Logistics Network Optimization Using Simulation Modeling

Logistics Network Optimization Using Simulation Modeling


Diageo is a British multinational alcoholic beverages company. Diageo Russia is one of Russia’s top five wholesale alcoholic beverage distributors, which is traditionally a low margin business where the bottom line is sensitive to the level of customer service and the high costs of logistics.

Diageo sought assistance from both Logistics Field Audit (business and supply chain management consulting) and Amalgama (simulation modeling consulting) after they experienced an increase in sales volume but failed to realize larger profits due to the logistics costs per unit.

Other mounting concerns for Diageo were the level of customer service and the cost of goods sold, combined with an increased inventory and future plans for development.

These development plans required predictive analytics and logistics planning for a new warehouse in Russia, as well as for expansion into the Urals and Siberia.

Finally, the consultants were also assigned to harness Diageo’s big data with simulation-based decision support. This would show how to decrease logistics costs and implement the logistics network optimization needed for the expanded network.

AnyLogic enabled the logistics planning and optimization required to achieve these goals.

Logistics Network Optimization

Logistics Optimization Model


The simulation model used for the logistics optimization includes three existing and one prospective factory, three border crossing points, three existing and five prospective warehouses, two customs offices, and up to 300 demand points grouped into 45 service groups. In addition, the logistics optimization model contains a replenishment algorithm, an order aggregation algorithm, load bearing algorithms, and delays at border crossing points. Demand and sales forecasting for all 280 Diageo products in 6 types of warehouses were also built into the model.

The logic behind the logistics optimization model included a replenishment algorithm concerning a segment of the logistics network, beginnings at the consolidation warehouse, with five days of lead time to the central distribution center and five days of lead time to the original distribution center — due to Russia’s large size and relatively slow transportation capacities. The replenishment algorithm considers a requirement diagram (planned sales diagram), current stock, lead time, and minimum order size, and then generates the requirements for replenishment, identifies the coverage gaps, (time periods when stock will be lower than the lowest threshold), and takes action to prevent any gaps in coverage. The capability to observe the model behavior dynamically was extremely useful in this case and a key benefit of using AnyLogic.

Validating the logistics optimization model was imperative and began by comparing data SAP ERP system data from the previous year. The results showed the differential was less than 5%.

Logistics Network Optimization Model Results

Logistics Network Optimization Model Results


After running the simulation, the logistics optimization model provides a stock level forecast for each product (15 days ahead), a complete cost for each and every delivered product unit, and clearly indicates what the target state of supply chain should be.

The initial value for Diageo included an increase in sales forecasting accuracy from 60% to 80%, giving a pay-off period of less than 2 years. This increase allowed Diageo to reduce their target stock level by 40% and reduce the logistics costs per unit by 7%, even with the sales growth plans. The research also saved the need for additional warehouse space after stocking and service levels were balanced.

View Andrey A. Malykhanov’s entire presentation about logistics simulation and optimization using AnyLogic software delivered at the AnyLogic Conference 2013:

Logistics simulation and optimization using AnyLogic software

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