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 customer service level and high logistics costs. Diageo sought assistance from the consulting companies Logistics Field Audit (business and supply chain management consulting) and Amalgama (simulation modeling consulting) when they experienced an increase in sales volume, but failed to realize larger profits due to logistics costs per unit. Other mounting concerns for Diageo were customer service level and cost of goods sold, combined with an increased inventory and future plans of development including a new warehouse in Russia and expansion to Urals and Siberia. The consultants were also assigned to manage Diageo’s big data with simulation-based decision support in order to show and prove ways to decrease logistics costs and choose a logistics configuration for the expanded client network.
The supply chain model includes three existing and one perspective factory, three border crossing points, three existing and five perspective warehouses, two customs offices and up to 300 demand points grouped into 45 service groups. In addition, the supply chain model contains a replenishment algorithm, order aggregation algorithm, load baring algorithms and delays at border crossing points. Demand and sales forecasting accuracy for all 280 Diageo products in 6 types of warehouses were also built into the model.
Logic built behind the simulation model included a replenishment algorithm concerning a segment of the supply chain that begins 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 takes into account a requirement diagram (planned sales diagram), current stock, lead time, and minimum order size, 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.
Model validation was imperative and began by comparing data from the previous year SAP ERP system data which assessment results gave less than 5% differential.
Initial value for Diageo included an increase in sales forecasting accuracy from 60%-80%, with a pay-off period of less than 2 years. This increase will allow Diageo to reduce their target stock level by 40% which will reduce logistic costs per unit by 7%, even with plans of sales growth. The research also denied the need for additional warehouse space, as the stock required to maintain the target service level was unreasonably high.
After running the simulation, the model provides the stock level forecast for each product (15 days ahead), a complete cost for each and every delivered product unit, and proves to the client what the target state of supply chain should be.
View Andrey A. Malykhanov’s entire presentation delivered at the AnyLogic Conference 2013: