It Takes More Than Math to Design a Distribution Network
New research demonstrates how the most efficient network designs also account for changing market conditions.
Distribution networks are the conduits that connect companies with their customers, so it is hardly surprising that the way these networks are designed has a critical impact on cost and customer service.
Companies commonly use mathematical optimization models to arrive at the best network design, but this approach is flawed in one key respect — it does not take into account changing market conditions during the several years it can take to complete a design project. This is particularly onerous in developing economies where markets tend to be extremely changeable.
Research carried out at the Malaysia Institute for Supply Chain Innovation (MISI) shows that supplementing mathematical models with analyses of external variables enables companies to develop the most efficient distribution networks. The research work was completed in collaboration with a leading Asian chemical manufacturer as part of a thesis project for the MIT-Malaysia Master of Science in Supply Chain Management.
Distribution network designs specify the locations of warehouses and how much product is allocated to each facility. A chemical company typically manufactures product in large plants to lower production costs by exploiting economies of scale. Product is shipped to numerous customer locations. The design of its distribution network, therefore, determines the total cost of delivering products to meet customer demand while maintaining the appropriate service levels.
There are many ways to configure a network to meet these goals. For example, a company can reduce its inventory holding cost by risk-pooling the inventory in a few warehouses. However, this option incurs higher transportation costs and longer lead times. Alternatively, a company could become extremely responsive to demand by stocking inventory in many warehouses. But such a strategy requires higher inventory volumes and hence higher carrying costs.
Mathematical models can be used to find the optimal solution, but this might not reflect real-world demands. External factors such as regional product demand, commercial real estate prices, and transportation costs can change markedly over the three- to five-year planning horizon that is common for these design projects.
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The MISI research project tackled this problem in four steps.
First, an optimization model was designed to minimize total costs including the costs associated with transporting product, opening and closing warehouses in different locations, fixed warehouse operations, and maintaining inventory.