess this manually across hundreds of stores. The model, however, quickly identified these clusters and made optimal decisions. Based on this principle, the Convect Flow network design optimization engine first grouped demand points using clustering methods, ensuring a certain level of service efficiency, response speed, and controllable transportation costs. It then selected potential warehouse locations based on these groupings. Finally, the engine identified the optimal warehouse locations and corresponding service areas based on user-defined objectives, such as service levels, distance, costs, and other detailed factors.
Of course, there are some considerations that are difficult to quantify in the warehouse site selection process, such as the impact of policies and regulations, employee accessibility, community and public relations, and geographical risks. If decision makers identify these factors based on experience, they can also set some alternatives as mandatory choices when using the optimization engine to make decisions. The optimization engine will select and expand on this basis, and provide corresponding terminal store coverage results. Algorithmic capabilities and manual experience complement each other, which can promote comprehensive, in-depth, flexible and effective analysis and decision-making of problems, and achieve twice the result with half the effort. In actual business, the construction of new sites or adjustment of site locations may not occur frequently. The problem we encounter more often is the adjustment of the service relationship between sites and demand points due to changes in demand, such as adjusting the distribution correspondence between warehouses and customers. This type of problem can also be solved with the help of the Convect Flow Network Design Application mentioned above. You only need to fix the warehouse list in the input data, and the model will skip the site selection decision step and directly match the relationship between warehouses and customer demand points based on the warehouse list provided by the user.
The site selection problem solves the layout of sites in the network, but in a network with fixed sites, there is still a route planning problem that needs to be solved. Route planning here not only refers to the service correspondence between upstream and downstream sites, but also includes more fine-grained routing routes, transportation methods, and transportation volume decisions. The case introduced below is about how to decide the flow relationship of products from factory to warehouse based on the needs of end customers when the locations of factories and warehouses are pre-determined.
Company Y is a dairy product manufacturer. Every month, each sales region formulates a future demand plan and then decides the route of the product from the processing plant to the warehouse, with the goal of minimizing transportation costs. The specific decisions to be made include:
- Factory-warehouse matching: the correspondence between processing plants and warehouses for each single product, and which processing plants or plants should supply each product warehouse.
- Supply quantity: For each product, the specific quantity of product that needs to be supplied between the processing plant and the warehouse that establish the supply relationship.
- Mode of transportation: For each product to be transported between the factory and the warehouse, which mode of transportation (road, water, air) and which carriers should be selected.
Constraints that need to be considered in the decision-making process include:
- The shipping mode of the product is restricted, and some products can only use specific shipping modes;
- On the link from factory to warehouse, the maximum total transport capacity of the transport mode is limited;
- In the link from factory to warehouse, there is a minimum shipping quantity restriction for a specific transportation mode;
- For specific products, there are restrictions on the ratio of factory-to-warehouse flow to total warehouse demand;
- Balance supply and demand while satisfying other constraints.
In response to this problem, the Distribution Resource Planning application of the Convect Flow Platform provides a solution. The application relies on a multi-period space-time network optimization solver to determine the transportation and allocation relationship between various sites in the supply chain when supply and demand are known.
In the supply chain network design decision-making process, the distribution resource plan is a good supplement to the site selection problem. After completing the long-term site layout planning, the medium-term route transportation planning can be decided by the distribution resource plan.
Through these cases, we can see that warehouse network planning is not only a link in supply chain management, but also a key factor in improving the overall operational efficiency of enterprises, reducing costs, and improving customer satisfaction. Scientific warehouse network planning combines data-driven algorithm analysis and experience judgment, which can provide enterprises with more accurate and efficient decision-making support in a complex and changing market environment.
It is worth noting that although warehouse network planning is a strategic decision, business personnel need to continuously optimize and adjust according to changes in operational strategies and market demand. This is not a one-time thing. Keeping up with the times is also the key to continuously optimizing the supply chain and improving corporate competitiveness.
