Authors:
Monica L. Nogueira
and
Noel P. Greis
Affiliation:
The University of North Carolina at Chapel Hill, United States
Keyword(s):
Answer Set Programming, Track-and-Trace, Supply Chain, Food Recall Process.
Related
Ontology
Subjects/Areas/Topics:
AI Programming
;
Applications and Case-studies
;
Artificial Intelligence
;
Domain Analysis and Modeling
;
Knowledge Engineering and Ontology Development
;
Knowledge Representation
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
Incomplete information and the inability to trace the movement of contaminated products across the food chain has hindered our ability to locate and remove contaminated products once a food recall has been announced. The FDA Food Safety Modernization Act (FSMA) that was signed into law in 2011, however, supports traceability by both expanding the registration requirements for companies that are involved in food production and, in the event of a food recall, requiring companies to provide information about their immediate suppliers and customers—what is referred to as “one step forward” and “one step backward” traceability. In this paper we implement the logic-based approach called answer set programming that uses inference rules to determine the set of all companies that may be linked to a contaminated product. Unlike other approaches, we do not depend on the availability of common standards or unique identifiers. Rather, the proposed approach utilizes information about the company’s
primary suppliers and customers along with their products—consistent with the “one step forward” and “one step backward” required under FMSA as noted above. We demonstrate this approach using the example of a food recall involving pork products.
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