Determining the Optimal Distribution Network in Europe for a Consumer Goods Giant

Background

The client is a well known consumer goods company with a European business that is growing very fast, both in their traditional Western European markets but specifically in the Eastern Europe. The specifications were clear, which increases the likelihood for success.

• The Company desires to deliver the promise of their global brands while driving down total landed costs and managing increasing complexity and shorter product life cycles. Reducing warehouse costs, reducing order cycle time, improving performance, and maximizing traceability were all particularly critical.

• The Company desires to establish the most efficient and cost effective way to support this growing export business including the possibility of consolidated singular orders encompassing all products offered, warehousing  (order receipt,  inventory storage, order picking and fulfilment) and transportation taking into consideration duties, customer service and costs. 

• The Company believes that delivering within 48 hours in Europe is critical to their business plans. In addition, being able to economically ship in less than full pallets to support launches into small but growing countries will be important to gaining distribution.

The Approach

The mapping of the current network was extra important since it provided some restrictions for the solutions in form of production capacity that couldn’t be moved and specific expertise in some value-added services.

In the data analytics, always a cornerstone in a network optimization, there were two main challenges.

1. The rapid growth in new markets means that there is not a lot of historical data to scale from. The solution was to focus on what the system needs to be able to handle, build a model type data set per market and use that as the planning factors. An added component in the solution search was the flexibility in volumes for markets with short history.

2. The historical data had gaps due to a history of acquisitions and new brands added. The approach here was to list the different assumptions and determine where an assumption would change the recommendation. Often times, the assumptions did not change the recommendation.

For the recommended locations, the following was analyzed:

• What comparable companies are located in the recommended area? This would provide a reality check in the form of “someone else thinks this is a great idea” but more that it would ensure that there is a workforce with experience in handling relevant products and customers.

• Which quality 3PL and carriers are located in the recommended area. This would make sure that there is a competitive landscape for RFPs and that their networks would support deliveries to the desired locations. It further emphasizes the relevant workforce availability.

The Outcome

Two locations would serve the main markets within the desired transit time and the logistics costs were significantly reduced. The existing location in France remained to support western Europe while the optimal second location was determined to be southwestern Poland, which could support Germany and central Europe, while also supporting the growth in Eastern Europe. The transportation cost is usually the main cost element, however, in this case, the added transportation cost from being slightly off the center of gravity was not significant enough to offset the savings in warehouse and labor costs plus access to new markets.