Global Supply Chain Network Optimization for a Medical Equipment Company

Background

The client was a multi-billion-dollar global medical equipment company, whose main growth comes from acquisitions, and was looking to consolidate and redefine their global logistics strategy. The initial focus was on North America and Europe where there were many different distribution strategies and shipping points due to the many recent acquisitions. The existing logistics network consisted of numerous manufacturing and warehouse locations across Europe and the US, shipping mostly parcel outbound to customers and a mix of parcel and freight between sites. Logistics costs were identified as one of the client’s biggest opportunities for cost savings. The goal was to establish shared DCs that would stock products from the different manufacturing sites and distribute them to customers that in many cases were overlapping. By restructuring the distribution network, the client could reduce warehousing and transportation costs while also enabling the following additional opportunities:

• Negotiate Improved Shipping Costs based on consolidated volumes

• Improved Business Case for Warehouse Automation through the economy of scale

• Improved Service to Customers, both shorter transit time and increased product availability

• Enable Growth – Organic Sales and Easier Acquisition Integration

• Sustainability Improvements

The Challenge

The first step was to determine the ideal number and location of the shared DCs in North America and Europe. To do this, a network optimization model was developed and analyzed along with all the qualitative factors. The client had some unique issues that posed challenges for the project.

Segregated Businesses: Some businesses and sites were integrated into the main ERP system, but many were still in separate systems. Every business also had different personnel and structures. This created challenges with data collection and merging. Many different parties needed to be aligned in order to bring the analyses and recommendations to life.

Product & Customer Service Requirements: Some products have expiration dates, specific temperature requirements and/or are considered hazardous or radioactive, meaning storage and shipping has special considerations. Many customers also expect products quick and fast, making 1- or 2-day delivery a key focus.

Difficulty in connecting sales transactions with shipments: There were hundreds of different freight agreements while the historical shipment data lacked details that would enable an understanding of what the charges were based on and what was included in each shipment. The sales data often lacked weights and tracking numbers that would enable the conversion to shipments.

The Approach

The initial focus was on the collection of data and information. To ensure that nothing was missed, meetings were set up with each business and site to understand the current state and learn about the similarities, differences, and challenges. It was also critical that all data was accurately captured.

Once all the data and information was gathered and cleansed, an in-depth analysis could be completed to understand the current state, identify improvement opportunities and determine the logic for building the network model. This was particularly important for this project to ensure the data could be manipulated properly to mimic reality.

Data was loaded into our proprietary network optimization tool to build the baseline model. This was built using a combination of inbound order data, outbound order data, transportation data, and manual logic so that the model could include item attributes and shipping costs could be applied correctly. In instances where the two data sets could not be tied together, the model was set up to fill in the blanks based on information that was gathered from data and interviews. One method was to use weight information that existed for the rest of the product group where possible or to identify typical shipments to main customers. The main goal for the baseline model was to validate the data, costs, and assumptions being used to build the model.

Now that the baseline model was built, the focus could shift towards finding the ideal solution. The initial step was a center of gravity analysis to determine the ideal distribution location(s) based volume and distance. This analysis was done separately for North America and Europe and analyzed for 1 to 6 locations in North America and 1 to 4 locations in Europe. The results of the center of gravity analysis, along with other quantitative and qualitative factors were analyzed to determine what alternative scenarios should be evaluated in the network optimization tool.

Scenarios were then evaluated and compared to the baseline and one another to determine the ideal strategy.

The Outcome

In North America, it was determined that a 2DC network with one location in the northeast and one on the west coast. By expanding the existing facility in the northeast, more products from Europe could be stocked in North America resulting in customers getting their product faster and cheaper. By opening a DC on the west coast, the large number of customers in the western US could then be serviced faster and cheaper than if they were to be serviced from the north east. The customers are primarily located on the coasts, making a central location less attractive than normal. The inbound/intercompany shipping costs did increase but this was offset with consolidated shipping. The new network was estimated to save 33% in total transportation costs per year and dramatically increase service levels to customers.. The warehouse and labor costs also increased with the addition of a new DC, but this was offset with closures of a few smaller sites and by locating the DCs in lower cost areas. This meant the transportation savings substantially outweighed the cost increases.  

In Europe, the optimal result was a DC in Brussels. There was also some transportation savings but the main factor in the business case was warehouse cost savings as many of the different businesses had their own 3PLs in the EU. By consolidating all distribution into 1 DC in Brussels, there was a total estimated savings of 32%.