Background:

Traditional “know your customer” due diligence has been at the foundation of pharma supply chain SOP for decades. All reputable pharmaceutical manufacturers and distributors have invested significant resources in developing best practices for maintaining the integrity of their order fulfillment process.

Monthly averaging and thresholds have traditionally provided account managers with the tools necessary to effectively identify orders with characteristics well outside the norm. As the size and complexity of the pharmaceutical industry grew, along with the demand for shorter order-to-cash sales cycles, account managers lacked the time and resources to thoroughly validate unusual orders prior to shipment.

Unfortunately, the criminal element was becoming increasingly bold and creative in their efforts to obtain drugs for illegal markets. In response, the DEA enacted a number of compliance and regulatory measures to increase pharma supply security and reduce illegal activity.

The combination of unabated illegal activity, increased legitimate market demand, and higher compliance and regulatory standards quickly undermined the relevance of traditional order monitoring techniques. The DEA demanded proactive interception of suspicious orders, not reports of suspicious sales after the fact.

Statistically Defensible Computational Analysis:

Those manufacturers and distributors of CS II-V with sufficient resources have responded to DEA mandates for upgrading suspicious order monitoring by developing and applying statistical analysis of every new order for controlled substances coming into their order management system.

Today’s DEA Compliance Blog Question:

Algorithms typically test each new order for significant deviation in order size, frequency and pattern. The tests are compared with the sales order history for a specific line item and specific ship to location. In this example the customer is being compared to itself.

The question is:  Are there benefits to comparing a new order not only to the buyer’s history for that specific item and destination, but also to the history of a group of peers within the supplier’s universe of buyers?

SomLink will support Peer Group Analysis for at least one early adopter. What is your assessment of this test?

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