A critical function of the Defense Logistics Agency (DLA) Troop Support (TS) Customer Pharmacy Operations Center (CPOC) is to guarantee the delivery and availability of pharmaceutical drugs to Warfighters, veterans, and their families. International DoD military bases rely on the DLA for excellence in customer support as well as their ability to obtain lifesaving drugs in emergency situations, especially when these bases cannot do so themselves. However, generic pharmaceutical drugs have a supply chain that includes global trade partners, and are also vulnerable to global disruptions, such as disasters including earthquakes, pandemics, and financial crises. Although the DLA TS CPOC excels at obtaining these drugs in urgent situations, it currently has a limited window on drug shortages, and often remediates drug supply chain issues on a case-by-case basis. Increasingly, generic drugs have Active Pharmaceutical Ingredients (APIs), which are ingredients in a pharmaceutical drug or pesticide that are biologically active. These APIs are made by just a few countries and knowing their origin is a matter of national security, particularly if the country of origin has political tensions with the United States or is experiencing a national disaster, causing the supply chain to be at risk. Currently, there are limited ways to assess risks around the drug supply chain, including country of origin, quality of ingredients, and events affecting the business transactions that make the sales of these drugs possible. The DLA TS CPOC seeks to better assess and predict drug shortages and drugs that are at risk, and they need a way to both acquire and process large sets of global business transactions to anticipate and mitigate drug supply chain risks long before they occur. Charles River Analytics proposes to design and demonstrate Probabilistic Reasoning on Supply Chain Readiness of International Pharmaceuticals using Trade Information (PRESCRIPTION), a set of services for obtaining the supply chain for pharmaceutical drugs, sourcing the country of origin for a drug's ingredients and API raw source materials, and assessing risk in the supply chain based on the country of origin and high-impact events. PRESCRIPTION will: (1) synthesize global trade data with a drug's ingredients and data on its raw source materials; (2) provide a probabilistic analysis framework to predict a material's country of origin and whether its supply chain is at risk based on patterns in the data; and (3) apply revolutionary visualization techniques to analyzed data to allow pharmacists to understand the supply chain network for a drug, its raw materials, and its manufacturers and suppliers to determine country of origin, drug quality, and vulnerabilities.
