Diageo

An Inventory Optimization Case Study

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Highlights
  • “Low hanging fruit” improvement in inventory done, needed smarter technology
  • Deployed inventory optimization bolted on to SAP ERP and Manugistics SCP
  • Achieved extremely high (99+%) service levels with significantly less inventory

Project and Objectives

Diageo North America is supported by a complex supply chain of eight manufacturing facilities, 5 contract manufacturers, 13 overseas import flows and 42 warehouses.

Vice President of Supply Chain Development Flavio De Simone was faced with conflicting goals. On one hand, the business required additional inventory to support volume growth, new SKUs, sales volatility from new products, and most important, the need for competitive service. On the other, the challenge was to keep working capital in line, given the cash demands of a growing business. The supply chain group had already reduced finished goods inventory by 18% in the past two years. He felt that the “low hanging fruit” in improving inventories was gone, so his group had to work figure out how to not just work harder, but work smarter through improved technology.

Day-to-Day

Diageo implemented the SO99+ solution from ToolsGroup. The inventory optimization solution bolts on to Diageo’s SAP ERP platform and their Manugistics supply chain planning (SCP) system. Forecast and order data is fed to the ToolsGroup system, where the demand is analysed and inventory modeled to create a high performance mix. Safety stocks and other inventory parameters are then fed back to Manugistics.

Diageo’s inventory planning and optimization has gone from a static annual review to a dynamic monthly process.

Also, instead of targeting SKUs individually, the system optimizes across the entire SKU portfolio to meet an aggregate service target. Service levels are then calculated for each item at each location.

Inventory Targeting "Makeover" Step Change in Sophistication & Accuracy

From

To

1. Annual static process

1. Monthly dynamic process

2. Data integrity issues between Planning system and Inventory Yargeting Model

2. Integrated Planning and Inventory Optimization

3. Monthly sales forecast error pro-portionately allocated to Replenishment Lead time

3. Dally/weekly/monthly sales volatility measures by SKU location

4. Variability over Replenishment Lead time only

4. Variability over Order Lead time and Replenishment Interval

5. Assume Normal Statistical Distribution of demand

5. Statistical Distribution of demand calculated for each item based on history

6. SKUs targeted individually

6. Mix optimization across SKU location portfolio to meet aggregate service target

7. SKUs calculated individually

7. Calculatig optimal service level for each item at each location

8. Warehouses targeted individually/no consideration for dependent demand

8. Overall optimization for dependent warehouses (multi-echelon)

9. No scenario modeling capabilities

9. Scenario modeling capabilities (strategically simulate service levels and inventory tradeoff

10. Rudimentary supply reliability input

10. Comprehensive production and trasportation reliability input

Results & Benefits

In less than 6 months, Diageo began meeting the project’s goals, including:

  • Service level excellence for their customers above 99%
  • New efficiencies to release working capital
  • Reduce organisational disruptions caused by Out-Of-Stocks
  • Less time spent justifying optimal stock levels

The US Spirit Bailment Warehouses Network, which includes 21 warehouses in 18 states, achieved a 99.6% case fill rate, with most recent results at 99.8%. Within 6 months, inventory turns had improved by 10%. Within 18 months, inventory turns had been improved by 30%.

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Diageo is the world’s leading premium drinks business with annual sales of $23 Billion. Its many well known brands include Smirnoff, Guinness, Johnnie Walker, Cuervo, Crown Royal, Tanqueray, J&B and Baileys.

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