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RDCs and Mathematical Optimization Transform a Global Health Supply Chain

ASCM Insights

Pioneering Supply Chain Integrity

RDCs and Mathematical Optimization Transform a Global Health Supply Chain

By Hua Ni

In supply chain, companies often leverage a regional distribution center (RDC) model to balance performance and cost. For the global health supply chain operated by Chemonics International and a consortium of partners, including IBM, RDCs play a critical role in perpetuating a healthy and robust network. They serve as a demand and supply buffer to meet variation more effectively with limited manufacturing capability, support the pre-positioning of products closer to customers for faster responses, provide an economy of scale to meet storage needs, enable logistics consolidation and serve as holding points for more efficient logistics operations, and provide better access to logistic resources.

When Chemonics and our partners began implementing the USAID Global Health Supply Chain Program - Procurement and Supply Management (GHSC-PSM) in 2015, the health of millions of patients was put in our hands. We were tasked with procuring and delivering an uninterrupted supply of lifesaving health commodities to nearly 60 countries around the world. Along with this responsibility came the opportunity to optimize the GHSC-PSM global supply chain network. 

From the very beginning, our clients recognized the opportunities for efficiency gains and cost reductions with a consolidated global supply chain network that included a streamlined RDC strategy. In 2016, while working closely with USAID in a rigorous network design optimization effort, we proposed a new RDC network in Belgium, the United Arab Emirates and South Africa, with anticipated annual warehousing and logistics cost savings of $38 million over six years.

We followed a five-step process to implement the new RDC strategy: 

  1. Product selection: Although RDCs provide many benefits, they are not suitable for all products. Some countries have stringent shelf-life requirements, at a 75-80% range when delivered. For those goods with a two-year shelf life, this translates to a maximum of two to three months of storage dwell. We analyzed historical data to identify high-demand, high-value products for the RDCs, as well as those with long production lead times. These goods include those that were previously delivered through RDCs, and some new ones, to assess the viability of the RDC network.
  2. Demand modeling: Unlike orders in many commercial settings, the orders we process for GHSC-PSM have lead times ranging from a few months to more than a year prior to the requested delivery date. This affects the transportation mode decision and whether the order should be fulfilled through an RDC or direct drop. Therefore, when we conducted demand modeling, we used clustering analysis on the historical order lead times by country and product to derive more accurate demand profiles over time. We also leveraged research and insights from the public health community to model future demand growth factors or disease burdens, which inform demand scenarios for our analyses.
  3. Location candidate screening: We started with a greenfield approach, considering all countries as possible candidates, and asked ourselves where the future RDCs could be. Leveraging historical supply and demand data, we developed a gravity model to come up with locations that could work well as consolidation points. We then analyzed various factors, such as infrastructure availability, access to transportation, stabilities and risk, and settled on a list of 12 countries.
  4. Transportation and warehouse cost modeling: After the short-listed locations were identified, we used a combination of market intelligence and information collected through warehouse and transportation requests for quote to more accurately model transportation and warehouse costs.
  5. Mathematical modeling and scenario analysis: The last, and most involved, step required building a mixed-integer network design optimization model that, for each given demand scenario, optimized the decisions of where to set up RDCs, which products should be stored in each RDC, how to fulfill demand (through RDC or direct drop), and which modes of transportation would work best. Manufacturing lead time, transportation lead time and shelf-life requirements were used to assess the likelihood of on-time delivery. The model minimizes warehouse and transportation costs after meeting a set performance target. Based on the large number of scenarios, we identified robust network design options and worked with USAID to factor in non-quantitative factors to come up with the final design recommendation.

GHSC-PSM’s RDC network became operational in April 2018. Based on the data collected to date, it is achieving close to 90 percent on-time delivery performance for the RDC-fulfilled orders and has realized $2.9 million in warehouse and freight cost savings in the first six months of operation. Once the new warehouse in South Africa is active later this year, GHSC-PSM projects an $8 million annual logistics savings, assuming similar levels of activity.

Read more about the Chemonics and IBM global health supply chain initiative in “Operating a Global Health Supply Chain in Low-Resource Settings.”

Hua Ni is the supply chain optimization lead analyst for GHSC-PSM and an associate partner of IBM GBS Public Service. For the last 14 years, he has been actively applying operations research and analytics in operations, logistics and supply chain. Ni may be contacted at hni@ghsc-psm.org.

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