Development of a Predictive Tool for Assessing Vulnerability to Economically Motivated Adulteration

Introduction: Economically motivated adulteration (EMA) is an established threat to grocery manufacturers. In today’s globally distributed and dynamic food supply network there are inherent risks to the integrity of the supply chain. The impact of EMA can range from minor (e.g., no public health impact and minor economic damage to an organization) to catastrophic (e.g., loss of life and loss of economic viability of an entire organization). Current methods of assessing potential vulnerabilities to EMA are reactive in nature and are unable to help prioritize decisions across the wide array of commodities many food safety and defense professionals must manage on a day-to-day basis.

Purpose: The purpose of this presentation is to demonstrate a predictive, software-based tool for assessing the vulnerability of commodities to EMA based on a combination of characteristic attributes and subject matter expert (SME) opinion.

Methods: A cloud-based software tool has been created that estimates the vulnerability of commodities to EMA by combining automated data mining of commodity characteristics (value, volume, scarcity, etc.), a multi-attribute utility model algorithm, and SME opinion. 

Results: The development of the foundation for assessing EMA vulnerability will be discussed, including: (1) the selection, quantification, and mining of characteristic attributes will be discussed; (2) the incorporation of mathematical algorithms based on utility modeling to capture EMA perpetrator hesitancies; and (3) SME opinion regarding the relative importance of commodity characteristics in driving EMA perpetrator behavior.  The resulting tool provides critical insight on EMA vulnerability that provides a defensible, consistent assessment able to inform decisions.  Retrospective case studies that demonstrate application of the EMA Vulnerability Assessment Tool will also be presented and discussed. 

Significance: This presentation provides a clear example of how a software-based decision aid can improve the efficiency and consistency of evaluating EMA vulnerability.