Detecting counterfeit products by means of frequent pattern mining

Product traceability is one of the major issues in supply chains management (e.g., Food, cosmetics, pharmaceutical, etc.).
Several studies has shown that traceability allows targeted product recalls representing a health risk (e.g.: counterfeit products), thus enhancing the communication and risks management. It can be defned as the ability to track and trace individual
items throughout their whole lifecycle from manufacturing to recycling. This includes real-time data analytics about actual
product behavior (ability to track) and product historical data (ability to trace). This paper presents a comparative study
between several works on product traceability and proposes a standardized traceability system architecture. In order to
implement a counterfeit/nonconforming product detection algorithm, we implement a cosmetic supply chain as a multi-agent
system implemented in Anylogic©. Data generated by this simulator are then used in order to identify genuine trajectories
across the whole SC. The genuine product trajectories (behavior) are inferred using a frequent pattern mining algorithm
(i.e., Apriori). This identifed trajectories are used as a reference in order to identify counterfeit products and detect false
alarms of product behavior